What is the CVD Risk Toolkit?

The CVD Risk Toolkit is a comprehensive cardiovascular disease risk assessment tool developed by a physician. It incorporates multiple validated algorithms including Framingham Risk Score (D'Agostino 2008 and Wilson 1998), QRISK3, QRISK4, ACC/AHA PREVENT equations, SCORE2, and Reynolds Risk Score. A key differentiator is its specialized Lipoprotein(a) risk calculation and cascade screening tools, addressing a critical gap in standard cardiovascular risk assessment. The toolkit is designed for healthcare professionals to support clinical decision-making with 99.9%+ calculation accuracy.

What is Lipoprotein(a) and why is it important for heart disease risk?

Lipoprotein(a), or Lp(a), is a genetically determined lipoprotein particle that is an independent, causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease. Unlike LDL cholesterol, Lp(a) levels are approximately 90% determined by genetics and remain stable throughout life. Elevated Lp(a) levels (above 50 mg/dL or 125 nmol/L) are associated with 2-3x increased risk of heart attack, stroke, and aortic valve disease. The CVD Risk Toolkit includes specialized Lp(a) risk modification calculators that adjust baseline cardiovascular risk scores and provides cascade screening recommendations for family members.

How accurate is this cardiovascular risk calculator?

The CVD Risk Toolkit implements published, peer-reviewed cardiovascular risk algorithms with 99.9%+ calculation accuracy verified against gold standard implementations. All calculations cite original research sources with DOI links. The toolkit includes 900+ automated validation tests to ensure accuracy. However, all clinical decisions should involve qualified healthcare professionals who can interpret results in the context of individual patient factors.

What is the difference between QRISK3 and QRISK4?

QRISK3 and QRISK4 are both UK-validated cardiovascular risk prediction algorithms. QRISK3 (2017) includes factors like ethnicity, chronic kidney disease, atrial fibrillation, migraine, rheumatoid arthritis, systemic lupus erythematosus, severe mental illness, and steroid use. QRISK4 (2024) adds 9 new risk conditions: brain, lung, oral, and blood cancers, COPD, Down syndrome, learning disability, and for women, pre-eclampsia and postnatal depression. Note: BP variability (SBP-SD) was already in QRISK3 since 2017. The CVD Risk Toolkit calculates both and provides side-by-side comparison with factor breakdown analysis.

What is the PREVENT equation and how is it different from Framingham?

The PREVENT (Predicting Risk of cardiovascular disease EVENTs) equations are the newest ACC/AHA cardiovascular risk algorithms published in 2024. Unlike older Framingham equations, PREVENT was developed using contemporary, diverse populations and provides both 10-year and 30-year risk estimates. It removes race as a variable, includes eGFR for kidney function, and can incorporate additional factors like urine albumin-to-creatinine ratio (UACR), HbA1c, and social deprivation index. The CVD Risk Toolkit implements both base and enhanced PREVENT models.

Who should have their Lp(a) tested?

Current guidelines recommend measuring Lp(a) at least once in every adult's lifetime to assess inherited risk. Priority testing is recommended for: individuals with personal or family history of premature ASCVD (men under 55, women under 65), family history of elevated Lp(a), familial hypercholesterolemia (FH), recurrent cardiovascular events despite optimal therapy, LDL-C that does not respond adequately to statins, or calcific aortic valve disease. Since Lp(a) is 90% genetic, first-degree relatives of those with elevated Lp(a) should undergo cascade screening.

What is cascade screening for Lipoprotein(a)?

Cascade screening is a systematic approach to identifying family members at risk by testing first-degree relatives (parents, siblings, children) of an individual with elevated Lp(a). Since Lp(a) levels are approximately 90% genetically determined, if one person has elevated Lp(a), there is about a 50% chance that each first-degree relative also has elevated levels. The CVD Risk Toolkit includes a Cascade Screening Calculator that estimates the number of family members likely to be affected and calculates the potential number of cardiovascular events that could be prevented through early identification and treatment intensification.

How do I convert Lp(a) from mg/dL to nmol/L?

Lp(a) unit conversion is complex because it depends on the assay used and the patient's Lp(a) isoform size. A commonly used conversion factor is 2.5 (multiply mg/dL by 2.5 to get nmol/L), but this can range from 2.0 to 3.0 depending on the laboratory method. The CVD Risk Toolkit provides unit conversion with clear warnings about this assay-dependent variability. Generally, thresholds for 'elevated' are considered to be 50 mg/dL or 125 nmol/L, and 'very high' at 80 mg/dL or 180 nmol/L. Always verify conversions with your laboratory's specific guidelines.

What is PCSK9 inhibitor eligibility and who qualifies?

PCSK9 inhibitors (evolocumab, alirocumab) are powerful cholesterol-lowering medications typically reserved for high-risk patients. Based on CCS 2021 guidelines, there are three main eligibility pathways: (1) Heterozygous Familial Hypercholesterolemia (HeFH) with LDL-C above target despite maximally tolerated statin plus ezetimibe, (2) Statin Intolerance with documented inability to tolerate at least two statins at lowest doses, and (3) Very High Risk cardiovascular disease with recurrent events or progressive disease despite optimal therapy. The CVD Risk Toolkit includes a comprehensive PCSK9i Eligibility Assessment that evaluates all three pathways.

What is the Body Roundness Index (BRI) and why is it included?

The Body Roundness Index (BRI) is a newer anthropometric measure that estimates body fat distribution and has been shown to predict cardiovascular risk better than BMI alone. BRI is calculated using height and waist circumference and correlates well with visceral adipose tissue. A BRI of 1-2 is considered very lean, 2-4 is average, 4-5 is overweight range, and above 5 indicates obesity. The CVD Risk Toolkit includes BRI calculation highlighted in teal in results tables as an additional metabolic risk indicator alongside traditional BMI.

What is the FIB-4 score and why is it relevant to cardiovascular risk?

FIB-4 (Fibrosis-4) is a non-invasive score that estimates liver fibrosis using age, AST, ALT, and platelet count. It's relevant to cardiovascular risk because non-alcoholic fatty liver disease (NAFLD) and liver fibrosis are associated with increased cardiovascular mortality. FIB-4 below 1.3 suggests low risk of advanced fibrosis, 1.3-2.67 is indeterminate, and above 2.67 suggests advanced fibrosis requiring further evaluation. The CVD Risk Toolkit calculates FIB-4 automatically from liver panel inputs and highlights abnormal values.

Can I use this calculator offline?

Yes, the CVD Risk Toolkit is a Progressive Web App (PWA) that works fully offline after initial loading. You can install it on your device (desktop or mobile) for instant access without internet connection. All calculations are performed locally in your browser. Data is stored securely in your browser's IndexedDB with AES-256-GCM encryption and never transmitted to external servers, ensuring HIPAA-compliant data handling suitable for clinical environments.

What clinical guidelines does the toolkit follow?

The CVD Risk Toolkit integrates multiple international clinical guidelines: CCS 2021 Canadian Cardiovascular Society Dyslipidemia Guidelines for LDL targets and PCSK9i eligibility, AHA/ACC 2018-2024 Guidelines for cholesterol management and PREVENT equations, ESC 2019-2021 European Society of Cardiology Guidelines for SCORE2 and lipid management, NICE Guidelines for QRISK implementation, and consensus statements from the National Lipid Association (NLA) and European Atherosclerosis Society (EAS) on Lp(a). All guidelines are referenced with DOI links.

How can I export my patient data?

The CVD Risk Toolkit supports multiple export formats: CSV export with 30+ columns including all risk scores and clinical data, JSON export for full assessment history and data portability, PDF report generation for patient records, FHIR R4/R5 compliant export for healthcare system integration, and HL7 v3 CDA export for clinical document exchange. All exports can be triggered from the Import/Export tab and include calculation timestamps and Patient ID linkage.

Why was this toolkit created?

The CVD Risk Toolkit was developed by a physician motivated by personal family history of elevated Lipoprotein(a). Recognizing that Lp(a) is significantly under-tested and poorly integrated into standard cardiovascular risk assessment despite being a causal, genetic risk factor affecting 20% of the population, this toolkit was created to: standardize when to test Lp(a), provide clear interpretation of results in clinical context, recommend evidence-based next steps including cascade family screening, and support research by building analyzable data for real-world outcomes studies. The goal is to prevent cardiovascular events through improved risk identification.

CVD Risk Toolkit v30.74.334 - Medical Grade Cardiovascular Risk Assessment

👤 Patient Demographics & Basic Information

Enter patient data for cardiovascular risk assessment

Patient ID: Not Generated

📋 Data:

📋 Basic Information

Patient Name (Optional)

Date of Birth Auto-calculates age

Lab Collection Date Date labs were drawn

Age (years) Framingham: 30-79 | QRISK: 25-84

Biological Sex

Ethnicity / Race Used for QRISK3/4 and ASCVD PCE. *ASCVD not validated for this group.

🚬 Smoking Status Syncs to Framingham & QRISK

📏 Anthropometric Measurements

Height

Weight

Waist Circumference

Measure at umbilicus level

Hip Circumference

⚠️ Required for BRI calculation

BMI

kg/m²

BRI (Body Roundness)
index

Waist/Hip Ratio

ratio

BSA

m²

IBW

❓ Why BRI over BMI?

Body Roundness Index (BRI) predicts central adiposity better than BMI. Studies show BRI correlates more strongly with cardiometabolic risk, visceral fat, and mortality. Thomas DM et al., Obesity 2013;21:E338-42.

Formula: BRI = 364.2 - 365.5 × √(1 - ((WC / 2π)² / (0.5 × height)²))

📊 BRI Interpretation Ranges:

< 3.41	Lean body composition
3.41 - 4.45	Normal/Healthy range
4.45 - 5.46	Elevated (overweight equivalent)
5.46 - 6.91	High (obese equivalent)
> 6.91	Very High (severe obesity)

Note: BRI > 4.45 associated with increased cardiometabolic risk. Rico-Martin S et al. PLoS One 2020;15:e0233629

🌍 BMI Ranges by Ethnicity & Race

Standard WHO BMI cutoffs (overweight ≥25, obese ≥30 kg/m²) were derived from predominantly White European populations. Research shows that metabolic and cardiovascular risk occurs at significantly lower BMI values in South Asian, East Asian, and other ethnic groups due to differences in body fat distribution, visceral adiposity, and genetic predisposition.

🧬 Why Do BMI Ranges Differ? — An Evolutionary Biology Perspective

Human populations evolved under vastly different environmental pressures over tens of thousands of years, shaping distinct patterns of how and where the body stores fat:

South & Southeast Asian populations evolved in tropical monsoon climates with cyclical feast-and-famine pressures. The thrifty phenotype hypothesis (Hales & Barker 1992) and its variants suggest that metabolic programming favoured efficient visceral fat storage — fat packed around organs rather than under the skin. This is why South Asian newborns already show greater central adipose deposition and subscapular skin-fold thickness than European newborns, even when born in the UK. At any given BMI, South Asians carry more visceral fat and less limb fat, making BMI a poor proxy for their true metabolic risk.
East Asian populations show a similar but less pronounced pattern. GWAS have identified developmental genes (NID2, HECTD4, GNAS) that regulate fat depot allocation differently in Asian vs European populations. For each 1-unit increase in BMI, Asians show a 15% increase in diabetes odds vs 11% in Europeans — the same BMI number carries fundamentally different metabolic meaning.
African-descent populations tend to store fat preferentially in subcutaneous depots (under the skin) rather than viscerally (around organs). Since visceral fat is the metabolically dangerous depot driving insulin resistance and inflammation, African-descent individuals are relatively protected from metabolic disease at higher BMI values. This is why their equivalent-risk BMI threshold (~28) is closer to the European standard.
Pacific Islander & Polynesian populations may carry “nutritionally thrifty” genes from ancestral Malay lineages, shaped by the feast-and-famine cycles of island colonization. These populations show some of the highest obesity rates globally when exposed to modern Western diets.

The core insight is that BMI measures total mass, not fat distribution. Two people at BMI 25 can have radically different visceral-to-subcutaneous fat ratios depending on ancestry, making one metabolically healthy and the other insulin-resistant. Over 460 GWAS loci now linked to fat distribution confirm that these differences are genetically encoded, not lifestyle artifacts. This is precisely why BRI and waist circumference are superior to BMI for assessing cardiometabolic risk across all populations.

Sources: Wells JCK, Philos Trans R Soc B 2023;378:20220224 • Sun C et al. Genes 2021;12:841 • Goh LGH et al. Endocrinol Metab 2020;35:681-695 • Yaghootkar H et al. J Intern Med 2020;288:271-283 • Neel JV, Am J Hum Genet 1962;14:353-362

Ethnicity	Normal	Overweight	Obese	T2D-Equivalent*
White/European	18.5–24.9	25.0–29.9	≥30.0	30.0 (ref)
South Asian ⚠️	18.5–22.9	23.0–24.9	≥25.0	23.9
East Asian (Chinese/Japanese)	18.5–22.9	23.0–27.4	≥27.5	26.9
Southeast Asian (Filipino/Korean/Vietnamese)	18.5–22.9	23.0–26.9	≥27.0	25–27
Arab/Middle Eastern	18.5–24.9	25.0–26.5	≥26.6	26.6
Black/African Descent	18.5–24.9	25.0–29.9	≥30.0	28.1
Hispanic/Latino	18.5–24.9	25.0–29.9	≥30.0	≈30.0

* T2D-Equivalent = BMI at which type 2 diabetes incidence matches White populations at BMI 30.0 (Caleyachetty et al. Lancet Diabetes Endocrinol 2021; Wang et al. Obesity 2024)

⚠️ South Asian Alert: South Asian populations develop T2D at BMI as low as 23.9 kg/m² — equivalent risk to BMI 30 in White Europeans. The ADA (2015) and USPSTF recommend diabetes screening at BMI ≥23 for Asian Americans. Filipino Americans show excess visceral fat even below BMI 23 on CT imaging.

📊 Key Finding: Asian Americans are not monolithic. Korean and South Asian Canadians had T2D incidence of 20.3 and 20.8 per 1,000 person-years respectively, vs 6.3–9.5 in Chinese and White Canadians. BMI cutoffs vary from 22.8 (Japanese) to 25 (India) to 28 (China).

✅ Why BRI Matters Here: BRI outperforms BMI across all ethnic groups for predicting cardiovascular mortality (Zhang et al. JAMA Netw Open 2024). BRI captures central adiposity regardless of ethnicity-specific BMI thresholds, making it particularly valuable in diverse populations.

📚 References (click to expand)

Caleyachetty R, et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 diabetes risk in England: a population-based cohort study. Lancet Diabetes Endocrinol 2021;9:419–426. DOI
Wang S, et al. Comparison of race- and ethnicity-specific BMI cutoffs for categorizing obesity severity: a multicountry prospective cohort study. Obesity 2024;32(10):1958–1966. DOI
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157–163. DOI
Hsu WC, et al. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care 2015;38:150–158. DOI
Araneta MR, et al. Optimum BMI cut points to screen Asian Americans for type 2 diabetes. Diabetes Care 2015;38:814–820. DOI
Hsu WC, et al. Body mass index thresholds for Asians: a race correction in need of correction? Ann Intern Med 2024;177(8):1119–1120. DOI
Batsis JA, et al. Race, ethnicity, sex, and obesity: is it time to personalize the scale? Mayo Clin Proc 2019;94(2):362–363. DOI
USPSTF. Screening for prediabetes and type 2 diabetes. JAMA 2021;326(8):736–743. DOI
ADA Standards of Care in Diabetes — 2024. Diabetes Care 2024;47(Suppl 1):S1–S321. DOI
Shahmohamadi E, et al. Ethnic differences in BMI cut-off values associated with cardiovascular risks in South Asians (AHA 2024 Abstract). Circulation 2024;150(Suppl_1):4145936.

🩺 Blood Pressure

📊 BP Readings (6+ readings for QRISK3 variability)

Most recent syncs to calculators

#	Date	SBP	DBP	Actions
1

Current SBP (mmHg) Or enter single reading directly

Current DBP (mmHg)

Heart Rate (bpm)

BP Treatment Status

⚠️ Risk Factors

💊 Medications & Laboratory Values

Enter current medications and lab results

🧪 Lipid Panel v

Total Cholesterol

LDL Cholesterol

HDL Cholesterol

Triglycerides

🧬 Lipoprotein(a)

⚠️ Elevated ≥50 mg/dL or ≥125 nmol/L

🔬 Apolipoprotein B ℹ️

Target: <90 mg/dL (high risk: <65)

Non-HDL

mg/dL

TC/HDL Ratio

LDL (Friedewald)

TC - HDL - TG/5

LDL (Martin/Hopkins)

More accurate for TG <400

TG/HDL Ratio

IR proxy: <2 ideal

Remnant Chol

mg/dL

TC - HDL - LDL

Lp(a)-Corrected LDL
mg/dL
LDL - Lp(a)×0.3

📊 LDL Calculation Methods:

Friedewald (1972): LDL = TC - HDL - TG/5 (mg/dL). Invalid if TG >400 mg/dL
Lerodalcibep (PCSK9i, 2025): Raal FJ et al. Lerodalcibep in heterozygous FH (LIBerate trials). Lancet Diabetes Endocrinol 2025. DOI: 10.1016/S2213-8587(24)00313-9
Martin/Hopkins (2013): Uses adjustable factor based on TG and non-HDL levels. More accurate for TG 100-400 mg/dL. Reference: JAMA 2013

🔬 Biomarker Quick Reference:

Lp(a): Genetically determined (~90%), independent CVD risk factor. 1 in 5 people have elevated levels. Not lowered by statins. Screen once in lifetime + cascade family screening if elevated.

ApoB-100: One molecule per atherogenic particle (LDL, VLDL, Lp(a)). Better predictor than LDL-C, especially with high TG or metabolic syndrome. Reflects total atherogenic particle count.

⚡ Quick Calculations

🔬 Advanced Lipid Markers (sdLDL, VLDL, Remnant, ApoA1, Lp-PLA2) v

🔬 About Advanced Lipids: These markers provide additional CVD risk stratification beyond standard lipid panels.

Small Dense LDL (sdLDL)

Desirable: <30 mg/dL | High risk: >50 mg/dL

VLDL Cholesterol

Normal: 5-40 mg/dL | Typically TG/5

Remnant Cholesterol

Desirable: <24 mg/dL | High: >30 mg/dL

Apolipoprotein A-I ℹ️

Low risk: >120 mg/dL

Lp-PLA2 (PLAC Test) ℹ️

Low risk: <200 ng/mL | Plaque inflammation marker

ApoB/ApoA1 Ratio

Target: <0.9

📊 Biomarker Interpretation Guide

ApoB/ApoA1 Ratio

ApoB represents atherogenic particles (LDL, VLDL, IDL, Lp(a)) while ApoA1 represents protective HDL particles.

