Accumulation of Deficits as a Key Risk Factor for Cardiovascular Morbidity and Mortality: A Pooled Analysis of 154 000 Individuals
Bibliographic record
Abstract
Background Frailty is associated with higher mortality in individuals at high cardiovascular disease ( CVD ) risk. We hypothesize that frailty is a more important prognostic factor than CVD risk factors and aim to determine the prognostic value of a cumulative deficit frailty index in patients with or at high risk for CVD . Methods and Results We conducted an individual‐level pooled analysis of participants with or at risk for CVD , recruited in 14 multicenter clinical trials. The cumulative deficit index was calculated as the proportion of 26 deficits exhibited. Individuals were categorized as nonfrail, prefrail, or frail if they had indexes of ≤0.1, >0.1 to 0.21, or >0.21, respectively. CVD risk was assessed using the Framingham score. Outcomes included CVD event (new or recurrent myocardial infarction, stroke, or heart failure) and mortality. We studied 154 696 patients (mean age, 70.8 years; 63% men) with median follow‐up of 3.2 years. There were 17 535 CVD events and 15 067 deaths. The frail group (n=13 872) had higher risk of a CVD event (incidence rate ratio, 1.97; 95% CI , 1.85–2.08), all‐cause mortality (hazard ratio, 1.91; 95% CI , 1.79–2.03), and CVD mortality (hazard ratio, 1.91; 95% CI , 1.77–2.05) than the nonfrail group (n=101 343). Associations remained unchanged after adjusting for CVD risk factors. The index statistically outperformed the Framingham score in its ability to discriminate CVD events (C‐statistic, 0.60 [95% CI , 0.60–0.61] versus 0.58 [95% CI , 0.57–0.58], respectively; P <0.001). Conclusions In individuals with or at high risk of developing CVD , the cumulative deficit index is associated with increased CVD events and mortality, independent of CVD risk factors, and adds incremental prognostic value.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".