Updating and Developing Risk Prediction Models for Incident Cardiovascular Disease in Ontario, Canada
Bibliographic record
Abstract
The Framingham Risk Score and Pooled Cohort Equations are used to predict cardiovascular disease risk and select individuals for preventative therapy. Studies have suggested these scores overestimate risk, may be underutilized because they are time consuming, and may predict narrower outcomes that may not be the most relevant endpoint for primary prevention. The objectives of this dissertation were: (1) recalibrate and validate the Framingham Risk Score and Pooled Cohort Equations; (2) develop and validate new sex-specific models for atherosclerotic cardiovascular disease using common serum laboratory tests; and (3) estimate the rate of nine cardiovascular disease manifestations in a contemporary primary prevention population.In the first study, the Framingham Risk Score was recalibrated in 6,938,971 individuals and validated in 71,450 primary care patients. Recalibration reduced overestimation in women (109% to 49%) and men (131% to 32%). However, recalibration of the Pooled Cohort Equations did not improve risk estimates. These results suggest recalibration is feasible but dependent on the model used. In the second study, sex-specific laboratory models were developed and internally validated for atherosclerotic cardiovascular disease in 3,995,218 individuals. The C-statistic was 0.77 in women and 0.72 in men with less than 2% relative difference between mean predicted and observed risks. External validation in 31,707 primary care patients demonstrated C-statistics of 0.72 in both sexes and less than 15% relative difference between mean predicted and observed risks. This suggests cardiovascular risk can be predicted without clinical risk factors which may simplify risk estimation in routine clinical practice. In the final study, among 7,496,165 individuals followed for 11 years, the rate of incident atherosclerotic cardiovascular disease defined by a myocardial infarction, stroke, or circulatory death event was 3.95 per 1000 person-years. When including unstable angina, transient ischemic attack, heart failure, peripheral arterial disease, coronary revascularization, and out-of-hospital cardiac arrest events, the rate was almost doubled at 6.67 per 1000 person-years. The most common additional manifestations were heart failure (12.0%) and coronary revascularization (12.7%). Therefore, atherosclerotic cardiovascular disease accounts for just over half of all incident cardiovascular disease manifestations. This suggests broader endpoints may enhance risk-discussions with patients and improve informed decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".