<0.6 Low Risk 0.6-0.9 Moderate >0.9 High Risk

Ref: ESC/EAS 2019 Dyslipidemia Guidelines

Lp-PLA2 (PLAC Test)

Lipoprotein-associated phospholipase A2 is a vascular-specific inflammatory enzyme associated with vulnerable atherosclerotic plaques.

<200 Low Inflam. 200-235 Borderline >235 High Risk

Ref: AACE 2017 Guidelines | Units: ng/mL

🫁 Liver Panel, FIB-4 & NAFLD v

AST

ALT

Platelets

Required for FIB-4 & NFS

Albumin

Required for NAFLD-FS

GGT

Optional

ALP

Optional

Diabetes/IFG Status For NAFLD Fibrosis Score

FIB-4 Score

Liver fibrosis

AST/ALT Ratio

NAFLD Fibrosis Score

Advanced fibrosis risk

APRI Score

Fibrosis/cirrhosis

MELD-Na

Liver disease severity

MELD 3.0 2025

Sex-adjusted severity

Liver Fibrosis Score Interpretation

Score	Low Risk	Indeterminate	High Risk
FIB-4	<1.30	1.30-2.67	>2.67
NAFLD-FS	<-1.455	-1.455 to 0.676	>0.676
APRI	<0.5	0.5-1.5	>1.5

References: FIB-4 (Sterling 2006), NAFLD-FS (Angulo 2007), APRI (Wai 2003)

⚡ Quick Liver Calculations

🔬 Enhanced Liver Fibrosis (ELF) Score v

About ELF Score: Uses direct serum markers of liver fibrosis (HA, PIIINP, TIMP-1) for accurate staging. More accurate than indirect scores but requires specialized testing.

Hyaluronic Acid (HA)

ng/mL

Normal: <75 ng/mL

PIIINP (Procollagen III N-terminal Peptide)

ng/mL

Normal: 3-15 ng/mL

TIMP-1 (Tissue Inhibitor of Metalloproteinase-1)

ng/mL

Normal: 150-250 ng/mL

ELF Score

Direct fibrosis markers

ELF Interpretation:
• <7.7: No/mild fibrosis (F0-F1)
• 7.7-9.8: Moderate fibrosis (F2-F3)
• ≥9.8: Advanced fibrosis/cirrhosis (F3-F4)

🔗 CKD-FIB4 Cardiometabolic Interaction v

About CKD-FIB4 Interaction: Combined kidney and liver assessment for cardiometabolic risk. Patients with both CKD and elevated FIB-4 have significantly higher cardiovascular and all-cause mortality than either condition alone.

eGFR Status

CKD Stage

FIB-4 Status

Fibrosis Risk

Combined Cardiometabolic Risk

Liver-Kidney interaction

Risk Stratification:
• Low: eGFR ≥60 AND FIB-4 <1.30 (HR 1.0 reference)
• Moderate: eGFR <60 OR FIB-4 ≥1.30 (HR ~1.5-2.0)
• High: eGFR <60 AND FIB-4 ≥1.30 (HR ~2.5-3.5)
• Very High: eGFR <45 AND FIB-4 ≥2.67 (HR ~4.0+)

🔬 Extended Liver (Bilirubin, LDH, Protein Studies) v

Total Bilirubin

Normal: 0.1-1.2 mg/dL | 2-21 µmol/L

Direct Bilirubin

Normal: 0.0-0.3 mg/dL

Indirect Bilirubin (Calc)

mg/dL

= Total - Direct | Normal: 0.1-0.9 mg/dL

LDH

Normal: 140-280 U/L

Total Protein

Normal: 6.0-8.3 g/dL

Globulin

Normal: 2.0-3.5 g/dL

A/G Ratio ℹ️

Normal: 1.1-2.5

📋 A/G Ratio (Albumin/Globulin)

Measures the ratio of albumin to globulins in blood. Abnormal values may indicate liver disease, kidney disease, immune disorders, or certain malignancies.

<1.0 Low - Liver disease, autoimmune, chronic inflammation 1.1-2.5 Normal >2.5 High - Immunoglobulin underproduction

🩸 Coagulation Panel (PT, INR, PTT, D-Dimer, Fibrinogen) v

PT (Prothrombin Time)

seconds

Normal: 11-13.5 seconds

INR Normal: 0.9-1.1 | Therapeutic (warfarin): 2.0-3.0

aPTT

seconds

Normal: 25-35 seconds

Fibrinogen

Normal: 200-400 mg/dL | 2-4 g/L

D-Dimer

Normal: <0.5 µg/mL FEU | Age-adjusted: Age × 0.01 if >50

🩸 Coagulation Reference

Test	Normal Range	Clinical Significance
PT/INR	11-13.5s / 0.9-1.1	Extrinsic pathway, warfarin monitoring
aPTT	25-35 seconds	Intrinsic pathway, heparin monitoring
Fibrinogen	200-400 mg/dL	CVD risk marker, acute phase reactant
D-Dimer	<0.5 µg/mL	VTE/PE exclusion, DIC monitoring

🫘 Kidney Panel & eGFR v

Creatinine

UACR (Urine Albumin-to-Creatinine Ratio)

A1: <30 | A2: 30-300 | A3: >300 mg/g

Urine Creatinine

Cystatin C

mg/L

Normal: 0.6-1.0 mg/L | Enables CKD-EPI cystatin C equations

eGFR Method

eGFR (Direct Input)

mL/min/1.73m²

Use if eGFR is known from lab report (overrides calculation)

BUN / Urea

Normal: 7-20 mg/dL (2.5-7.1 mmol/L)

Uric Acid

Normal: M 3.4-7.0, F 2.4-6.0 mg/dL | Gout risk >7.0 mg/dL

⚡ Electrolytes

Sodium

mmol/L

Normal: 135-145

Potassium

mmol/L

Normal: 3.5-5.0

Phosphate

Normal: 2.5-4.5 mg/dL

Magnesium

Normal: 1.7-2.2 mg/dL

🧪 Calcium & Ionized Calcium

Calcium (Total)

Normal: 8.5-10.5 mg/dL

Albumin (for correction)

g/dL

Normal: 3.5-5.0 g/dL

Ionized Ca (calculated)

mmol/L

Normal: 1.12-1.32 mmol/L

Ionized Ca (measured/direct)

mmol/L

From ABG/iSTAT. Overrides calculated value when available.

ℹ️ Why ionized calcium matters...

Formula: Corrected Ca = Total Ca + 0.8 × (4.0 - Albumin) | Ionized Ca ≈ 0.45 × Corrected Ca
Clinical significance: Only ~45% of serum calcium is physiologically active (ionized). The rest is protein-bound (40%) or complexed with anions (15%). Ionized calcium regulates muscle contraction, nerve function, blood clotting, and cardiac rhythm.

eGFR

mL/min/1.73m²

CKD Stage

BUN/Creat

ratio

Corrected Ca

mg/dL

Ca × P Product

mg²/dL²

⚠️ Calcium × Phosphate Product:

< 50: Target range (KDIGO compliant)
50-55: Borderline - close monitoring advised
> 55: High risk - above KDIGO target
> 70: Very high risk - metastatic calcification likely

Reference: KDOQI 2003 / KDIGO 2017 CKD-MBD Guidelines

⚡ Extended Electrolytes (Chloride, CO2, Anion Gap) v

🩸 CBC & ANC Calculator (Enhanced) v

WBC Count

Hemoglobin

Hematocrit (%)

Platelets (×10⁹/L)

RBC (×10¹²/L)

MCV (fL)

MCH (pg)

MCHC (g/dL)

RDW (%)

🧬 White Cell Differential

Neutrophils (×10⁹/L)

Lymphocytes (×10⁹/L)

Monocytes (×10⁹/L)

Eosinophils (×10⁹/L)

Basophils (×10⁹/L)

Calculated Percentages (from absolute ÷ WBC × 100) Neut: % Lymph: % Mono: % Eos: % Baso: %

🔬 Comprehensive ANC Calculator (Marrowforums Reference)

Input Format

Segmented Neutrophils %

Band Neutrophils %

Metamyelocytes %

Myelocytes %

Promyelocytes %

Blasts % ⚠️ Blasts >0% requires urgent hematology review

💡 Tip: Include ALL immature forms for accurate ANC in bone marrow disorders. Bands >10% indicates left shift (infection/stress).

ANC

cells/μL

Interpretation

Neutropenia Grade

Total Immatures %

📖 ANC Reference (marrowforums.org)

Formula: ANC = WBC × (Segs + Bands + Metas + Myelos + Promyelos) / 100 × 1000

<500	Severe neutropenia (Grade 4)	High infection risk
500-999	Moderate neutropenia (Grade 3)	Significant risk
1000-1499	Mild neutropenia (Grade 2)	Moderate risk
1500-8000	Normal range	Low risk
>8000	Neutrophilia	Investigate cause

🩸 Extended Hematology (Reticulocytes, ESR, Iron Studies) v

🔴 Hemolysis Panel (LDH, Haptoglobin, Bilirubin) v

🔬 Hemolysis Workup
Key markers for detecting intravascular and extravascular hemolysis. Fields auto-sync from other panels.

Haptoglobin ℹ️

Normal: 30-200 mg/dL | ↓ in hemolysis (consumed binding free Hb)

🔗 LDH (synced from Liver)

U/L

Normal: 140-280 U/L | ↑ in hemolysis (released from RBCs)

🔗 Indirect Bilirubin (synced from Liver)

mg/dL

Normal: 0.1-1.0 mg/dL | ↑ in hemolysis (unconjugated)

🔗 Reticulocytes (synced from CBC)

Normal: 0.5-2.5% | ↑ in hemolysis (compensatory)

Free Hemoglobin (Plasma)

mg/dL

Normal: <5 mg/dL | ↑ in intravascular hemolysis

Hemopexin

mg/dL

Normal: 50-115 mg/dL | ↓ in severe hemolysis

Hemolysis Assessment

Type

Hemolysis Score

🔴 Hemolysis Markers Interpretation

Classic Hemolysis Pattern:

↓ Haptoglobin (consumed binding free Hb)
↑ LDH (released from lysed RBCs)
↑ Indirect Bilirubin (Hb breakdown product)
↑ Reticulocytes (compensatory response)

Intravascular vs Extravascular:

Intravascular: Free Hb ↑↑, Hemoglobinuria, Hemosiderinuria
Extravascular: Splenomegaly, Jaundice, Gallstones

Ref: Williams Hematology | ASH Guidelines on Hemolytic Anemia

🍬 Diabetes Panel & HOMA v

Fasting Glucose

HbA1c

Normal: <5.7% / <39 mmol/mol | Prediabetes: 5.7-6.4% / 39-47 mmol/mol | Diabetes: ≥6.5% / ≥48 mmol/mol

Fasting Insulin

HOMA-IR

Insulin resistance

Est. Average Glucose

mg/dL

HOMA-IR Interpretation

<1.0: Normal sensitivity | 1.0-1.9: Early resistance | 2.0-2.9: Significant resistance | ≥3.0: Severe resistance

Formula: (Fasting Insulin × Fasting Glucose) / 405

Reference: Matthews DR et al. Diabetologia 1985. DOI: 10.1007/BF00280883

⚡ Quick Diabetes Calculations

🧪 Extended Diabetes (Fructosamine, C-Peptide, GAD65) v

🔥 Inflammation Panel v

hs-CRP

<1 low risk, 1-3 moderate, >3 high risk (mg/L)

ESR (mm/hr)

Ferritin

Normal: M 30-400, F 15-150 ng/mL

Homocysteine (µmol/L) >15 µmol/L associated with increased CVD risk

🔥 Extended Inflammatory (IL-6, TNF-α, Procalcitonin) v

❤ Cardiac Biomarkers v

💡 About Natriuretic Peptides: BNP and NT-proBNP are released by the heart in response to wall stress, making them valuable biomarkers for heart failure diagnosis and prognosis. They also provide cardiovascular risk stratification in the general population.

BNP

Heart failure unlikely: <100 pg/mL | Possible: 100-400 | Likely: >400

NT-proBNP

Age-adjusted cutoffs (HF exclusion):
<50 years: <450 pg/mL | 50-75 years: <900 pg/mL | >75 years: <1800 pg/mL

High-Sensitivity Troponin

99th percentile varies by assay; >14 ng/L suspicious for MI

HF Risk Assessment

Clinical Interpretation Guide

Biomarker	Low Risk	Intermediate	High Risk
BNP	<100 pg/mL	100-400 pg/mL	>400 pg/mL
NT-proBNP	<300 pg/mL	300-900 pg/mL	>900 pg/mL
hs-Troponin	<14 ng/L	14-52 ng/L	>52 ng/L

References: ESC 2021 HF Guidelines, ACC/AHA 2022 HF Guidelines, McDonagh TA et al. Eur Heart J 2021

❤️ Extended Cardiac (Myoglobin, CK-MB, H-FABP) v

🦋 Thyroid Function v

TSH

Normal: 0.4-4.0 mIU/L

Free T4

Normal: 9-25 pmol/L | 0.7-1.9 ng/dL

Free T3

Normal: 3-7 pmol/L | 200-440 pg/dL

ℹ Clinical Note: Thyroid dysfunction affects cardiovascular risk. Hypothyroidism increases LDL and CVD risk. Hyperthyroidism can cause arrhythmias and heart failure.

🧬 Hormones, Vitamins & Nuclear Receptor Ligands v

Compounds that cross cell membranes and bind to cytosolic or nuclear receptors

Vitamin D (25-OH)

Deficient: <50 nmol/L (<20 ng/mL)

Testosterone

Estradiol

Normal (premenopausal): 70-530 pmol/L

Cortisol AM

Normal AM: 140-690 nmol/L | 5-25 µg/dL

DHEA-S

Progesterone

IGF-1

SHBG (nmol/L)

FSH

Postmenopausal: 16-110

Postmenopausal: 11-58

💊 Vitamins & Nutrients

Vitamin B12

Deficient: <150 pmol/L, Borderline: 150-220, Normal: >220

Folate

Deficient: <7 nmol/L, Normal: 7-45

🔬 Mechanism: These lipophilic compounds cross cell membranes and bind to intracellular receptors (nuclear hormone receptors), regulating gene transcription. They affect cardiovascular risk through multiple pathways including lipid metabolism, inflammation, and vascular function.

🥞 Pancreatic Panel v

Lipase

Normal: 0-160 U/L | >3× ULN suggests acute pancreatitis

Amylase

Normal: 28-100 U/L

🧲 Iron Studies v

Serum Iron

Normal: 60-170 µg/dL (10.7-30.4 µmol/L)

TIBC (Total Iron Binding Capacity)

Normal: 250-370 µg/dL (44.8-66.3 µmol/L)

UIBC (Unsaturated IBC)

Normal: 150-300 µg/dL | TIBC = Iron + UIBC

Transferrin

Normal: 200-360 mg/dL (2.0-3.6 g/L)

sTfR (Soluble Transferrin Receptor)

Normal: 1.8-4.6 mg/L | Elevated in iron deficiency

Ret-He / CHr (Reticulocyte Hemoglobin)

Normal: >28 pg | Low = iron-restricted erythropoiesis

Hepcidin ℹ️

Normal: 5-35 ng/mL | Key iron regulator | ↑ in inflammation, ↓ in iron deficiency

🔗 Ferritin (synced from Hematology)

ng/mL

Edit in Hematology panel → auto-syncs here for iron calculations

Transferrin Saturation ℹ️

Normal: 20-50% | Formula: (Iron/TIBC) × 100

sTfR/Log Ferritin Index

ratio

>2 = iron deficiency | <1 = anemia of chronic disease

TSAT

Iron Status

sTfR/Log Ferritin

🩸 Iron Panel Interpretation

TSAT = (Serum Iron / TIBC) × 100. Essential for assessing iron status.
sTfR rises in iron deficiency but remains normal in anemia of chronic disease.
Ret-He reflects iron available for hemoglobin synthesis in the past 3-4 days.

TSAT <20% Iron Deficiency TSAT 20-50% Normal TSAT >50% Iron Overload

Ref: KDIGO Anemia Guidelines | ASH Iron Deficiency Guidelines 2021

🩺 Anemia Classification Module v

🔬 Anemia Classification System
Integrates CBC, Iron Studies, and clinical parameters for comprehensive anemia diagnosis

Hemoglobin (synced)

g/dL

Anemia: M <13.5, F <12.0 g/dL

MCV (synced)

Micro <80 | Normal 80-100 | Macro >100

MCHC (synced)

g/dL

Hypo <32 | Normal 32-36 | Hyper >36

RDW (synced)

Normal: 11.5-14.5% | Elevated = anisocytosis

Reticulocyte Count (synced)

Normal: 0.5-2.5% | Response indicator

Ferritin (synced)

ng/mL

Iron stores indicator

Anemia Classification

MCV Category

MCHC Category

Reticulocyte Production Index

🩺 Anemia Classification Algorithm

Step 1: Confirm anemia (Hgb <13.5 M / <12.0 F g/dL)

Step 2: Classify by MCV:

Microcytic (<80 fL): Iron deficiency, Thalassemia, Chronic disease, Sideroblastic
Normocytic (80-100 fL): Acute blood loss, Hemolysis, Early iron def, Chronic disease, Renal
Macrocytic (>100 fL): B12/Folate def, MDS, Liver disease, Hypothyroid, Reticulocytosis

Step 3: Assess reticulocyte response (RPI):

RPI >2: Adequate marrow response (hemolysis, blood loss)
RPI <2: Hypoproliferative (marrow failure, nutritional def)

Ref: ASH Anemia Guidelines | Williams Hematology

🚧 Module Under Development
Full anemia classification with differential diagnosis, peripheral smear integration, and treatment recommendations coming in future versions.

🔬 Tumor Markers v

⚠️ Note: Tumor markers are for monitoring, not screening.

PSA

ng/mL

Normal: <4 ng/mL (age-dependent)

CEA

ng/mL

Normal: <3 ng/mL

CA-125

U/mL

Normal: <35 U/mL

CA 19-9

U/mL

Normal: <37 U/mL

AFP

Normal: <10 ng/mL

Prolactin

Normal: M 4-15, F 4-23 ng/mL

💊 Current Medications

Active Medications

Expected LDL Reduction

PCSK9 Eligibility

Interactions Found

✅ v30.74.73: Medication table is now correctly positioned below summary cards

Medication Name	Dose	Frequency	Start Date	Duration (months)	Status	LDL Reduction %	Actions

📅 Medication Timeline

Add medications with start dates above to see the timeline

💊 Lipid-Lowering Drug Interaction Checker

This checker screens for common cardiovascular drug interactions including:

Statin + Fibrate: Increased myopathy risk (use fenofibrate over gemfibrozil)
Statin + CYP3A4 inhibitors: Avoid simvastatin with macrolides, azoles
PCSK9i + Statin: Synergistic effect (no interaction concern)
Ezetimibe + Cyclosporine: Increased ezetimibe exposure
Anticoagulants + NSAIDs: Increased bleeding risk

Note: Always verify interactions with clinical pharmacy resources.

