Predicting Risk of Atherosclerotic Cardiovascular Disease Using Pooled Cohort Equations in Older Adults With Frailty, Multimorbidity, and Competing Risks
Bibliographic record
Abstract
Background Assessment of atherosclerotic cardiovascular disease (ASCVD) risk is crucial for prevention and management, but the performance of the pooled cohort equations in older adults with frailty and multimorbidity is unknown. We evaluated the pooled cohort equations in these subgroups and the impact of competing risks. Methods and Results In 4249 community-dwelling adults, aged ≥65 years, from the CHS (Cardiovascular Health Study), we calculated 10-year risk of hard ASCVD. Frailty was determined using the Fried phenotype. Latent class analysis was used to identify individuals with multimorbidity patterns using chronic conditions. We assessed discrimination using the C-statistic and calibration by comparing predicted ASCVD risks with estimated risk using cause-specific and cumulative incidence models, by multimorbidity patterns and frailty status. A total of 917 (21.6%) participants had an ASCVD event, and 706 (16.6%) had a competing event of death. C-statistic was 0.68 in men and 0.69 in women; calibration was good when compared with cause-specific and cumulative incidence estimated risks (males, -0.1% and 3.3%; females, 0.6% and 1.4%). Latent class analysis identified 4 patterns: minimal disease, cardiometabolic, low cognition, musculoskeletal-lung depression. In the cardiometabolic pattern, ASCVD risk was overpredicted compared with cumulative incidence risk in men (7.4%) and women (6.8%). Risk was underpredicted in men (-10.7%) and women (-8.2%) with frailty compared with cause-specific risk. Miscalibration occurred mostly at high predicted risk ranges. Conclusions ASCVD prediction was good in this cohort of adults aged ≥65 years. Although calibration varied by multimorbidity patterns, frailty, and competing risks, miscalibration was mostly present at high predicted risk ranges and thus less likely to alter decision making for primary prevention therapy.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".