Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses
Bibliographic record
Abstract
OBJECTIVE: The accuracy of various 10-year cardiovascular disease (CVD) risk calculators in Indians may not be the same as in other populations. Present study was conducted to compare the various calculators for CVD risk assessment and statin eligibility according to different guidelines. METHODS: Consecutive 1110 patients who presented after their first myocardial infarction were included. Their CVD risk was calculated using Framingham Risk score- Coronary heart disease (FRS-CHD), Framingham Risk Score- Cardiovascular Disease (FRS-CVD), QRISK2, Joint British Society risk calculator 3 (JBS3), American College of Cardiology/American Heart Association (ACC/AHA), atherosclerotic cardiovascular disease (ASCVD) and WHO risk charts, assuming that they had presented one day before cardiac event for risk assessment. Eligibility for statin uses was also looked into using ACC/AHA, NICE and Canadian guidelines. RESULTS: FRS-CVD risk assessment model has performed the best as it could identify the highest number of patients (51.9%) to be at high CVD risk while WHO and ASCVD calculators have performed the worst (only 16.2% and 28.3% patients respectively were stratified into high CVD risk) considering 20% as cut off for high risk definition. QRISK2, JBS3 and FRS-CHD have performed intermediately. Using NICE, ACC/AHA and Canadian guidelines; 76%, 69% and 44.6% patients respectively were found to be eligible for statin use. CONCLUSION: FRS-CVD appears to be the most useful for CVD risk assessment in Indians, but the difference may be because FRS-CVD estimates risk for several additional outcomes as compared with other risk scores. For statin eligibility, however, NICE guideline use is the most appropriate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".