📓 Related References: 💉 Lipid Science 🧬 Lp(a) Science 🫁 Liver/FIB-4 🫘 Kidney/eGFR 🔬 Metabolic 🩸 Hematology

📊 Baseline Lipid Tracking (NICE NG238 Compliance)

Track pre-treatment lipid values and monitor % reduction. NICE target: ≥40% non-HDL-C reduction from baseline.

No baseline recorded. Enter current lab values and click "Set Baseline" to start tracking.

📊 Framingham Risk Score Calculator

10-Year CVD Risk (D'Agostino et al., Circulation 2008)

📊 Select Framingham Algorithm:

📈 Using: D'Agostino 2008 (General CVD) - CCS 2021 Recommended

✅ Outcomes Predicted: CHD (MI, angina) + Stroke + Peripheral Artery Disease + Heart Failure + CV Death

📚 Understanding FRS Algorithm Differences: Wilson 1998 vs D'Agostino 2008 (Click to expand)

Feature	D'Agostino 2008 (CVD)	Wilson 1998 (CHD)
Outcomes Predicted	CHD + Stroke + PAD + Heart Failure	CHD Only (MI, angina, CHD death)
Risk Level Output	Higher (comprehensive)	Lower (narrower scope)
CCS 2021 Guideline	✅ Recommended	❌ Not recommended
MDCalc / EMR Calculators	❌ Not used	✅ Uses this (OSCAR, etc.)
Example: 72F, TC 6.43mmol/L, HDL 1.23, SBP 130	~24.8%	~6%

⚠️ Critical Interpretation Note

BOTH algorithms are mathematically correct - they simply measure different things. A patient showing 6% with Wilson 1998 and 25% with D'Agostino 2008 is NOT at lower risk - the 6% only captures coronary heart disease risk, while the 25% captures TOTAL cardiovascular risk including stroke, peripheral artery disease, and heart failure.

Clinical implication: Using Wilson 1998 may underestimate total cardiovascular burden, especially in elderly patients where stroke and heart failure risk become significant.

🗺️ Geographic & Situational Usage Guide

🇨🇦 Canada

Recommended: D'Agostino 2008

CCS 2021 Dyslipidemia Guidelines explicitly recommend the D'Agostino 2008 General CVD algorithm. Risk thresholds: Low <10%, Intermediate 10-19.9%, High ≥20%.

🇺🇸 United States

Recommended: PREVENT (2024) or ASCVD PCE (2013)

ACC/AHA guidelines favor PREVENT equations (2024) or Pooled Cohort Equations (2013). Framingham is acceptable but less commonly used in current practice.

🇬🇧 United Kingdom

Recommended: QRISK3

NICE guidelines mandate QRISK3 for UK populations. It includes ethnicity, deprivation index, and conditions specific to UK demographics.

🇪🇺 Europe

Recommended: SCORE2 / SCORE2-OP

ESC 2021 guidelines recommend SCORE2 (ages 40-69) or SCORE2-OP (70+) calibrated to 4 European risk regions.

💻 MDCalc / EMR Matching

Use: Wilson 1998

To match MDCalc results or most EMR-embedded calculators (OSCAR, Epic legacy), select Wilson 1998. Useful for comparison and historical data.

🧓 Elderly / Comprehensive Risk

Use: D'Agostino 2008

In elderly patients, stroke and heart failure risk become substantial. Wilson 1998 misses these outcomes. D'Agostino 2008 provides comprehensive risk.

References:
• Wilson PW et al. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47. • D'Agostino RB Sr et al. General cardiovascular risk profile for use in primary care. Circulation 2008;117:743-53. • Pearson GJ et al. 2021 CCS Guidelines for the Management of Dyslipidemia. Can J Cardiol 2021;37:1129-1150.

🧬

Important: Lp(a) Is Not Part of the Original Framingham Equation

The Framingham Risk Score was not calibrated to account for Lipoprotein(a) levels. Lp(a) was not measured in the original Framingham cohort, and therefore its independent cardiovascular risk contribution is not captured in this model.

The Lp(a) risk modifier shown below has been added as a demonstration of the potential effects elevated Lp(a) could have on cardiovascular risk if it were incorporated into predictive models. This implementation is based on contemporary evidence from EAS 2022 consensus guidelines and Mendelian randomization studies, which suggest Lp(a) acts as an independent, multiplicative risk factor.

💡 Purpose: This feature demonstrates the importance of factoring Lp(a) into future cardiovascular risk prediction models and highlights the potential under-estimation of risk in patients with elevated Lp(a) when using traditional calculators.

📋 Risk Factor Inputs (9 Required)

Enter values here OR they will auto-populate from Patient & Labs tabs if filled there.

Age (30-79)Valid: 30-79 years

Biological Sex

Total Cholesterol

HDL Cholesterol

Systolic BP (mmHg)

BP Treatment

🚬 Smoking Status

Diabetes

🔄 Auto-Sync: If you've entered data in the Patient & Labs tabs, click "Sync from Patient Tab" to pull those values here.

🧬 Lipoprotein(a) - Lp(a) Integration v

Lp(a) Value

Unit

Calculated Risk Modifier Format: 1.5× (auto-calculated from value above or manually select)

Note: Lp(a) is not part of the original Framingham equation but is applied as a risk multiplier based on current evidence (EAS 2022 consensus).

📈 Results

Algorithm Used

D'Agostino 2008

General Cardiovascular Disease

Endpoints Captured

MI • Stroke • PAD • Heart Failure • CV Death

✅

CCS 2021
Recommended

Circulation 2008;117:743

Age Pts	TC Pts	HDL Pts	SBP Pts	Smoking	Diabetes	Total Pts	Base Risk %	Modified Risk %
--	--	--	--	--	--	--	--	--

10-Year Base Risk

Lp(a)-Modified Risk
%

Risk Category

Heart Age

years

NNT (5yr)

to prevent 1 event

Recommendations:

📋 FRS Calculation History

📈 Pre-Modifier Risk

Base 10-Year Risk

🧬 Lp(a) Modifier

1.0×

✅ Post-Modifier Risk

Risk Category

💡 Tip: Click any row in the table below to display its values in the boxes above.

Patient ID	BMI	BRI	Pre-Modifier Risk	Lp(a) Level	Lp(a) Modifier	Post-Modifier Risk	Risk Level	Algorithm	Actions

Results are automatically added after each calculation. Calculations continue under the same Patient ID until a new ID is generated.

📛 Reference

Citation: D'Agostino RB et al. Circulation 2008;117:743-53. DOI: 10.1161/CIRCULATIONAHA.107.699579

Risk Categories: Low (<10%), Intermediate (10-19%), High (≥20%)

Validation: framinghamheartstudy.org

📓 Related References: ❤ CVD Risk Calculators 🧬 Lp(a) Science 📋 Clinical Guidelines ─⚠️ Validation Tools

🇬🇧 QRISK3/QRISK4 Calculator

UK-validated CVD risk with ethnicity, deprivation, and 14+ conditions

⚠️ Important Calculation Notice:

This toolkit implements calibrated QRISK3/4 algorithms using official ClinRisk fractional polynomial coefficients (qrisk.org/src.php). For clinical decision-making, please validate results using the official calculator at qrisk.org.

For clinical decision-making, please validate results using the official calculator at qrisk.org. Minor variations may occur due to implementation differences.

🧬

Important: Lp(a) Is Not Part of the Original QRISK3/4 Equations

Neither QRISK3 nor QRISK4 were calibrated to account for Lipoprotein(a) levels. While QRISK includes many cardiovascular risk factors, Lp(a) was not systematically measured in the derivation cohorts, and its independent risk contribution is not captured in these models.

The Lp(a) risk modifier available in this toolkit has been added as a demonstration of the potential effects elevated Lp(a) could have on cardiovascular risk if it were incorporated into these UK-validated equations. This is based on evidence suggesting Lp(a) is an independent, causal risk factor for ASCVD with approximately 2-fold increased risk at levels ≥50 mg/dL.

💡 Research Implication: This feature highlights the potential gap in current risk models for patients with elevated Lp(a) and demonstrates the importance of developing next-generation prediction tools that incorporate this genetically-determined risk factor.

🔬 Understanding QRISK3 vs QRISK4

QRISK3 (2017)

Based on 10+ million UK patient records
9 ethnicity categories with specific coefficients
14 clinical conditions (RA, SLE, CKD, AF, etc.)
Townsend deprivation score integration
Validated in multiple UK populations

QRISK4 (2024) - New Features

BP Variability: SBP standard deviation from 6+ readings
Long COVID: Post-COVID cardiovascular effects
Air Pollution: Environmental exposure factor
Enhanced calibration for contemporary populations
Better discrimination in younger patients

💡 Tip: Use QRISK3 for standard UK assessments (includes BP variability since 2017). Use QRISK4 when the patient has any of 9 new conditions: or for patients with Long COVID or high pollution exposure. Both scores are calculated simultaneously for comparison.

👤 Demographicsv

Age (25-84)

Biological Sex

Ethnicity (QRISK3 + ASCVD PCE) ⬤ = ASCVD PCE validated | ◐ = ASCVD uses White coefficients*

Townsend Deprivation Score (Optional) Range: -8 (affluent) to +12 (deprived)

ℹ About Townsend Score:
The Townsend Deprivation Index is a UK-specific measure of socioeconomic status based on:
• Unemployment rate
• Non-car ownership
• Non-home ownership
• Household overcrowding

How to determine: In the UK, look up your postcode at ukpostcode.co.uk or similar services. For non-UK users, leave at 0 (average) or estimate based on local socioeconomic conditions.

Impact: Higher scores (more deprived areas) increase calculated CVD risk by ~3% per point.

🇨🇦 Canadian Users: Use the Canadian Marginalization Index (CAN-Marg) as a proxy. Highly deprived = +4 to +8, Average = 0, Affluent = -4 to -6.

📏 Clinical Measurementsv

BMI (kg/m²)

Systolic BP (mmHg)

TC/HDL Ratio

SBP Std Dev (QRISK3/4) ℹ️

🧬 Lipoprotein(a) - Lp(a) for Risk Enhancement v

Lp(a) Value

Unit

Note: Lp(a) is not part of QRISK3/4 algorithms but elevated levels ≥50 mg/dL indicate enhanced cardiovascular risk and may warrant more aggressive LDL targets per CCS 2021 guidelines.

Input Completeness 0%

⚖️ QRISK3 vs QRISK4 Comparison

Algorithm 1

QRISK3

Hippisley-Cox et al. 2017

22 standard risk factors

Algorithm 2

QRISK4

2017: Adds BP variability (SBP-SD)

SBP standard deviation from 6+ readings

⚠️

SBP-SD
Not Provided

QRISK3/4 BP variability

Calculate from readings ↑

Factor	QRISK3	QRISK4
10-Year CVD Risk	--%	--%
Risk Category	--	--
Heart Age	-- years	-- years
Difference (QRISK4 - QRISK3)	--

QRISK3

QRISK4

QRISK3 + Lp(a)

QRISK4 + Lp(a)

Risk Category

Heart Age

years

NNT (5yr)

to prevent 1 event

Post-Statin Risk

% (est.)

Relative Risk vs Healthy

× age-matched

📋 QRISK Calculation History

📈 Pre-Modifier Risk

QRISK3

QRISK4

🧬 Lp(a) Modifier

1.0×

✅ Post-Modifier Risk

QRISK3

QRISK4

🎯 Risk Category

Patient ID

💡 Tip: Click any row in the table below to display its values in the boxes above.

Patient ID	BMI	BRI	Pre-Mod QRISK3	Pre-Mod QRISK4	Lp(a) Level	Modifier	Post-Mod QRISK3	Post-Mod QRISK4	Risk Level	SBP-SD	Actions

Calculations continue under the same Patient ID until a new ID is generated. Click "Recommendations" to view treatment options.

📛 Reference

QRISK3: Hippisley-Cox J et al. BMJ 2017;357:j2099

QRISK4: Adds 9 new conditions (cancers, COPD, Down syndrome, learning disability; pre-eclampsia & postnatal depression for women). Note: SBP-SD was already in QRISK3.

Validation: qrisk.org | NICE CG181

📓 Related References: ❤ CVD Risk Calculators 🌎 Ethnic Considerations 📋 Clinical Guidelines ─⚠️ Validation Tools

Outcome	Horizon	Base Risk	Modified	Delta
ASCVD	10-yr	--	--	--
ASCVD	30-yr	--	--	--
Heart Failure	10-yr	--	--	--
Heart Failure	30-yr	--	--	--

Patient ID	Model	ASCVD 10yr	ASCVD 30yr	HF 10yr	HF 30yr	Lp(a) Mod	CAC	Category	Time
No calculations yet. Fill in the fields above and click Calculate.

📈 Combined Risk Assessment & International Scores

Compare all risk scores: Framingham, QRISK3/4, PREVENT, SCORE2/SCORE2-OP/SCORE2-Diabetes

Runs Framingham, QRISK3/4, SCORE2/OP, PREVENT (10yr+30yr), Reynolds & ASCVD PCE using Patient & Labs data

⚙ Detecting...

📚 Understanding CVD Risk Calculators (Click to Expand)

🇪🇺 SCORE2 Family (ESC 2021/2023)

The Systematic COronary Risk Evaluation 2 (SCORE2) is the European Society of Cardiology's recommended calculator for estimating 10-year risk of fatal and non-fatal CVD events (MI, stroke, CV death) in apparently healthy Europeans without prior CVD.

SCORE2 (Ages 40-69, Non-Diabetic)
Inputs: Age, Sex, Smoking, SBP, Total Cholesterol, HDL-C, Risk Region

SCORE2-OP (Ages 70+, Older Persons)
Same inputs as SCORE2, recalibrated for older populations with different competing risks

SCORE2-Diabetes (Ages 40-69, Type 2 Diabetes) - ESC 2023
Additional inputs: HbA1c, Age at diabetes diagnosis, eGFR. Provides diabetes-specific risk stratification

Key feature: Risk estimates are calibrated to 4 European risk regions based on WHO CVD mortality data.

🇺🇸 PREVENT Equations (AHA 2024)

Predicting Risk of CVD EVENTs is the American Heart Association's newest risk calculator, replacing the 2013 Pooled Cohort Equations (PCE). Based on data from 6.6 million diverse US adults, it estimates 10-year and 30-year risk for ages 30-79.

Key Innovations:

No race variable - promotes equitable risk assessment
Includes Heart Failure - predicts Total CVD, ASCVD, and HF separately
CKM Integration - incorporates BMI, eGFR (cardiovascular-kidney-metabolic factors)
Optional Enhancers - HbA1c, UACR, Social Deprivation Index (SDI)
Statin adjustment - accounts for current statin use

Reference: Khan SS et al. Circulation 2024;149:430-449

✅ Which Calculator Should I Use?

European patient, no diabetes	SCORE2 (40-69) or SCORE2-OP (70+)
European patient with Type 2 DM	SCORE2-Diabetes (preferred)
US/North American patient	PREVENT (with optional enhancers)
UK patient	QRISK3 (NICE-recommended), SCORE2 (ESC)
Young adults (30-39)	PREVENT only (SCORE2 starts at 40)
Heart failure risk focus	PREVENT (includes HF-specific model)

📊 Risk Score Comparison

Framingham 10-Year
(D'Agostino - CVD)

ASCVD 10-Year (PCE 2013)
ACC/AHA

Lifetime Risk
to Age 80

Using: White coefficients

Reynolds Risk

CAC Score (Agatston) 0 = no calcification

CAC Category

CAC-Adjusted Risk

QRISK3

QRISK4 (+9 new factors)

⚠️ No SBP-SD — add BP readings

Lifetime Risk

% to age 95

Heart Age

years

SCORE2/OP

SCORE2-Diabetes

PREVENT 10-Year

PREVENT 30-Year

PREVENT ASCVD

PREVENT HF

PREVENT CHD

PREVENT Stroke

PREVENT Model

💾

Save This Assessment

Save all risk scores, labs, and patient data to History tab for tracking

🌍 External Validation Tools, International Models & Clinical Notes (v' + CVD_TOOLKIT_VERSION + ')

✅ PREVENT Validation
Validated against preventr R package v0.11.0 (Sadler et al.).
10yr model: ages 30-79 | 30yr model: ages 30-59 only
GitHub: bcjaeger/preventr

🇪🇺 SCORE2/OP Validation
Calibrated vs ESC 2021 published examples.
CE-marked reference: U-Prevent.com
SCORE2-OP: Fine-Gray competing risk model (ages 70-89)

🌏 SCORE2 Asia-Pacific (ESC 2025)
Ages 40-69 only. No SCORE2-OP equivalent for Asian 70+.
Not yet implemented — awaiting coefficient extraction.
R Shiny Validator (scientific use only)

⚠️ SCORE2-Diabetes Scope
ESC 2023 model: European populations only (ages 40-69).
Not validated for Asia-Pacific populations.
Asian stroke subtypes (haemorrhagic > ischaemic) not modeled.

🇺🇸 ACC/AHA 2013 ASCVD (Pooled Cohort Equations) - Description & Validation

🧬 What is ASCVD PCE?

The Pooled Cohort Equations (PCE) were developed by the ACC/AHA in 2013 to estimate 10-year risk of a first hard atherosclerotic cardiovascular disease (ASCVD) event, defined as:

Nonfatal myocardial infarction (heart attack)
Fatal coronary heart disease
Nonfatal or fatal stroke

📊 Key Features

Race-specific equations: Separate coefficients for White and African American populations
Age range: Validated for ages 40-79
Continuous variables: Uses logarithmic transformations for more precise risk estimation
Widely adopted: Basis for statin therapy recommendations in ACC/AHA guidelines since 2013

🎯 Risk Categories (ACC/AHA 2018)

Risk Category	10-Year Risk	Statin Recommendation
Low	<5%	Lifestyle modification; discuss if risk enhancers present
Borderline	5% to <7.5%	Consider moderate-intensity statin if risk enhancers present
Intermediate	7.5% to <20%	Moderate-intensity statin recommended; consider CAC if decision uncertain
High	≥20%	High-intensity statin; consider adding ezetimibe

⚖️ ASCVD PCE vs PREVENT ASCVD

These are different calculators!
• ASCVD PCE (2013): Original Pooled Cohort Equations - race-specific, ages 40-79
• PREVENT ASCVD (2024): Newer AHA equations - no race variable, ages 30-79, includes kidney function

✅ Validation Status

This implementation has been validated against the official ACC ASCVD Risk Estimator Plus.
Test case (55M White, TC 213, HDL 50, SBP 120, non-smoker, non-diabetic): Expected 5.3%, Calculator shows 5.3% ✓

⚠️ Limitations

May overestimate risk in some contemporary populations
Not validated for races other than White or African American
Being superseded by PREVENT equations (2024) for new patients
Does not include Lp(a), family history, or other emerging risk factors

Reference: Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. Circulation. 2014;129(25 Suppl 2):S49-73.

📖 Reynolds Risk Score - Description & Interpretation

🧬 What is the Reynolds Risk Score?

The Reynolds Risk Score (RRS) is a cardiovascular risk prediction model developed by Dr. Paul Ridker and colleagues, published in 2007-2008. It was designed to improve upon the Framingham Risk Score by incorporating two additional biomarkers: high-sensitivity C-reactive protein (hs-CRP) and parental history of MI before age 60.

📊 What It Determines

The Reynolds Risk Score estimates the 10-year probability of developing a major cardiovascular event, including:

Myocardial infarction (heart attack)
Ischemic stroke
Coronary revascularization (CABG or PCI)
Cardiovascular death

🎯 Risk Categories

Risk Category	10-Year Risk	Clinical Interpretation
Low	<5%	Lifestyle modification; reassess in 5 years
Intermediate-Low	5-10%	Consider additional risk assessment (CAC, Lp(a))
Intermediate-High	10-20%	Discuss statin therapy; intensive lifestyle changes
High	≥20%	Recommend statin therapy; target LDL reduction ≥50%

📝 Key Inputs

Age, Sex, Systolic BP, Total Cholesterol, HDL, hs-CRP, Current Smoking, Parental MI <60, HbA1c (if diabetic)

📚 Reference

Women: Ridker PM et al. Circulation 2007;115:450-8. DOI: 10.1161/CIRCULATIONAHA.106.659482
Men: Ridker PM et al. Circulation 2008;117:2458-66. DOI: 10.1161/CIRCULATIONAHA.107.756271

🇪🇺 SCORE2 Family - Description & Interpretation

🧬 What is SCORE2?

SCORE2 (Systematic COronary Risk Evaluation 2) is the updated European cardiovascular risk prediction system recommended by the European Society of Cardiology (ESC) 2021 guidelines. It replaces the original SCORE algorithm and predicts both fatal AND non-fatal CVD events.

🗺 SCORE2 Family Variants

SCORE2: For individuals aged 40-69 years without diabetes
SCORE2-OP: For older persons aged 70+ years
preventr R Package: Mayer M. preventr: An Implementation of the PREVENT and Pooled Cohort Equations. R package version 0.11.0, 2025. CRAN: preventr — v30.74.292: Coefficient source (100 coefficient sets validated)
SCORE2-Diabetes: For Type 2 diabetics aged 40-69 years (ESC 2023)

📊 What It Determines

10-year risk of fatal + non-fatal cardiovascular events (MI, stroke, CVD death). Unlike original SCORE (fatal only), SCORE2 captures the full burden of CVD.

🎯 Risk Thresholds by Age Group

Category	Age <50	Age 50-69	Age ≥70
Low-Moderate	<2.5%	<5%	<7.5%
High	2.5-7.5%	5-10%	7.5-15%
Very High	≥7.5%	≥10%	≥15%

🌍 Risk Regions

Low: Belgium, Denmark, France, Israel, Norway, Spain, Switzerland, UK
Moderate: Austria, Cyprus, Finland, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Slovenia, Sweden
High: Bosnia, Croatia, Czech Republic, Estonia, Hungary, Poland, Slovakia, Turkey
Very High: Albania, Bulgaria, Latvia, Lithuania, Moldova, Romania, Russia, Serbia, Ukraine

💉 SCORE2-Diabetes Specifics

For Type 2 diabetics, includes: HbA1c (mmol/mol), Age at DM diagnosis, and eGFR as additional predictors.

📚 References

SCORE2: SCORE2 Working Group. Eur Heart J 2021;42:2439-54
SCORE2-OP: SCORE2-OP Working Group. Eur Heart J 2021;42:2455-67
SCORE2-Diabetes: ESC Working Group. Eur Heart J 2023;44:2544-56

🇺🇸 PREVENT Equations - Description & Interpretation

🧬 What is PREVENT?

PREVENT (Predicting Risk of cardiovascular disease EVENTs) is a comprehensive cardiovascular risk prediction model developed by the American Heart Association (AHA) and published in 2024. It represents a major update to cardiovascular risk assessment, designed for use in diverse US populations.

📊 What It Determines

PREVENT provides separate risk estimates for:

Total CVD: Combined risk of ASCVD + Heart Failure
ASCVD: Atherosclerotic cardiovascular disease (MI, stroke)
Heart Failure: New-onset heart failure requiring hospitalization
10-year and 30-year risk horizons

🏛 Model Variants

Model	Required Inputs	Use Case
Base	Age, Sex, Race, SBP, TC, HDL, DM, Smoking, eGFR, BMI, BP Rx, Statin	Standard clinical assessment
Enhanced	Base + UACR + HbA1c (if diabetic)	CKD and diabetes refinement
Full (+ SDI)	Enhanced + Social Deprivation Index	Social determinants integration

🎯 Risk Categories (10-Year Total CVD)

Category	Risk %	Clinical Implications
Low	<5%	Lifestyle optimization; reassess in 4-6 years
Borderline	5-7.5%	Risk discussion; consider CAC, Lp(a) testing
Intermediate	7.5-20%	Moderate-intensity statin; risk-enhancers guide therapy
High	≥20%	High-intensity statin; target LDL ≥50% reduction

✨ Key Innovations

Includes eGFR as core predictor (kidney-heart axis)
Separate Heart Failure prediction model
Works for ages 30-79 (younger than SCORE2)
Social determinants via ZIP-code SDI
30-year lifetime risk for younger patients

📚 Reference

Khan SS et al. Circulation 2024;149:e1144-e1156. DOI: 10.1161/CIR.0000000000001191

🔬 Liver Fibrosis & Cardiometabolic Risk Summary

Non-invasive liver fibrosis scores and CKD-liver interaction assessment

FIB-4 Score

Fibrosis index

NAFLD-FS

NAFLD fibrosis

APRI

AST/Plt ratio

ELF Score

Direct markers

CKD-FIB4 Combined Risk

Cardiometabolic

Interpretation Guide:
◇ FIB-4: <1.30 (low) | 1.30-2.67 (indeterminate) | >2.67 (high)
◇ NAFLD-FS: <-1.455 (low) | -1.455 to 0.676 (indeterminate) | >0.676 (high)
◇ ELF: <7.7 (no/mild) | 7.7-9.8 (moderate) | ≥9.8 (advanced fibrosis)
◇ CKD-FIB4: Combined kidney-liver risk multiplicatively increases CV mortality

🇺🇸 AHA PREVENT Calculator (2024)

Predicting Risk of CVD EVENTs - Khan SS et al., Circulation 2024

Estimates 10-year and 30-year risk for Total CVD, ASCVD (MI/Stroke), and Heart Failure in US adults ages 30-79 without prior CVD.

🫀 PREVENT (AHA 2024)

ASCVD 10yr

--%

HF 10yr

--%

Lp(a) Modifier

--×

Model

Calculate in the dedicated PREVENT tab for full results including vascular age, CAC reclassification, and treatment implications.

PREVENT Model

Auto mode: Uses UACR model if UACR provided, HbA1c model if HbA1c provided, Full model if both + SDI, otherwise Base.

BMI (kg/m²)

eGFR (mL/min/1.73m²)

UACR (mg/g) (Enhanced)

HbA1c (%) (If Diabetic)

🚬 Smoking Status

Currently on Statin Therapy

On Antihypertensive Therapy

Social Deprivation Index (1-10) (Optional - Full Model)

📍 About Social Deprivation Index (SDI): ZIP code-based composite measuring poverty, education, housing, employment, and healthcare access. Scale: 1 (least deprived) to 10 (most deprived). Lookup: graham-center.org/maps | Non-US: Use 5 (average) or local equivalent.

📊 PREVENT Risk Estimates

10-YEAR RISK

Total CVD

ASCVD

Heart Failure

CHD

Stroke

30-YEAR RISK

Total CVD

ASCVD

Heart Failure

CHD

Stroke

Risk Category

Model Used

📋 PREVENT Risk Thresholds (Anticipated ACC/AHA 2025):

Low: <5% (lifestyle modification)
Borderline: 5-7.5% (consider risk enhancers, lifestyle)
Intermediate: 7.5-20% (moderate-intensity statin if risk discussion favorable)
High: ≥20% (high-intensity statin recommended)

💊 Treatment Effect Simulator

Model the impact of various treatments on your cardiovascular risk

📊 Current Risk Profile

Current LDL-C (mg/dL)

Current SBP (mmHg)

Baseline 10-yr Risk (%)

Target LDL-C (mg/dL)

💉 Select Treatments to Simulate

High-Intensity Statin (50% LDL↓, 35% RR↓) Moderate-Intensity Statin (35% LDL↓, 25% RR↓) + Ezetimibe (18% additional LDL↓) + PCSK9 Inhibitor (60% additional LDL↓) Antihypertensive Therapy (~10 mmHg↓, 20% RR↓/10mmHg) SGLT2 Inhibitor (30% HF↓, 10% MACE↓) GLP-1 RA (14% MACE↓, 3-7% weight loss) Inclisiran (Leqvio) (50% LDL↓, Q6M dosing) Bempedoic Acid (18% LDL↓, 13% MACE↓)

🔮 What-If Scenario Modeling

Compare your current risk against optimized scenarios

💊 Ready for Treatment Planning?

View evidence-based treatment recommendations based on your calculated risk scores

📄 Export Complete Report

Generate a comprehensive PDF report with all risk scores, laboratory values, visualizations, and treatment recommendations.

📓 Related References: ❤ CVD Risk Calculators 💉 Lipid Science 📋 Clinical Guidelines 🫘 eGFR Methods

💚 Cardiorenal Protection Assessment

Diabetes Canada 2024 GLP-1 RA/SGLT2i Guidelines + KDIGO 2022 CKD Staging (ADA-KDIGO Consensus: de Boer IH et al. Diabetes Care 2022;45(12):3075-90)

⚠️ KDIGO 2026 guideline update in public review — pending integration upon final publication

🏥 CKD Staging (KDIGO 2024)

Serum Creatinine

Albumin-to-Creatinine Ratio (ACR)

eGFR Calculation Method

Calculated eGFR

-- mL/min/1.73m²

📊 KDIGO CKD Risk Heat Map

Combined risk based on eGFR stage and albuminuria category

eGFR Stage	A1 <30 mg/g	A2 30-300 mg/g	A3 >300 mg/g
G1 ≥90	Low	Moderate	High
G2 60-89	Low	Moderate	High
G3a 45-59	Moderate	High	Very High
G3b 30-44	High	Very High	Very High
G4 15-29	Very High	Very High	Very High
G5 <15	Very High	Very High	Very High

Low Risk - Annual monitoring

Moderate - Annual monitoring

High - Q6 months

Very High - Q3-4 months

Reference: KDIGO 2024 Clinical Practice Guideline for CKD Evaluation and Management

📉 Kidney Failure Risk Equation (KFRE)

4-variable KFRE for patients with eGFR <60

Prediction Timeframe

📆 CKD Monitoring Frequency (KDIGO 2024)

Risk Level	Monitoring Frequency	Key Tests	Actions
Low	Annually	eGFR, ACR, BP	Standard CVD risk management
Moderate	Annually	eGFR, ACR, BP, lipids	Optimize BP & glucose; consider SGLT2i
High	Every 6 months	eGFR, ACR, K+, bicarb, BP	SGLT2i if eligible; ACEi/ARB titration
Very High	Every 3-4 months	eGFR, ACR, K+, bicarb, Ca/PO4, Hgb, BP	Nephrology referral; KRT planning if KFRE >10%

❤️ Cardiorenal Indications

💥 Type 2 Diabetes ❤ Atherosclerotic CVD 💚 Heart Failure 📋 Chronic Kidney Disease 💧 Albuminuria (ACR >3) ⚠️ High CV Risk (≥2 factors)

💊 Current Cardiorenal Therapy

💉 GLP-1 RA Therapy

⭕ GLP-1 RA: Not on therapy

💊 SGLT2 Inhibitor

⭕ SGLT2i: Not on therapy

💊 Statin Therapy

⭕ Statin: Not on therapy

❤️ RAAS Blockade

⭕ ACEi/ARB: Not on therapy

📋 Cardiorenal Assessment

📚 Quick Reference: Cardiorenal Agents

GLP-1 RA Options (MACE Benefit)

Semaglutide (Ozempic)	26% MACE ↓
Semaglutide (Rybelsus)	14% MACE ↓
Liraglutide (Victoza)	13% MACE ↓
Dulaglutide (Trulicity)	12% MACE ↓

SGLT2i Options (by eGFR)

Empagliflozin (Jardiance)	eGFR ≥20
Dapagliflozin (Forxiga)	eGFR ≥25
Canagliflozin (Invokana)	eGFR ≥30

HF benefit independent of diabetes status

References: Diabetes Canada 2024 CPG, CCS 2022 GLP-1/SGLT2 Guidelines, KDIGO 2024

💊 Medication Dose Adjustment by eGFR (KDIGO 2024)

Select a medication to see dose recommendations based on current eGFR

Select Medication

Current eGFR (mL/min/1.73m²)

⚠ Important: Always verify dose adjustments with current product monograph and consider individual patient factors. Consult nephrology for eGFR < 30 mL/min.

🏥 Nephrology Referral Criteria (KDIGO 2024)

🚨 Urgent Referral

eGFR < 15 mL/min (unless stable on dialysis)
Rapid eGFR decline > 5 mL/min/year
UACR > 300 mg/g with hematuria
Refractory hypertension (> 4 agents)
Hyperkalemia > 6.0 mEq/L
Suspected glomerulonephritis

📋 Routine Referral

eGFR < 30 mL/min
UACR > 300 mg/g
Sustained eGFR decline > 3 mL/min/year
Unexplained anemia (Hgb < 10 g/dL)
Resistant hypertension
Unexplained hematuria

📈 eGFR Trend Calculator & Trajectory

Enter multiple eGFR readings to calculate decline rate and estimate time to ESKD

📋 Enter Historical eGFR Values

Date

eGFR (mL/min/1.73m²)

📚 Reference: KDIGO defines rapid progression as eGFR decline >5 mL/min/1.73m²/year. Normal age-related decline is ~0.5-1.0 mL/min/year after age 40.
Note: Minimum 2 readings required; accuracy improves with more data points over longer periods.

📉 KFRE Risk Trajectory Visualization

Visualize how kidney failure risk changes based on different scenarios

⚙️ Configure Scenario

Current Age (years)

Sex

Current eGFR (mL/min/1.73m²)

ACR (mg/g)

eGFR Decline Rate (mL/min/year)

Projection Years

Compare Treatment Scenarios:

Baseline (no intervention) + SGLT2i (↓30% decline) + ACEi/ARB (↓20% decline) Optimal (SGLT2i + RAASi)

📚 References:

Tangri N et al. KFRE validation. JAMA 2016;315(2):164-174
DAPA-CKD: 39% reduction in kidney failure risk with dapagliflozin
CREDENCE: 30% reduction in kidney outcomes with canagliflozin

🧬 Lipoprotein(a) Assessment

Comprehensive Lp(a) risk evaluation and family cascade screening

👨 Why This Toolkit Was Created

This toolkit was created because of my personal family history with elevated Lipoprotein(a). After discovering my own elevated Lp(a) levels, I learned that this genetic risk factor affects approximately 1 in 5 people worldwide, yet remains vastly under-tested and under-recognized.

Because Lp(a) is ~90% genetically determined, there's a 50% chance that first-degree relatives (parents, siblings, children) also have elevated levels. This makes cascade screening critically important for identifying at-risk family members early.

My goal is to help clinicians and patients better understand this often-overlooked cardiovascular risk factor and facilitate appropriate testing and family screening. Early awareness can lead to more aggressive management of modifiable risk factors and potentially life-saving interventions.

Emerging therapies targeting Lp(a), including antisense oligonucleotides and small interfering RNAs (siRNAs), are currently in Phase 3 clinical trials and may soon provide the first effective treatments for this genetic condition.

-- Manjinder Singh Nanrey

🔬 Lp(a) Risk Assessment

Lp(a) Value

Unit

⚠️

Critical Notice: Traditional Risk Calculators Do NOT Account for Lp(a)

The Framingham Risk Score, QRISK3, and QRISK4 equations were not calibrated to account for Lipoprotein(a) levels. Lp(a) was not systematically measured in the original derivation cohorts for these calculators, meaning its independent and causal contribution to cardiovascular disease risk is not captured in their predictions.

The Lp(a) risk modifiers shown below and throughout this toolkit are demonstrations of the potential effects that elevated Lp(a) could have on estimated cardiovascular risk if it were incorporated into these validated risk prediction models. These modifications are based on contemporary evidence from:

EAS 2022 Consensus Statement on Lipoprotein(a)
Mendelian randomization studies demonstrating causal relationship
Meta-analyses showing ~2-fold risk increase at levels ≥50 mg/dL
Copenhagen studies on extreme Lp(a) levels and ASCVD outcomes

💡 Why This Matters:

By demonstrating how Lp(a) could modify risk estimates, we highlight the critical importance of incorporating this genetic risk factor into future cardiovascular risk prediction models. Patients with elevated Lp(a) may have their true cardiovascular risk significantly underestimated by current calculators, potentially leading to under-treatment.

📊 Lp(a) Effect on Risk Calculator Estimates

The panels below demonstrate how your Lp(a) level could theoretically modify risk estimates from traditional calculators. Enter your Lp(a) value above, then click each panel to see the projected impact.

📊 Effect on Framingham Risk Score (FRS) v

Your Lp(a) Level

Lp(a) Risk Multiplier

Risk Category Shift

Projected FRS with Lp(a) Adjustment

Original FRS (if calculated)

--%

→

Lp(a)-Modified FRS

--%

⚠️ Demonstration Only: The Framingham equation does not include Lp(a). This projection illustrates potential risk underestimation in patients with elevated Lp(a).

🇬🇧 Effect on QRISK3 v

Your Lp(a) Level

Lp(a) Risk Multiplier

Treatment Threshold Impact

Projected QRISK3 with Lp(a) Adjustment

Original QRISK3 (if calculated)

--%

→

Lp(a)-Modified QRISK3

--%

🏁 NICE/SIGN Treatment Threshold Analysis:

Enter Lp(a) value to see if elevated Lp(a) would push you above the 10% statin initiation threshold.

⚠️ Demonstration Only: QRISK3 does not include Lp(a) in its 14 risk factors. This illustrates how patients with elevated Lp(a) may fall below treatment thresholds despite elevated true risk.

🆕 Effect on QRISK4 (2024) v

Your Lp(a) Level

Lp(a) Risk Multiplier

Combined Risk Gap

Projected QRISK4 with Lp(a) Adjustment

Original QRISK4 (if calculated)

--%

→

Lp(a)-Modified QRISK4

--%

🔬 Research Insight:

Even QRISK4, despite adding 7 new factors in 2024 (COPD, learning disability, Down syndrome, 4 cancer types), still does not include Lp(a). This represents a significant opportunity for future model development, as Lp(a) is one of the strongest genetic risk factors for ASCVD and affects approximately 20% of the global population.

⚠️ Demonstration Only: QRISK4 does not include Lp(a) despite its established causal role in ASCVD. Future iterations of QRISK or new models like PREVENT may incorporate Lp(a).

🇺🇸 Effect on PREVENT Equations (AHA/ACC 2024) v

Your Lp(a) Level

Lp(a) Risk Multiplier

Lifetime Risk Impact

📅 10-Year PREVENT Risk with Lp(a) Adjustment

Original 10-Year PREVENT

--%

→

Lp(a)-Modified 10-Year

--%

📆 30-Year PREVENT Risk with Lp(a) Adjustment

Original 30-Year PREVENT

--%

→

Lp(a)-Modified 30-Year

--%

10-Year Risk Category

30-Year Risk Category

💡 PREVENT Equation Context:

The AHA/ACC PREVENT equations (2024) are designed to predict total CVD risk including heart failure - a broader outcome than ASCVD alone. While PREVENT includes novel factors like eGFR, UACR, and HbA1c, it does not yet incorporate Lp(a). Given the strong evidence for Lp(a) as a causal risk factor for both atherosclerotic CVD and aortic stenosis, future PREVENT updates may include this important biomarker.

🔍 30-Year Risk Significance:

The 30-year risk estimate is particularly relevant for younger patients with elevated Lp(a), as their lifetime cumulative risk may be substantially higher than traditional 10-year estimates suggest. Enter your Lp(a) value and calculate PREVENT scores to see the impact.

⚠️ Demonstration Only: The PREVENT equations do not include Lp(a) in their current version. This projection illustrates how elevated Lp(a) may compound both short-term and long-term cardiovascular risk beyond what standard calculators predict.

📛 Key Lp(a) Facts

🧬 Genetic Determination:

Lp(a) levels are approximately 90% genetically determined and remain stable throughout life. Unlike LDL-C, diet and exercise have minimal impact on Lp(a) levels.

⚠️ Prevalence & Underdiagnosis:

Approximately 20% of the global population has elevated Lp(a). However, fewer than 1% of at-risk individuals have been tested, making it one of the most underdiagnosed cardiovascular risk factors.

⚠ Cardiovascular Impact:

Elevated Lp(a) is an independent risk factor for atherosclerotic CVD, aortic stenosis, and heart failure. Risk increases progressively with Lp(a) levels, with extreme levels (>180 mg/dL) conferring 3× baseline risk.

💊 Management Strategies:

Current management focuses on aggressive LDL-C lowering, with PCSK9 inhibitors providing modest (20-25%) Lp(a) reduction. Five novel therapies target Lp(a) directly — three ASO/siRNA agents in Phase 3 CVOTs (pelacarsen, olpasiran, lepodisiran achieving 80-94% reduction), one siRNA with Phase 2 complete (zerlasiran, 96% reduction), and one oral agent in Phase 2 (muvalaplin, up to 86% reduction). First CVOT results expected H1 2026 (Lp(a)HORIZON). If positive, FDA approval for pelacarsen could follow in H2 2026.

🔬 Emerging Lp(a)-Lowering Therapies

As of early 2026, no drug has been approved specifically for lowering Lp(a). Multiple agents are in Phase 3 CVOTs.

📅 Updated: Feb 2026

🚨

Lp(a)HORIZON Results Watch

The first-ever Lp(a) CVOT (Lp(a)HORIZON, pelacarsen, n=8,323, event-driven) was extended from 2025 to H1 2026 for event accrual — topline results are now IMMINENT. This will test the "Lp(a) hypothesis" — whether specific Lp(a) lowering reduces cardiovascular events. If positive: paradigm shift. Regulatory submissions planned H2 2026. OCEAN(a)-OUTCOMES (olpasiran, n=7,297) follows Dec 2026. ACCLAIM-Lp(a) (lepodisiran, up to 16,700 pts) is the largest trial and currently enrolling. Novartis has also initiated a combination trial of pelacarsen + inclisiran (NCT06813911).

IMMINENT

NCT04023552

Pelacarsen Phase 3 CVOT

Type: Antisense Oligonucleotide (ASO)
Developer: Novartis / Ionis
Route: SC injection, monthly (80 mg)

~80%

Lp(a) reduction (Phase 2)

Lp(a)HORIZON: 8,323 pts with established CVD + Lp(a) ≥70 mg/dL. Primary endpoint: expanded MACE. Topline results expected H1 2026. First CVOT to report on Lp(a) lowering.
Lp(a)FRONTIERS CAVS: Impact on calcified aortic valve stenosis progression (enrolling).

Olpasiran Phase 3 CVOT

Type: Small Interfering RNA (siRNA)
Developer: Amgen
Route: SC injection, every 12 weeks (75 mg)

~94%

Lp(a) reduction at 36 wks (Phase 2)

OCEAN(a)-OUTCOMES: 7,297 pts with ASCVD + Lp(a) ≥200 nmol/L. Primary endpoint: CHD death, MI, urgent revascularization. Completion est. Dec 2026.
Note: Phase 2 showed mild increase in hyperglycemia/new-onset DM — monitor in Phase 3.

Lepodisiran Phase 3 CVOT

Type: Long-duration siRNA (non-canonical)
Developer: Eli Lilly
Route: SC injection, every 24-52 weeks (400 mg)

~94%

Lp(a) reduction, sustained >90% at 360 days (ALPACA Phase 2, NEJM 2025)

ACCLAIM-Lp(a): Enrolling up to 16,700 pts (largest Lp(a) trial). ALPACA Phase 2 (ACC.25): 93.9% Lp(a) reduction at 180 days with single 400mg dose, sustained >90% at 360 days. Lp(a) ≥175 nmol/L. Includes both established ASCVD and high-risk primary prevention. Primary endpoint: MACE-4.
Unique: Single injection provides >90% reduction lasting >12 months.

Zerlasiran Phase 2 Complete

Type: siRNA (GalNAc-conjugated)
Developer: Silence Therapeutics
Route: SC injection, every 16 weeks (300 mg)

~96%

Lp(a) reduction at 36 wks (ALPACAR-360, JAMA 2024)

ALPACAR-360: 172 pts, Phase 2 complete. Reductions persisted at 60-week final visit. Phase 3 program on hold — seeking development partner.
Max reduction 90-98% with higher doses.

Muvalaplin Phase 2

Type: Small molecule inhibitor (apo(a)-ApoB binding)
Developer: Eli Lilly
Route: Oral, daily

63-86%

Lp(a) reduction (Phase 2, JAMA 2025)

Mechanism: Inhibits Lp(a) assembly by blocking apo(a)-ApoB100 binding. First oral Lp(a)-lowering agent. ALPACA Phase 2 (NCT05565742) recruitment complete.
Key advantage: Oral dosing could dramatically improve access.

📅 CVOT Results Timeline

H1 2026Lp(a)HORIZON (pelacarsen, n=8,323) — First Lp(a) CVOT to report (extended from 2025; event-driven) Dec 2026OCEAN(a)-OUTCOMES (olpasiran, n=7,297, Lp(a)≥200 nmol/L) 2027+ACCLAIM-Lp(a) (lepodisiran, enrolling up to 16,700 pts, Lp(a)≥175 nmol/L) TBDZerlasiran Phase 3 — on hold, seeking development partner

FDA requires evidence that Lp(a) lowering reduces CV events before approving any targeted therapy. These CVOTs will determine whether decades of genetic and epidemiologic evidence translate to clinical benefit.

📚 Key References: Nissen SE et al. NEJM 2025;392:1673-83 (Lepodisiran ALPACA) • Nicholls SJ et al. JAMA 2025;333:222-31 (Muvalaplin Phase 2) • Nissen SE et al. JAMA 2024;332:1992-2002 (Zerlasiran ALPACAR-360) • Cho L et al. Am Heart J 2025;287:1-9 (Lp(a)HORIZON design/rationale)

🧬 Lp(a) CVOT Outcomes Tracker Real-time trial status dashboard

These are the first-ever cardiovascular outcomes trials testing whether lowering Lp(a) reduces CV events. FDA requires CVOT evidence before approving any Lp(a)-targeted therapy. Positive results would represent a paradigm shift — validating decades of genetic/epidemiologic evidence and opening the door to treating ~20% of the global population with elevated Lp(a).

Trial	Drug	N	Lp(a) ↓	Primary Endpoint	Status	Results
Lp(a)HORIZON	Pelacarsen (ASO)	8,323	~80%	Expanded MACE (CV death, MI, stroke, urgent revasc)	Enrolled	H1 2026
OCEAN(a)-OUTCOMES	Olpasiran (siRNA)	7,297	~94%	CHD death, MI, urgent coronary revasc	Enrolled	Dec 2026
ACCLAIM-Lp(a)	Lepodisiran (siRNA)	16,700	~94%	MACE-4 (CV death, MI, stroke, urgent revasc)	Enrolling	2027+
ALPACAR-360	Zerlasiran (siRNA)	372	~96%	Safety + Lp(a) durability (Phase 2)	Complete	✅ 2024
MOVE-Lp(a)	Muvalaplin (oral)	TBD	63-86%	Phase 3 CVOT design pending	Phase 2	2027+

If Lp(a)HORIZON is POSITIVE:

FDA filing for pelacarsen expected H2 2026 (if CVOT positive)
ALPACA Phase 2 (lepodisiran): 93.9% Lp(a) reduction at 180d with 400mg dose (ACC.25, Nissen et al.)
ALPACAR-360 Phase 2 (zerlasiran): 96.4% reduction at 36wk; Phase 3 on hold pending partner
Muvalaplin Phase 2: up to 86% Lp(a) reduction (oral, daily); ALPACA trial completed
First approved Lp(a)-lowering drug (potential 2027)
Validates Lp(a) as therapeutic target
Cascade screening becomes medically actionable
Updates to CCS, ACC/AHA, ESC guidelines expected

If Lp(a)HORIZON is NEGATIVE/NEUTRAL:

Questions about ASO mechanism vs siRNA
OCEAN(a) and ACCLAIM still informative (different drug class)
80% reduction may be insufficient — siRNAs achieve 94%+
Debate: Is Lp(a) causal or just a marker?
Risk-enhancing factor status may not change

⚠️ Clinical Context: Unlike LDL where statin CVOTs were positive from the start (4S trial, 1994), Lp(a) has never had an outcomes trial. Genetic evidence is strong (Mendelian randomization studies show ~29% higher CVD risk per 50 nmol/L increase), but epidemiologic evidence is not equivalent to interventional proof. The Lp(a)HORIZON result will be one of the most important cardiology results of the decade. Current management: intensify other modifiable risk factors (LDL, BP, smoking, diabetes) when Lp(a) is elevated.

Sources: ClinicalTrials.gov NCT04023552, NCT05581303, NCT05900141 • Cho L et al. Am Heart J 2025 • Nissen SE et al. NEJM 2025 • Koschinsky ML et al. J Clin Lipidol 2024

📊 Therapy Response Estimator

Estimate post-treatment Lp(a) based on current level and expected drug efficacy (Phase 2 data).

Current Lp(a)

Unit

Therapy

👨‍👩‍👧‍👪 Family Cascade Screening Calculator

Calculate potential impact of screening first-degree relatives (~90% heritability):

# Living Parents

# Siblings

# Adult Children

📊 Lp(a) Risk Thresholds

Category	mg/dL	nmol/L	Risk Multiplier
Normal	<30	<75	1.0×
Borderline	30-49	75-124	1.2×
Elevated	50-79	125-199	1.5×
Very High	≥80	≥200	2.0×
Extreme	≥180	≥450	3.0×

⚠️ Unit Conversion Warning: Lp(a) conversion is assay-dependent (typically 2.0-2.5× factor). Verify with lab-specific conversion.

📓 Related References: 🧬 Lp(a) Science 💉 Lipid Science 🌎 Ethnic Variations 📋 Clinical Guidelines

📝 Developed by: Manjinder S., M.D., B.Sc. (Biochemistry) • Lp(a) research motivated by personal family history with elevated lipoprotein(a)

💉 Lp(a)-Lowering Therapy Tracker

Track Lp(a)-targeted therapy effectiveness. Labs auto-sync from the Medications & Labs tab. Baseline values are locked at first entry.

Therapy Agent

Therapy Start Date

Dose

Route

🧪 Monitoring Labs (synced from Labs tab)

Lab Value	Baseline	Current	% Change	Target
Lp(a)	--	--	--	<75 nmol/L
LDL-C	--	--	--	<2.0 mmol/L
ApoB	--	--	--	<0.8 g/L
Non-HDL-C	--	--	--	<2.6 mmol/L
hsCRP	--	--	--	<2.0 mg/L
AST	--	--	--	10-40 U/L
ALT	--	--	--	7-56 U/L
eGFR	--	--	--	>60 mL/min
Platelets	--	--	--	150-400
OxPL-apoB Research	--	--	--	Research only

📈 Lp(a) Trend

From calculation history

💉 Injection Tracker

Last Injection

Days Since

Next Due

🔬 OxPL-apoB (Research)

RESEARCH ONLY

OxPL-apoB

Baseline

% Change

Oxidized phospholipids on ApoB — OCEAN(a) trial secondary endpoint. Commercial assays not yet widely available. Ref: Rosenson RS et al. JACC 2024.

Note: No Lp(a)-lowering therapy is FDA/Health Canada approved as of March 2026. Lp(a)HORIZON (pelacarsen) topline results are imminent. Pelacarsen (Lp(a)HORIZON), Olpasiran (OCEAN(a)), and Lepodisiran Phase 3 trials are ongoing. This tracker is for research and clinical trial monitoring purposes.

📋 Patient Summary Dashboard

Clinical overview — auto-populated from all tabs

No Patient Data

Generate Patient ID in Patient tab

📊 Cardiovascular Risk Scores

No risk scores calculated

Complete Patient + Labs tabs first

🩸 Lipid Panel

No lipid data

🧪 Key Biomarkers

No biomarker data

💊 Active Medications

No medications recorded

📓 Assessment History

View, compare, and manage saved assessments

Search Patient ID

Filter by Date

Date Range End

Filter by Risk Level

Sort By

Items Per Page

Total Assessments

Avg Risk Score

Risk Trend

Last Assessment

	Date/Time	Patient ID	FRS Base / Lp(a)	QRISK3	QRISK4	PREVENT 10y	PREVENT 30y	Lp(a)	BRI	Actions
📋 No saved assessments yet. Complete an assessment and click "Save Assessment" to build your history.

Showing 0-0 of 0 entries

Page 1 of 1

Go to:

📊 Table Columns: FRS = Framingham Risk Score (Base% / Lp(a)-adjusted%) | QRISK3/4 = UK-validated risk | PREVENT = AHA 2024 (10-year and 30-year) | Lp(a) & BRI highlighted

📈 Risk Trend Analysis

Filter risk trend by patient ID

📈

No Trend Data Yet

Complete and save assessments over time to see your cardiovascular risk trend. Each saved assessment adds a data point showing Framingham, QRISK3, and PREVENT scores.

🧪 Biomarker Trends Over Time

📈 Save 2+ assessments to see biomarker trends over time

💊 Medication Timeline

💊 Save assessments with medications to see adherence timeline

📊 Patient vs Practice Population

Compares current patient's risk scores and labs against all saved assessments in this practice

🎯 Risk Score Percentile

Save 3+ patient assessments to enable population comparison

🧪 Biomarker Percentile

Save 3+ patient assessments to enable population comparison

💾 Session History Viewer

Total Sessions

Unique Patients

Total Calculations

Avg Session

Session ID	Start Time	Patient ID	Calculations	Duration	Actions
Click "Load Sessions" to view session history

⏯ EventBus Event Replay

Progress

Current

Total

Status

Idle

📜 Event Queue

Load events to see queue

✅ Executed Events

No events executed yet

💡 Event Replay: Replay recorded EventBus events to debug workflows, reproduce issues, or demonstrate features. Events are re-emitted through the EventBus in sequence with optional delays for visibility.

🔒 Data Persistence & Backup

Checking...

File System Backup

Not Configured

OPFS Storage

Checking...

Last Auto-Save

Never

Last Backup

Never

Backup Count

🛡 Data Protection Features

API Status: OPFS: checking... FileSystem: checking... IndexedDB: checking...

File System Access: Save backups to a folder that survives browser data clearing (Chrome/Edge)
Origin Private File System: Browser-managed persistent storage with higher retention
Auto-Backup: Automatic backups every 15 minutes to multiple locations
Download Backups: Manual download backup as a failsafe
Storage Pressure Detection: Automatic emergency backup when storage is low

📀 Version History & Rollback

Saved Versions

Current Version

Never

Last Saved

Version	Date/Time	Description	Fields	Actions
Click "Load History" to view saved versions

⚠️ Rollback Warning: Rolling back will overwrite current data. A backup of the current state will be created automatically before rollback.

📁 Import / Export Data

Import from files, paste lab reports, batch import patients, or capture images

📤 Import Data

📁

Drag & Drop Files Here
or click to browse

Supported: JSON, CSV, PDF (Text + Scanned with OCR), Images (JPG/PNG with OCR), Excel (.xlsx), Text Files (.txt) | Max 24,000 chars

👥 Batch Patient Import

Import multiple patients from CSV or Excel. Each patient will receive a unique CVD-YYYYMMDD-XXXXXXXXXXXXXX ID.

📋 Required CSV/Excel Columns:

                        name, age, sex, sbp, dbp, tc, ldl, hdl, triglycerides, smoking, diabetes

                        Optional: creatinine, hba1c, lipoa, waist, hip, weight, height, egfr

Select CSV or Excel File

🏥 FHIR R4 Healthcare Import

Import patient data from HL7 FHIR R4 Bundle resources. Supports Patient, Observation, Condition, and MedicationStatement resources.

✅ Supported FHIR Resources:

Patient: Demographics, identifiers

Observation: Labs, vitals (LOINC coded)

Condition: Diagnoses (SNOMED coded)

Medication: Current medications

FHIR R4 Bundle JSON

📚 Batch FHIR Import (Multiple Bundles)

Select multiple FHIR Bundle files to import sequentially. Each bundle will be processed and conflicts will be resolved.

📋 Paste Lab Report

Copy and paste a lab report directly below. The system will automatically extract patient info, lab values, and populate fields.

🔍 Awaiting paste...

0 / 24,000 chars

📤 Export Data

Export Format

Patient Data Lab Results Medications Calculations Cardiorenal Assessment

🔑 Encrypt export with AES-256-GCM

💾 Export Templates

No saved templates.

🔑 Encrypted Export/Import

Securely transfer patient data between systems with AES-256-GCM encryption and digital signature verification.

Security: Files are encrypted with AES-256-GCM and signed for authenticity verification.
Only CVD Toolkit can decrypt and verify these packages.

⚙ Settings

Configure application preferences

🎨 Appearancev

Theme

Sunrise: :00 Sunset: :00

Text Size

📏 Default Unitsv

🌍 Regional Presets

Click a preset to apply regional default units for all fields

📋 Lab Report Parsing Mode

Controls how the toolkit interprets units when parsing pasted lab reports. Auto-detect analyzes the report format to determine the correct units.

Parsing Unit Mode Auto-detect examines unit labels in the lab report

Output Conversion What units to display after parsing

Current Mode: Auto-Detect (parsing will analyze unit labels)

Measurement System

Lipid Units

⚙ Detailed Unit Settings

Glucose

HbA1c

Creatinine

Hemoglobin

Liver Enzymes

Free T4

BNP/NT-proBNP

Vitamins (B12, D)

This will update all unit selectors without changing entered values

🌍 Regional Unit Presets

Apply standard unit conventions for your region with one click

💾 Unit Preference Persistence

Your unit preferences are automatically saved and restored on page load

📊 Differential Unit Tracking

Original entry units are tracked for differential reporting.

⚙ Behaviorv

Auto-save data Auto-calculate on input Show explanations Enable keyboard shortcuts Confirm before clearing

🔑 Data Managementv

🔒 Encryption Key Backup

If browser data is cleared, encrypted patient records become permanently unreadable without a key backup. Export your key now.

Key Status
Checking...

Fingerprint
--

Last Backup
Never

🛡 Encryption Coverage Dashboard

Patients (Cache) ✅

Calculations (Cache) ✅

History ✅

Medications (DB) ✅

Lab Results (DB) ✅

localStorage PHI ✅

🗄 Database Consolidation

Consolidate 14 separate databases into 2 optimized stores with full AES-256-GCM encryption.

Status: Checking...

Proxy: --

Modules: --

Encrypt: --

💾 Session Recovery

If you previously chose "Decide Later" when prompted to restore a session, you can manually restore it here.

Checking for saved sessions...

📃 Version History & Rollback

The toolkit automatically saves versions of your data. You can view history and restore previous versions if needed.

Versions saved: 0 Last save: --

Note: Up to 50 versions are stored locally. Older versions are automatically removed.

🧠 Memory Management

Monitor and optimize memory usage for better performance.

Memory Used:
--

Memory Health:
Good

⚡ Performance & Accelerationv

💻 System Status

GPU Acceleration:
Checking...

WebWorker Pool:
Checking...

OCR Engine:
Checking...

PDF Generator:
Checking...

📊 Performance Metrics

GPU Calculations: 0
Worker Calculations: 0
CPU Calculations: 0
Total Time: 0ms

🧩 Advanced Modules & Integrationsv

Configure advanced external modules for research, compliance, EMR integration, and reporting.

📊 Research Data Exporter

Export anonymized patient data for research in SPSS, R, SAS, and Stata formats. HIPAA Safe Harbor compliant de-identification.

Ready

🏥 EMR Integration

Connect to external EMR systems via HL7 v2/v3, FHIR R4, CCD, and C-CDA standards. Fetch patient data and push calculation results.

Not Configured

FHIR Endpoint URL

Authentication

🔒 Compliance Monitor

HIPAA audit logging for PHI access, data exports, and security events. Generate compliance reports for audits.

Active

Events Logged

Session ID

Last Event

📝 Report Templates

Professional report layouts for clinical summaries, detailed assessments, lipid panels, and Lp(a) cascade screening.

4 Templates

Select Template

🔍 Dependency Visualizer

Interactive module dependency graph for debugging and system understanding. Visualize how modules connect and communicate.

-- Modules

📊 Advanced Module Health Summary

                                Loading module status...
                            

💾 Storage Information

Local Storage: Calculating...

IndexedDB: Available

Encryption: AES-256-GCM (when enabled)

📝 Developed by: Manjinder S., M.D., B.Sc. (Biochemistry)

🧪 Diagnostics & Testingv

Run built-in diagnostic tests to verify toolkit integrity, field sync, and calculation accuracy.

Tip: Open browser DevTools Console (F12) for detailed diagnostic output. Run MasterTestSuite.runAll() for the full 805-test suite.

💜

Future Research & Development

Advancing cardiovascular risk prediction through innovative technology, genetic research, and AI-powered clinical decision support

🔬 Research-Driven

📊 Evidence-Based

🤖 AI-Powered

🧬

Lipoprotein(a) Research Initiative

Our flagship research priority

⚠️ The Problem

20% of the global population has elevated Lp(a)
90% genetically determined - cannot be modified by lifestyle
<1% of at-risk patients have been tested
Current therapies reduce Lp(a) by only 20-30%
Family cascade screening is rarely performed

✅ Our Research Goals

Universal screening tool for primary care
Cascade screening calculator for family impact
AI-powered Lp(a) level prediction from other biomarkers
Treatment response modeling for emerging therapies
Registry contribution for outcomes research

💊 Emerging Lp(a)-Lowering Therapies Pipeline

💊

Pelacarsen

~80% Lp(a) reduction

Lp(a)HORIZON: 8,323 pts

Phase 3 results H1 2026

💊

Olpasiran

~94% Lp(a) reduction

OCEAN(a): 7,200+ pts

Phase 3 results 2026-27

💊

Lepodisiran

~94% Lp(a) reduction

ACCLAIM-Lp(a): 12,500 pts

Phase 3 enrolling

💊

Zerlasiran

~90% Lp(a) reduction

ALPACAR-360: 172 pts

Phase 2 complete

These novel therapies may establish first-ever Lp(a)-specific treatments — Pelacarsen CVOT results expected H1 2026

🎯 Research Priorities 2025-2027

PRIORITY 1

🧬

Polygenic Risk Score Integration

Integrate CAD-PRS, AF-PRS, and stroke-PRS for comprehensive genetic risk stratification beyond single-gene disorders.

Genomics Risk Stratification

BACKUP

💾

💾 Data Backup Manager

Protect your patient data by configuring a backup location. Backups are encrypted and can be restored on any device.

⚠️ No backup location configured. Choose a backup destination to protect your data.

⚙️ Backup Settings

Backup Destination

Backup Filename

Include system learning data (Cortex patterns)

🔄 Restore from Backup

📃 Backup History

No backups created yet. Configure a backup location to get started.

Research Notice: This toolkit is an educational and research support tool. It is not intended to replace professional medical judgment. All clinical decisions should be made by qualified healthcare providers.

🔬

Research Tools & Analysis

Advanced research capabilities for cardiovascular outcomes analysis

📊

Cohort Analysis

Functional

Loading cohort data...

💾

Research Data Export

HIPAA Safe Harbor

Export de-identified patient data for statistical analysis. All PHI removed per HIPAA Safe Harbor method.

Ready to export

🧬

Lp(a) Therapeutic Pipeline & Meta-Analysis

Live Tracking

Agent	Mechanism	Lp(a) Reduction	Trial (Phase)	Status
Pelacarsen	ASO (antisense)	~80%	Lp(a)HORIZON (Ph3 CVOT, n=8,323)	Results IMMINENT (H1 2026)
Olpasiran	siRNA	~94%	OCEAN(a)-OUTCOMES (Ph3, n=7,297)	Est. Dec 2026
Lepodisiran	siRNA	~96%	ACCLAIM-Lp(a) (Ph3, up to 16,700)	Enrolling
Zerlasiran	siRNA	~96%	Phase 2 complete	Awaiting Ph3
Muvalaplin	Oral small molecule	~86%	KRAKEN (Ph2b), CAPSIZE (Ph2b renal)	Enrolling

📈

Calculator Comparison

Live

Run calculators to see comparison

⚡

Live Calculation Feed

EventBus

Waiting for calculations...

Calculations

Avg Risk

Unique Calcs

📐

Statistical Analysis Engine

📊 Number Needed to Treat (NNT)

Baseline Risk (%)

Relative Risk Reduction (%)

📏 Confidence Intervals

95% CI for current calculator risk estimates

Run calculators to generate CIs

📈 Cohort Descriptive Stats

Summary statistics from saved patient assessments

Save assessments to generate stats

🔍 Calculator Agreement

Bland-Altman analysis between calculator pairs

Run ≥2 calculators to compare

⚡

Programmatic Calculator API

v30.74.313+

DOM-free calculator APIs for batch processing, cohort analysis, and programmatic testing. All accept a params object and return a pure result — no DOM reads or writes.

Framingham (30-79y)
calculateFraminghamFromParams({
  age, sex, tc, hdl, sbp,
  treated, smoker, diabetic
})→ {risk, base, modified, heartAge}

QRISK3/4 (25-84y)
calculateQRISK3FromParams({
  age, sex, bmi, sbp,
  tcHdlRatio, townsend, ...
})→ {qrisk3, qrisk4, heartAge3}

PREVENT (30-79y)
calculatePREVENTFromParams({
  age, sex, sbp, tc, hdl,
  bmi, egfr, diabetes, ...
})→ {risk10yr, risk30yr, ...}

SCORE2/OP/DM (40-89y)
calculateSCORE2FromParams({
  age, sex, tc, hdl, sbp,
  smoking, region, ...
})→ {score, algorithm, category}

SCORE2 Family (40-89y)
calculateSCORE2FromParams({
  age, sex, tc, hdl, sbp,
  smoking, region, diabetes
})→ {score, algorithm, category}

Reynolds (45-80y)
calculateReynoldsFromParams({
  age, sex, sbp, tc, hdl,
  hscrp, smoking, ...
})→ {risk, category, model}

🧪

Parameter Sandbox

Interactive

Tweak parameters and see how all calculators respond simultaneously.

Age55

SBP140

TC (mg/dL)220

HDL50

Smoker Diabetes Male Female

Research Notice: These tools are intended for research purposes only. All clinical decisions should be validated by qualified healthcare professionals.

🏥

Practice Dashboard

Aggregate analytics across all saved patient assessments — risk distributions, treatment gaps, screening rates & research export

Total Patients

High Risk (≥20%)

Intermediate (10-20%)

Low Risk (<10%)

Lp(a) Screened

Mean Age

LDL at Target

On Statin Rx

📊 Risk Score Distribution

👥 Age & Sex Distribution

🚨 Treatment Gap Analysis

🧬 Lp(a) Screening Coverage

🧪 Practice Biomarker Summary

Biomarker	n	Mean	Median	SD	Min	Max	% at Target

🏅 Quality Improvement Report

Generate a multi-page PDF with HEDIS-style quality measures, embedded charts, treatment gap analysis, and Lp(a) cascade screening metrics.

CVD-01: Lp(a) Screening Rate

CVD-02: LDL at Target (High Risk)

CVD-03: BP at Target (<140/90)

CVD-04: Statin Rx (High Risk)

CVD-05: HbA1c Monitoring

CVD-06: eGFR Screening

🧬 Cascade Screening Coverage

Family Members

Tested

Elevated Lp(a)

Overdue (>90d)

📋 STROBE Reporting Checklist

Auto-populate STROBE observational study checklist items from your practice data. Pre-fills study design, participant count, variables, data sources, and key results.

📊 QI Measure Benchmarking

Compare your practice's quality measures against peer averages. Upload anonymized QI snapshots exported from other practices, or use national benchmarks.

📨 Cascade Screening Referral Letter

Auto-generate referral letters for family members identified through cascade screening. Pre-fills proband Lp(a) level, inheritance probability, recommended assay, and clinical rationale.

Referring Physician Name

Clinic Name

📓 Reproducible Analysis Notebooks

Export complete, ready-to-run analysis notebooks with publication-quality tables, figures, statistical tests, and data dictionary. Open directly in RStudio or JupyterLab.

📓

R Markdown (.Rmd)

knitr + kableExtra + ggplot2

Generates PDF/HTML with YAML header, code chunks for demographics table, risk distribution, Kaplan-Meier curves, treatment gap forest plot, Lp(a) dose-response analysis, and correlation matrix.

📔

Jupyter Notebook (.ipynb)

pandas + seaborn + scipy

Python notebook with demographic summary, risk score boxplots, treatment gap analysis, Lp(a) distribution, logistic regression setup, and AUC comparison across calculators.

🔬 Research Data Export

Export de-identified practice data with companion data dictionary for statistical analysis.

📊

Batch Processing & Population Health

Process multiple patient records simultaneously for population health screening

📁 Batch Import

Upload a CSV or Excel file with patient data for batch risk calculation.

📤

Drop CSV/Excel file here or click to browse

Supports .csv, .xlsx, .xls formats

⚙️ Batch Calculation Options

Framingham Risk Score QRISK3 SCORE2 PREVENT Reynolds Risk Score

💡 Tip: Include an hscrp column in your CSV for accurate Reynolds results. Without it, hs-CRP defaults to 1.0 mg/L, which is Reynolds' key differentiator from Framingham.

Lp(a) Risk Modifier

No file loaded GPU: checking...

❤️ Support This Project

Your donation keeps the CVD Toolkit free and helps fund server costs, AI development, and clinical updates.

☕ Buy Us a Coffee on Ko-fi

❓ Help & Clinical References

Quick start guide, shortcuts, and citations

❤️ Support This Projectv

Scan QR or click to donate

☕ Support Further Development

Your donation helps support:

💻 Web Server Hosting: Keeping the toolkit accessible online
🤖 AI Subscription: Continued development using AI assistance
🔬 New Features: Quantum computing integration, ML risk prediction
📚 Clinical Updates: Keeping guidelines current (CCS, AHA/ACC, ESC)
📱 Mobile Optimization: Enhanced experience on all devices

☕ Buy Us a Coffee

Every contribution helps keep this project alive. Thank you! 🙏

🤖

Developed with AI Collaboration NEW

This comprehensive CVD Risk Toolkit was built through the collaborative efforts of artificial intelligence, clinical expertise, and dedicated mentorship. The result: a medical-grade tool designed to enhance patient care.

💡 If AI can help build tools that improve healthcare delivery, imagine what it could do for your daily clinical documentation. Free yourself from administrative burden and spend more time with your patients.

📝 Explore AI-Powered Clinical Documentation →

"The future of medicine is not AI replacing clinicians, but clinicians empowered by AI to deliver exceptional care."

🧪 Master Test Suite v30.74.265 (2341+ tests)v

Run comprehensive diagnostics using MasterTestSuite v30.74.73 - same tests as Ctrl+Shift+T with 15-section report and automatic GitHub issue creation.

📋 Unified Report Sections (15):

1. Module Health (55) 2. CVDTestSuite 3. ValidationSuite 4. Stress Tests 5. Predictive Maint. 6. Module Comm. 7. Process Tree 8. Performance 9. Memory Usage 10. Storage Status 11. Browser Features 12. Console Summary 13. Error Analysis 14. Edge Cases 15. Gold Standard (505)

📊 Comprehensive Test Suite v30.74.73:
• Gold Standard: 505 clinically validated cases
• E2E Scenarios: 70+ end-to-end tests
• Deep Trace: Root cause analysis
• Export: JSON, CSV, PDF for audits

15. Calc Accuracy

📤 GitHub Auto-Upload: Reports are automatically uploaded to wazscience/debugging-support as issues with labels based on severity.

📊 Debug Results

📡 EventBus Performance Monitor

Emitted

Handled

Dropped

Errors

Subscriptions

100%

Success Rate

📊 Top Events

No events yet

⏱ Recent Activity

No recent activity

📝 Registered Events (0)

🧪 EventBus Unit Tests

Click "Run Tests" to execute EventBus unit tests

📊 System Monitoring Dashboard v30.74.73 v

Real-time monitoring of EventBus, Communication Hub, and module dependencies.

📡 EventBus Health HEALTHY

Events Emitted

Events Handled

Dropped

Errors

Version: -- | Subscribers: 0

🔗 CommHub Statistics ACTIVE

Direct Channels

Channel Hits

Batched Events

Avg Latency

🏗 Dependency Health VALID

Modules

Dep Links

Circular

Warnings

📦 Module Status

Inline

Hybrid

External

Total: 113 modules registered

📊 Active Direct Channels

Channel	Messages	Avg Latency	Status
Click "Refresh Dashboard" to load channel data

🏗 Dependency Graph

💡 Optimization Recommendations

Click "Optimize Channels" to analyze and get recommendations.

🚀 Quick Start Guidev

Patient Demographics: Enter age, sex, height, weight, waist, hip, BP
Medications & Labs: Input medications and all lab panels
Framingham: Calculate 10-year CHD risk with Lp(a) modifier
QRISK3/4: UK-validated CVD risk with 14+ conditions
Combined Results: Compare all scores, calculate PREVENT
Lp(a) Assessment: Evaluate risk and cascade screening
Save/Export: Save assessments and export reports

⌨ Keyboard Shortcutsv

Shortcut	Action
`Alt` + `1-9, 0`	Switch tabs 1-10
`Ctrl` + `S`	Save assessment
`Ctrl` + `N`	New patient / Clear
`Ctrl` + `P`	Print report
`Ctrl` + `E`	Export data

⚖️ Legal Disclaimerv

⚕️ Clinical Use Disclaimer Status

Status: Checking...

Version: 3.0.0

Accepted: --

Expires: --

You must accept the Clinical Use Disclaimer to use this application. Consent is valid for 30 days and will require re-acceptance after expiration.

🧠 ML Data Collection Consent

Status:

Consented:

Optional anonymous data collection to improve cardiovascular risk prediction models. All data is de-identified and encrypted with AES-256-GCM.

⚠️ Important Notice

This application is intended for use by qualified healthcare professionals only. All risk calculations should be verified independently using official calculators for clinical decision-making.

📜 Clinical Use Disclaimer (v3.0.0)

1. EDUCATIONAL AND INFORMATIONAL PURPOSES ONLY

This CVD Risk Assessment Application ("Application") is provided solely for educational and informational purposes. It is designed to assist qualified healthcare professionals in cardiovascular risk assessment but is not intended to replace professional medical judgment, diagnosis, or treatment.

2. NOT A MEDICAL DEVICE

This Application is NOT a licensed, registered, or approved medical device under any jurisdiction including Health Canada, the FDA (USA), or CE marking (EU). The risk scores and recommendations generated should be considered supplementary information only and must be independently verified.

3. PROFESSIONAL RESPONSIBILITY

Healthcare professionals using this Application bear sole and complete responsibility for all clinical decisions made in connection with patient care. This includes:

Verification of all calculations using official validated calculators
Consideration of all relevant patient factors not captured by risk algorithms
Exercise of independent clinical judgment
Adherence to current clinical guidelines and institutional policies

4. ALGORITHM LIMITATIONS

The risk algorithms implemented (Framingham, QRISK3, QRISK4, PREVENT) were developed and validated in specific populations. Accuracy may vary when applied to:

Populations outside original derivation cohorts
Patients with conditions not included in model development
Clinical scenarios with multiple comorbidities
Extreme values outside typical ranges

5. NO WARRANTY

THIS APPLICATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. THE CREATORS EXPRESSLY DISCLAIM ALL WARRANTIES INCLUDING MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT.

6. LIMITATION OF LIABILITY

THE CREATORS SHALL NOT BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES ARISING FROM USE OF THIS APPLICATION, INCLUDING BUT NOT LIMITED TO PATIENT HARM, TREATMENT ERRORS, OR ADVERSE CLINICAL OUTCOMES.

🔑 Data Privacy & Security Notice (v3.0.1)

LOCAL PROCESSING: All calculations are performed locally within your browser. No patient data is transmitted to external servers by default.

DATA STORAGE: The Application may temporarily store data in browser local storage for session management. This data remains on your device.

REGIONAL COMPLIANCE:

🇨🇦 Canada (PIPEDA): Users must ensure compliance with provincial health information legislation
🇺🇸 USA (HIPAA): PHI handling is your responsibility as a covered entity
🇪🇺 EU (GDPR): You are the data controller for any patient data entered

ENCRYPTION: When enabled, data is encrypted using AES-256-GCM. However, no electronic system is 100% secure.

RECOMMENDATION: Anonymize or de-identify patient data before entry. Do not enter full patient names, health card numbers, or other direct identifiers.

💊 Medication Recommendations Disclaimer (v1.0.0)

GUIDANCE ONLY: Medication recommendations are based on CCS 2021 Guidelines for Dyslipidemia and serve as general guidance only.

PRESCRIBING RESPONSIBILITY: The healthcare professional bears full responsibility for all prescribing decisions including:

Reviewing patient allergies and contraindications
Checking drug-drug interactions
Verifying dosing appropriateness
Monitoring for adverse effects

⚠️ WARNING: This Application does NOT independently verify drug allergies, contraindications, or interactions.

💉 PCSK9 Inhibitor Eligibility Disclaimer (v1.0.0)

BC PHARMACARE CRITERIA: PCSK9 eligibility assessments are based on publicly available BC PharmaCare Special Authority criteria. These may not reflect current criteria or individual case variations.

NO GUARANTEE: Eligibility assessments do not guarantee actual coverage approval. Always verify directly with the relevant provincial formulary.

CONSULT OFFICIAL SOURCES: For actual Special Authority applications, consult the official BC PharmaCare forms and current criteria at gov.bc.ca/pharmacare

🔒 Privacy & Data Collectionv

🔐 Data Security

All ML training data is encrypted with AES-256-GCM before transmission. Encryption keys are derived using PBKDF2 with 100,000 iterations. Your consent preferences are stored locally on your device.

📋 What Data is Collected

Anonymized calculation inputs - Age ranges (not exact), rounded lab values
Risk score outputs - Framingham, QRISK3, PREVENT scores
Biomarker patterns - Lp(a), ApoB, eGFR distributions
Usage statistics - Which calculators are used most

⛔ What is NEVER Collected

Patient names, IDs, or any identifiers
Exact dates of birth or precise ages
Geographic location or addresses
Any information that could identify individuals

🎯 How Data is Used

Train ML models for better CVD prediction
Research novel biomarker correlations
Validate calculator algorithms
Improve future toolkit versions

💡 Your Control: You must explicitly consent before any data is collected. You can revoke consent at any time, and all preferences are stored locally on your device. No data is ever collected without your permission.

📱 Using the Toolkit Offlinev

📱 Install as App (Progressive Web App)

This toolkit can be installed on your device and used offline without an internet connection. All calculations work locally - no data is ever sent to servers.

🖥 Desktop (Chrome, Edge, Firefox)

Look for the install icon (+ or 📤) in the browser address bar
Click it and select "Install" or "Add to Home Screen"
The toolkit will open as a standalone app window
It will now be available in your Start Menu / Applications folder

📱 iPhone / iPad (Safari)

Open the toolkit in Safari (required for iOS)
Tap the Share button (square with arrow pointing up)
Scroll down and tap "Add to Home Screen"
Tap "Add" in the top right corner
The toolkit will appear as an app icon on your home screen

📱 Android (Chrome)

Open the toolkit in Chrome
Tap the three-dot menu (⋮) in the top right
Tap "Install app" or "Add to Home Screen"
Confirm by tapping "Install"
The toolkit will appear in your app drawer

📱 How Offline Mode Works

First Visit: The toolkit downloads and caches all necessary files
Subsequent Visits: Works entirely from cache - no internet required
Data Storage: Patient data is stored encrypted in your browser's IndexedDB
Auto-Save: Changes are automatically saved every 30 seconds
Updates: When online, the toolkit checks for updates automatically

⚠️ Important Notes:

Clearing browser data will delete saved patient assessments
The offline indicator (📱) appears in the header when disconnected
Export your data regularly as a backup (JSON format)
Chart visualizations require Chart.js to load on first use

📶

Offline Status

Checking...

📦 PWA Assets Download

Download icons and manifest for self-hosting or server deployment.

Icon sizes included: 72, 96, 128, 144, 152, 192, 384, 512px

📛 Clinical References (100+ Citations)v

❤ CVD Risk Assessment Calculators

Framingham (Original): D'Agostino RB et al. General cardiovascular risk profile for use in primary care. Circulation 2008;117:743-53. DOI: 10.1161/CIRCULATIONAHA.107.699579
QRISK3: Hippisley-Cox J et al. Development and validation of QRISK3. BMJ 2017;357:j2099. DOI: 10.1136/bmj.j2099
QRISK3 Source Code: ClinRisk Ltd. Official QRISK3-2017 C implementation (GNU LGPL v3). qrisk.org/src.php - Used for v30.74.73 calibration
QRISK4: Hippisley-Cox J et al. QRISK4: An updated algorithm. BMJ 2024. qrisk.org/QRISK4
PREVENT Equations: Khan SS et al. Development and validation of the AHA PREVENT equations. Circulation 2024;149:430-449. DOI: 10.1161/CIRCULATIONAHA.123.067626 — v30.74.73: Unit harmonization (mmol/L). v30.74.292: Centering verified (age 55, non-HDL-C 3.5, HDL-C 1.3, SBP 130). Coefficients from preventr v0.11.0. Models use age-scale hazard with Fine-Gray competing risk.
SCORE2: SCORE2 Working Group & ESC CRC. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of CVD in Europe. Eur Heart J 2021;42(25):2439-2454. DOI: 10.1093/eurheartj/ehab309 - v30.74.73: Calibrated to published anchor (M50: Low=5.9%, VH=14.0%)
SCORE2-OP: SCORE2-OP Working Group & ESC CRC. SCORE2-OP risk prediction algorithms: estimating incident CVD event risk in older persons in four geographical risk regions. Eur Heart J 2021;42(25):2455-2467. DOI: 10.1093/eurheartj/ehab312 - v30.74.73: Published Table 2 coefficients with age interactions, calibrated to Figure S9 (M75: Low=16%, VH=37%)
ESC 2021 CVD Prevention: Visseren FLJ et al. 2021 ESC Guidelines on CVD prevention in clinical practice. Eur Heart J 2021;42(34):3227-3337. DOI: 10.1093/eurheartj/ehab484 - Age-dependent risk thresholds for SCORE2/SCORE2-OP
preventr R Package: Mayer M. preventr: An Implementation of the PREVENT and Pooled Cohort Equations. R package version 0.11.0, 2025. CRAN: preventr — v30.74.292: Coefficient source (100 coefficient sets validated)
SCORE2-Diabetes: SCORE2-Diabetes Working Group. CVD risk estimation for persons with Type 2 diabetes. Eur Heart J 2023;44:2544-2556. DOI: 10.1093/eurheartj/ehad260
Lifetime CVD Risk: Berry JD et al. Lifetime risks of CVD. N Engl J Med 2012;366:321-329. DOI: 10.1056/NEJMoa1012848
BP Variability & CVD: Stevens SL et al. Blood pressure variability and CVD. BMJ 2016;354:i4098. DOI: 10.1136/bmj.i4098
Reynolds Risk Score: Ridker PM et al. Circulation 2007;115:450-8. DOI: 10.1161/CIRCULATIONAHA.106.659482
Pooled Cohort Equations: Goff DC et al. ACC/AHA guidelines. Circulation 2014;129:S49-73. DOI: 10.1161/01.cir.0000437741.48606.98

✅ v30.74.73 Calibration Notes: QRISK3 implementation uses official ClinRisk fractional polynomial coefficients with sex-specific transforms (female: age^-2, age; male: age^-1, age³), exact centering constants from derivation cohort, and complete age interaction terms. SCORE2 calibrated against published ESC 2021 example (M50 smoker SBP140 TC5.5 HDL1.3: Low=5.9%, VH=14.0%). SCORE2-OP calibrated against published Figure S9 anchor (M75 smoker SBP150 nonHDL4.5: Low=16%, VH=37%) with CONOR-derived age×risk-factor interaction terms from Table 2. PREVENT unit harmonization ensures mmol/L conversion for lipid coefficients. PREVENT centering verified against Khan SS et al. Table 2: age centered at 55, non-HDL-C 3.5 mmol/L, HDL-C 1.3 mmol/L, SBP 130 mmHg. Coefficients sourced from preventr R package v0.11.0 (Mayer M, 2025; CRAN). GOLD-02 (M55 smoker) produces exact match (10.3% CVD 10yr) confirming coefficient+centering accuracy at reference age.

🧬 Lipoprotein(a) Science

EAS Consensus Statement: Kronenberg F et al. Lipoprotein(a) in atherosclerotic CVD and aortic stenosis. Eur Heart J 2022;43:3925-46. DOI: 10.1093/eurheartj/ehac361
Lp(a) Risk Enhancement: Kamstrup PR et al. Extreme Lp(a) levels and risk of MI. JAMA 2009;301:2331-9. DOI: 10.1001/jama.2009.801
Genetic Causality: Burgess S et al. Lp(a) and CHD - Mendelian randomization. J Am Coll Cardiol 2018;72:1959-70. DOI: 10.1016/j.jacc.2018.07.084
PCSK9 + Lp(a): O'Donoghue ML et al. ODYSSEY OUTCOMES. Circulation 2019;139:1483-92. DOI: 10.1161/CIRCULATIONAHA.118.037184
Cascade Screening: Tsimikas S et al. Role of Lp(a) in cardiovascular disease. J Am Coll Cardiol 2021;78:634-49. DOI: 10.1016/j.jacc.2021.06.011
Pelacarsen Phase 2: Tsimikas S et al. Lipoprotein(a) reduction in persons with CVD. N Engl J Med 2020;382:244-255. DOI: 10.1056/NEJMoa1905239
Olpasiran Phase 2 (OCEAN(a)-DOSE): O'Donoghue ML et al. Small interfering RNA to lower Lp(a) in CVD. N Engl J Med 2022;387:2030-2041. DOI: 10.1056/NEJMoa2211023
Lepodisiran Phase 1: Nissen SE et al. Single ascending-dose study of a short interfering RNA targeting Lp(a). JAMA 2023;330:2075-2083. DOI: 10.1001/jama.2023.21437
Zerlasiran Phase 2 (ALPACAR-360): Nissen SE et al. Zerlasiran for Lp(a) lowering. JAMA 2024;332:1992-2002. DOI: 10.1001/jama.2024.21957
Muvalaplin Phase 2: Nicholls SJ et al. Oral small molecule Lp(a)-lowering. JAMA 2025;333:222-231. DOI: 10.1001/jama.2024.24017
Ethnic Variation: Enkhmaa B et al. Lipoprotein(a) and ethnicity. Atherosclerosis 2016;249:44-51. DOI: 10.1016/j.atherosclerosis.2016.03.031
Aortic Stenosis Risk: Arsenault BJ et al. Lp(a) levels and aortic valve calcification. J Am Coll Cardiol 2014;63:1724-30. DOI: 10.1016/j.jacc.2013.12.046

💉 Lipid & Apolipoprotein Science

ApoB Superiority: Sniderman AD et al. Apolipoprotein B vs LDL cholesterol. Lancet 2022;399:2173-84. DOI: 10.1016/S0140-6736(22)00624-0
Non-HDL Cholesterol: Brunner FJ et al. Residual risk after LDL lowering. Circulation 2019;140:305-14. DOI: 10.1161/CIRCULATIONAHA.118.039115
TRL Remnants: Varbo A et al. Remnant cholesterol and ischemic heart disease. JAMA 2013;309:2084-91. DOI: 10.1001/jama.2013.5679
TC/HDL Ratio: Gaziano JM et al. Fasting triglycerides, HDL, and risk of MI. Circulation 1997;96:2520-25. DOI: 10.1161/01.cir.96.8.2520
ApoB/ApoA1 Ratio: Walldius G et al. AMORIS study. Lancet 2001;358:2026-33. DOI: 10.1016/S0140-6736(01)07098-2

📏 Anthropometric Measures

Body Roundness Index: Thomas DM et al. A simple validated model for predicting body fat. Obesity 2013;21:E338-42. DOI: 10.1002/oby.20408
BRI vs BMI: Rico-Martin S et al. BRI effectiveness in detecting metabolic syndrome. Nutrients 2020;12:E3302. DOI: 10.3390/nu12113302
BRI & Mortality (2024): Zhang X et al. BRI and all-cause mortality. JAMA Netw Open 2024;7:e2415051. DOI: 10.1001/jamanetworkopen.2024.15051
Waist-Hip Ratio: Yusuf S et al. INTERHEART Study. Lancet 2005;366:1640-9. DOI: 10.1016/S0140-6736(05)67663-5
BMI Limitations: Prentice AM, Jebb SA. Beyond BMI. Obes Rev 2001;2:141-7. DOI: 10.1046/j.1467-789x.2001.00031.x
Ethnicity-Specific BMI (Lancet 2021): Caleyachetty R, et al. Ethnicity-specific BMI cutoffs for obesity based on T2D risk in England. Lancet Diabetes Endocrinol 2021;9:419-426. DOI: 10.1016/S2213-8587(21)00088-7
Race-Specific BMI (2024): Wang S, et al. Race- and ethnicity-specific BMI cutoffs for obesity severity. Obesity 2024;32(10):1958-1966. DOI: 10.1002/oby.24129
WHO Asian BMI: WHO Expert Consultation. BMI for Asian populations. Lancet 2004;363:157-163. DOI: 10.1016/S0140-6736(03)15268-3
Asian BMI Screening: Hsu WC, et al. BMI cut points for at-risk Asian Americans. Diabetes Care 2015;38:150-158. DOI: 10.2337/dc14-2391
ADA Asian BMI: Araneta MR, et al. Optimum BMI cut points for Asian Americans. Diabetes Care 2015;38:814-820. DOI: 10.2337/dc14-2071
Personalized BMI: Batsis JA, et al. Race, ethnicity, sex, and obesity: personalize the scale? Mayo Clin Proc 2019;94(2):362-363. DOI: 10.1016/j.mayocp.2018.10.014
AHA 2024 SA CVD BMI: Shahmohamadi E, et al. Ethnic differences in BMI cut-offs for CVD risks in South Asians. Circulation 2024;150(Suppl_1):4145936.
Evolutionary Adiposity: Wells JCK. Natural selection and human adiposity: crafty genotype, thrifty phenotype. Philos Trans R Soc B 2023;378:20220224. DOI: 10.1098/rstb.2022.0224
Fat Distribution Genetics: Sun C, et al. Genetics of body fat distribution: comparative analyses in European, Asian and African ancestries. Genes 2021;12:841. DOI: 10.3390/genes12060841
Visceral Adiposity in Asians: Goh LGH, et al. Genetic and environmental factors contributing to visceral adiposity in Asian populations. Endocrinol Metab 2020;35:681-695. DOI: 10.3803/EnM.2020.772
Ethnic Adiposity & Diabetes: Yaghootkar H, et al. Ethnic differences in adiposity and diabetes risk — insights from genetic studies. J Intern Med 2020;288:271-283. DOI: 10.1111/joim.13082

🫁 Liver Function & Fibrosis

FIB-4 Index: Sterling RK et al. Development of a simple noninvasive index. Hepatology 2006;43:1317-25. DOI: 10.1002/hep.21178
FIB-4 Validation: McPherson S et al. Validation of FIB-4 for NAFLD. Gut 2017;66:138-45. DOI: 10.1136/gutjnl-2015-310864
NAFLD & CVD: Targher G et al. NAFLD and increased risk of CVD. Gut 2017;66:1386-96. DOI: 10.1136/gutjnl-2015-310683
AST/ALT Ratio: Williams AL, Hoofnagle JH. Ratio of serum AST to ALT. Gastroenterology 1988;95:734-9. DOI: 10.1016/s0016-5085(88)80022-2
APRI (Original): Wai CT et al. Simple noninvasive index for HCV fibrosis. Hepatology 2003;38:518-526. DOI: 10.1002/hep.20150
Age-Adjusted FIB-4: McPherson S et al. Age as a confounding factor for FIB-4. Am J Gastroenterol 2017;112:740-751.
AASLD NAFLD/MASLD Guidance: Rinella ME et al. AASLD Practice Guidance. Hepatology 2023;77:1797-1835.

🫘 Kidney Function & eGFR

CKD-EPI 2021: Inker LA et al. New creatinine- and cystatin C-based equations. N Engl J Med 2021;385:1737-49. DOI: 10.1056/NEJMoa2102953
CKD-EPI 2009: Levey AS et al. A new equation to estimate GFR. Ann Intern Med 2009;150:604-12. DOI: 10.7326/0003-4819-150-9-200905050-00006
KFRE Multinational Update: Tangri N et al. Multinational KFRE validation. JAMA 2016;315:164-174. DOI: 10.1001/jama.2015.18202
KDIGO 2024 CKD Guidelines: Kidney Disease: Improving Global Outcomes (KDIGO). Clinical Practice Guideline for CKD Evaluation and Management. KDIGO CKD Guidelines
KFRE (Kidney Failure Risk Equation): Tangri N et al. A predictive model for progression of CKD to kidney failure. JAMA 2011;305:1553-9. DOI: 10.1001/jama.2011.451
CKD & CVD Risk: Go AS et al. Chronic kidney disease and cardiovascular risk. N Engl J Med 2004;351:1296-305. DOI: 10.1056/NEJMoa041031
Diabetes Canada Cardiorenal: McFarlane P et al. Chronic Kidney Disease in Diabetes. Can J Diabetes 2024. guidelines.diabetes.ca

🔬 Metabolic & Glycemic Markers

HOMA-IR: Matthews DR et al. Homeostasis model assessment. Diabetologia 1985;28:412-9. DOI: 10.1007/BF00280883
HbA1c Target: American Diabetes Association. Glycemic targets. Diabetes Care 2024;47(Suppl 1):S158-S178. DOI: 10.2337/dc24-S006
Fasting Glucose: DECODE Study Group. Glucose tolerance and CVD mortality. Lancet 1999;354:617-21. DOI: 10.1016/S0140-6736(98)12131-1

🔥 Inflammatory Markers

hs-CRP: Ridker PM et al. C-reactive protein and risk prediction. N Engl J Med 2002;347:1557-65. DOI: 10.1056/NEJMoa021993
JUPITER Trial: Ridker PM et al. Rosuvastatin and elevated hs-CRP. N Engl J Med 2008;359:2195-207. DOI: 10.1056/NEJMoa0807646
CANTOS Trial: Ridker PM et al. Antiinflammatory therapy with canakinumab. N Engl J Med 2017;377:1119-31. DOI: 10.1056/NEJMoa1707914

🩸 Hematology & Blood Counts

ANC Calculation: Marrowforums.org validated calculator. marrowforums.org/anc.html
Neutropenia Grading: Common Terminology Criteria for Adverse Events (CTCAE) v5.0. NCI CTCAE
Platelet Reference: Kaushansky K et al. Williams Hematology, 9th Ed. McGraw-Hill 2016.
WHO Anaemia Thresholds: World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia. WHO/NMH/NHD/MNM/11.1. 2024 update. WHO Reference

📋 Clinical Guidelines

CCS 2021 Dyslipidemia: Pearson GJ et al. CCS Guidelines. Can J Cardiol 2021;37:1129-50. DOI: 10.1016/j.cjca.2021.03.016
AHA/ACC 2019: Arnett DK et al. Guideline on primary prevention. Circulation 2019;140:e596-e646. DOI: 10.1161/CIR.0000000000000678
ESC 2021: Visseren FLJ et al. ESC Prevention Guidelines. Eur Heart J 2021;42:3227-337. DOI: 10.1093/eurheartj/ehab484
SCORE2 (ESC 2021): SCORE2 Working Group. SCORE2 risk prediction algorithms. Eur Heart J 2021;42:2439-54. DOI: 10.1093/eurheartj/ehab309
SCORE2-OP (ESC 2021): SCORE2-OP Working Group. Prediction algorithms for older persons. Eur Heart J 2021;42:2455-67. DOI: 10.1093/eurheartj/ehab312
SCORE2-Diabetes (ESC 2023): SCORE2-Diabetes Working Group. 10-year CVD risk estimation for persons with Type 2 diabetes. Eur Heart J 2023;44:2544-2556. DOI: 10.1093/eurheartj/ehad260
AHA PREVENT (2024): Khan SS et al. Novel PREVENT equations for 10-year and 30-year CVD risk. Circulation 2024;149:e1144-e1156. DOI: 10.1161/CIR.0000000000001191
NICE CVD Prevention: NICE guideline CG181. Lipid modification. NICE CG181
ACC/AHA 2025 ACS Guideline: Writing Committee. Management of ACS. J Am Coll Cardiol 2025. DOI: 10.1016/j.jacc.2024.11.009
Diabetes Canada 2024: Shah BR et al. Diabetes Canada clinical practice guidelines. Can J Diabetes 2024;48:415-424.
BC PharmaCare PCSK9: Special Authority Criteria 2024. BC PharmaCare

🌎 Ethnic & Regional Considerations

South Asian CVD Risk: Gupta M et al. South Asians and cardiovascular risk. Can J Cardiol 2006;22:193-7. DOI: 10.1016/s0828-282x(06)70893-4
MASALA Study: Kanaya AM et al. Mediators of Atherosclerosis in South Asians. J Clin Lipidol 2013;7:561-70. DOI: 10.1016/j.jacl.2013.05.006
African Ancestry: Carnethon MR et al. Cardiovascular health in African Americans. Circulation 2017;136:e393-e423. DOI: 10.1161/CIR.0000000000000534
Indigenous Health: Heart and Stroke Foundation of Canada. Indigenous peoples. heartandstroke.ca

─⚠️ Validation Tools & External Calculators

qrisk.org - Official QRISK3/QRISK4 Calculator
framinghamheartstudy.org - Framingham Heart Study
ACC ASCVD Risk Estimator Plus
heartscore.org - ESC SCORE2/SCORE2-OP Calculator
ESC SCORE2-Diabetes Calculator - Type 2 Diabetes Risk
AHA PREVENT Calculator - Official PREVENT Equations (10-year & 30-year)
kidney.org eGFR Calculator
marrowforums.org/anc.html - ANC Calculator
MDCalc CKD-EPI 2021
MDCalc FIB-4 Calculator

🎯 Algorithm Calibration & Validation (v30.74.73)

SCORE2 Published Example: M50 smoker, SBP 140, TC 5.5, HDL 1.3: Low-risk=5.9%, Very-high=14.0% (male); Low=4.2%, VH=13.7% (female). Source: SCORE2 Working Group. Eur Heart J 2021;42(25):2439-2454, Table/Figure 3. DOI: 10.1093/eurheartj/ehab309
SCORE2-OP Published Example: M75 smoker, SBP 150, non-HDL-c 4.5: Low-risk=16%, Very-high=37% (male); Low=14%, VH=44% (female). Source: SCORE2-OP Working Group. Eur Heart J 2021;42(25):2455-2467, Supplementary Figure S9. DOI: 10.1093/eurheartj/ehab312
SCORE2-OP CONOR Coefficients: Table 2 of SCORE2-OP paper: sex-specific coefficients and SHRs centered at age=73, SBP=150, with age×risk-factor interaction terms. Derivation: 28,503 individuals, 10,089 CVD events.
SCORE2/SCORE2-OP Validation: Kimenai DM et al. Validation of SCORE2 and SCORE2-OP in the EPIC-Norfolk prospective population cohort. Eur J Prev Cardiol 2024;31(2):182-190. DOI: 10.1093/eurjpc/zwad289
Risk Chart vs Calculator: Hageman SHJ et al. Using SCORE2 with a risk chart or online calculator: impact on model performance. Eur Heart J Qual Clin Outcomes 2025. DOI: 10.1093/ehjqcco/qcaf122
Vascular Age from SCORE2: Cuende JI et al. Vascular age calculation from SCORE2 and SCORE2-OP. medRxiv 2025. DOI: 10.1101/2025.05.05.25327022 - Heart Age methodology reference
PREVENT Development: Khan SS et al. Development and validation of the AHA PREVENT equations. Circulation 2024;149(6):430-449. DOI: 10.1161/CIRCULATIONAHA.123.067626
Updates in CV Risk Assessment: ACC Experts. Updates in cardiovascular disease risk assessment: an international perspective (SCORE2, SCORE2-OP, PREVENT comparison). ACC 2025

🌍 External Validation Tools & International Risk Models (v30.74.73)

📊 R Shiny Validation App (SCORE2 Asia-Pacific): Hageman SHJ et al. interactive validator at hagemanshj.shinyapps.io/SCORE2ASIAPACIFIC — for scientific use only, not clinical deployment. Can be used as external gold standard alongside the preventr R package for PREVENT validation.
⚕️ CE-Marked U-Prevent Platform: SCORE2 Asia-Pacific integration being added to U-Prevent (CE-marked medical device). U-Prevent provides an independent clinical validation framework and serves as the reference CE-marked implementation for SCORE2 family calculators. Future toolkit validation should be cross-referenced against U-Prevent outputs.
📦 preventr R Package (PREVENT Validation): Sadler B et al. preventr v0.11.0 — official R implementation of AHA PREVENT equations. GitHub. Our PREVENT implementation uses coefficients extracted directly from preventr:::coef_10yr and preventr:::coef_30yr (23-26 terms per model).
🇪🇺→🌏 SCORE2 Asia-Pacific (ESC 2025): Hageman SHJ et al. Risk prediction of CVD in the Asia-Pacific region: the SCORE2 Asia-Pacific model. Eur Heart J 2025;46(8):702-715. DOI: 10.1093/eurheartj/ehae609. Derivation: 8.4M individuals (556K events). Validated in 9.6M individuals across 12 countries. C-statistic: 0.710 (range 0.605-0.840). Age range: 40-69 only — no SCORE2-OP equivalent for older Asian populations published yet.
⚠️ SCORE2 Asia-Pacific Limitation (Stroke Subtypes): Yang C & Lv J. Commentary: SCORE2 Asia-Pacific model does not distinguish haemorrhagic vs ischaemic stroke subtypes. Eur Heart J 2025;46:1680. In Asian populations, haemorrhagic stroke is more prevalent; statins may increase haemorrhagic stroke risk in some populations. Clinical interpretation should consider this limitation.
🧬 SCORE2 Asia-Pacific Risk Regions: Low (<100 CVD deaths/100K): Australia, NZ, Japan, South Korea. Moderate (100-150): Singapore, Hong Kong, Taiwan. High (150-300): China, Thailand, Malaysia, Vietnam. Very High (≥300): India, Indonesia, Philippines, Bangladesh, Pakistan.
🚫 SCORE2-Diabetes Not Validated for Asia-Pacific: The ESC 2023 SCORE2-Diabetes model (SCORE2-Diabetes Working Group. Eur Heart J 2023;44:2544-2556) was derived and calibrated exclusively in European populations. No Asia-Pacific recalibration has been published. Applying European coefficients to Asian populations may overestimate risk for some subgroups.
📅 PREVENT 30-Year Model Age Limit: The PREVENT 30-year risk model (Khan SS et al.) is validated for ages 30-59 only (the patient must be young enough that a 30-year projection does not exceed the maximum study follow-up). For ages 60-79, only the 10-year PREVENT model should be used. This toolkit shows a warning when 30-year risk is calculated for patients aged 60+.
🔄 Competing Risk Adjustment (SCORE2-OP): SCORE2-OP uses a Fine-Gray competing risk subdistribution hazard model (non-CVD mortality as competing event), per SCORE2-OP Working Group methodology. This is critical for patients ≥70 where non-CVD mortality significantly affects risk estimation. Standard Cox models would overestimate CVD risk in this age group.

📛 Citation Note: All DOI links verified as of February 2026. Click any DOI to access the source publication. Algorithm calibration anchored against published ESC 2021 examples (v30.74.73).

ℹ Aboutv

CVD Risk Toolkit v30.74.73

Created by: Manjinder S., M.D., B.Sc. (Biochemistry)

A.S. (General Science) • Minor in Commerce

v30.74.73 New Features:

SCORE2 ESC 2021 Calibration: Anchored to published M50 example (5.9%/14.0%) with non-linear age×SBP interactions
SCORE2-OP Published Coefficients: Table 2 CONOR-derived coefficients with age×risk-factor interactions, calibrated to published M75 anchor (16%/37%)
SCORE2→SCORE2-OP Auto-Delegation: Ages ≥70 automatically routed to SCORE2-OP algorithm
QRISK3-Lifetime Integration: Lifetime CVD risk with Aalen-Johansen competing risk estimator, wired into QRISK results display
Cumulative Risk Curve Visualization: Canvas-based patient vs optimal risk curves from current age to 95
Heart Age from Lifetime Risk: Binary search to find age where optimal-risk person matches patient's lifetime risk
PREVENT Unit Harmonization: Auto-detection and mmol/L conversion for lipid coefficients to eliminate mg/dL unit mismatch
Algorithm Calibration References: 8 new calibration/validation citations including EPIC-Norfolk validation study

v30.74.73 Features:

Element Pre-Registration System: 85+ elements validated before tests run with auto-creation
Lazy Panel Pre-Render: Hidden panels (HeFH, SI, VHR) elements guaranteed in DOM
Dynamic Panel Discovery: Auto-detect hidden panels with form elements
Patient ID Integration: All calculations linked to active Patient ID with timestamps
QRISK3 vs QRISK4 Comparison: Side-by-side table with difference analysis and factor breakdown
Enhanced CSV Export: 30+ column export with all risk scores and clinical data
JSON Export: Full assessment history export for data portability
Family Cascade Screening: Detailed impact analysis with NNT and patient record linking
PCSK9i Eligibility: CCS 2021 three-pathway assessment (HeFH, Statin Intolerance, VHR)
900+ Automated Tests: Comprehensive validation with 99%+ pass rate
Async Test Execution: Proper Promise-based test completion tracking
Performance Optimized: 50 tests/batch, 60s cache TTL, faster execution
90+ Clinical References: DOI links across 12 categories

⚠️ Medical Disclaimer: This toolkit is for educational and clinical decision support only. Verify calculations with validated external tools. Clinical decisions require qualified healthcare professionals.

👨‍⚕️ About the Developer

Manjinder S., M.D.

B.Sc. (Biochemistry) • A.S. (General Science) • Minor in Commerce

This toolkit was developed with a personal mission: to improve cardiovascular risk assessment, particularly for elevated Lp(a), inspired by family health history.

⚛ Quantum Healthcare Lab

QSVM Risk Prediction & Simulation Demo

Simulation Mode Active

🧪 QSVM Risk Prediction Demo

Test Quantum Support Vector Machine prediction vs classical methods

Age

TC/HDL Ratio

Systolic BP (mmHg)

Lp(a) (nmol/L)

Smoking

Diabetes

Click "Run QSVM Prediction" to see results

📊 Classical vs Quantum Comparison

Generate batch predictions and compare accuracy metrics

Classical SVM

Accuracy

Quantum SVM

Accuracy

No patients generated yet

🖥 IBM Quantum Access

This project has access to IBM Quantum systems for real quantum execution

127

Qubits

Min/Month

Heron

Processor

⚠ Simulation Mode

Currently running in browser simulation. Real quantum execution requires Python backend with IBM Qiskit Runtime.

📖 How QSVM Works

Feature Encoding

Patient risk factors (age, BP, Lp(a), etc.) are encoded into quantum states using ZZ-Feature Maps

Quantum Kernel

Entanglement captures ALL feature interactions simultaneously, impossible classically

Classification

SVM hyperplane in quantum feature space separates high/low risk with enhanced precision

Research Result: Hybrid quantum models (QGA-QPSO-QSVM) achieved 97.83% accuracy for heart disease prediction in published studies.

📊 Session Metrics

Total Predictions

QSVM Predictions

Classical Predictions

Avg Latency (ms)

Cascade Screenings

CVD Risk Toolkit

Test Results

Stress Test Results

📊 Batch Risk Calculation

🔬 Deep Research Analysis

🤖 AI-Powered Analysis

📤 Cohort Data Export

💊 Medication Reconciliation

📈 Population Health Dashboard

🩸 Antiplatelet Therapy Assessment

Drug Interaction Results

Drug-Condition Check Results

⚠️ Data Conflict Detected

eGFR Trend Over Time

PCSK9 Inhibitor Eligibility Pathways

👤 Patient Demographics & Basic Information

📋 Basic Information

📏 Anthropometric Measurements

❓ Why BRI over BMI?

🌍 BMI Ranges by Ethnicity & Race

🧬 Why Do BMI Ranges Differ? — An Evolutionary Biology Perspective

🩺 Blood Pressure

📊 BP Readings (6+ readings for QRISK3 variability)

⚠️ Risk Factors

💊 Medications & Laboratory Values

📊 Biomarker Interpretation Guide

Liver Fibrosis Score Interpretation

📋 A/G Ratio (Albumin/Globulin)

🩸 Coagulation Reference

⚡ Electrolytes

🧬 White Cell Differential

🔬 Comprehensive ANC Calculator (Marrowforums Reference)

📖 ANC Reference (marrowforums.org)

🔴 Hemolysis Markers Interpretation

HOMA-IR Interpretation

Clinical Interpretation Guide

💊 Vitamins & Nutrients

🩸 Iron Panel Interpretation

🩺 Anemia Classification Algorithm

💊 Current Medications

📅 Medication Timeline

⚠️ Drug Interactions Detected

💊 Lipid-Lowering Drug Interaction Checker

📊 Baseline Lipid Tracking (NICE NG238 Compliance)

📊 Framingham Risk Score Calculator

⚠️ Critical Interpretation Note

🗺️ Geographic & Situational Usage Guide

🇨🇦 Canada

🇺🇸 United States

🇬🇧 United Kingdom

🇪🇺 Europe

💻 MDCalc / EMR Matching

🧓 Elderly / Comprehensive Risk

📋 Risk Factor Inputs (9 Required)

📈 Results

📋 Clinical Interpretation (CCS 2021)

📋 FRS Calculation History

📛 Reference

🇬🇧 QRISK3/QRISK4 Calculator

🔬 Understanding QRISK3 vs QRISK4

📊 Age-Adjusted Risk Modeling

New Factors (All Adults)

Additional Factors (Women Only) — QRISK4 enhancement only; not in standard QRISK3

Exploratory Factors — Not in published QR4 model; for research tracking only

🧮 See Age Impact on Your Selected Conditions

📈 BP Trend & Variability

📊 Population BP Variability

⚖️ QRISK3 vs QRISK4 Comparison

📈 Lifetime CVD Risk (Aalen-Johansen Estimator)

📋 QRISK Calculation History

📛 Reference

🫀 AHA PREVENT Equations (2024)

📊 Model Tier

📋 Core Inputs (auto-synced from Patient & Labs tabs)

⭐ Enhancement Inputs

🧬 Lp(a) Risk Modification

🦴 CAC Score Integration (optional)

📅 Vascular Age

💊 Treatment Implications

📖 Understanding PREVENT

📜 Calculation History