Abstract 14496: Frailty is Associated With Increased Cardiovascular Mortality in 2,837,152 United States Veterans Aged 65 and Older
Bibliographic record
Abstract
Introduction: Frailty has been associated with an increased risk of all-cause mortality and CV events. There are limited data from the modern era of CV prevention to examine the relationship between frailty and CV mortality. We hypothesized that frailty would be associated with with increased risk of CV mortality. Methods: All Veterans ≥65 who were regular users of VA care from 2002–2014 were included. Index date was the last visit date in the year of cohort entry. Data were queried biennially, with frailty & outcomes queried each period. Frailty was defined using a 31-item previously validated frailty index, ranging from 0-1. Degrees of frailty were defined as: not frail (FI <0.1), pre-frail (FI >0.1-≤0.2), mild frailty (FI >0.2-≤0.3), moderate frailty (FI >0.3-≤0.4), and severe frailty (FI >0.4). Variables were extracted from national VA administrative data linked to Medicare and Medicaid. The primary outcome was CV mortality. Survival analysis was performed. Models were adjusted for age, sex, race/ethnicity, geographic region, smoking status, hyperlipidemia, statin use, and blood-pressure medication use. Results: There were 2,837,152 Veterans included in the analysis. In 2002 mean age was 74+/- 5.8 years and in 2012 was 76+/- 8.1 years, 98% were male, 88.8% were white. In 2002, the median frailty score was 0.16 (IQR= 0.13). This increased and then stabilized to 0.19 for 2006 to 2012 (IQR= ranging 0.19 to 0.23). Overall frailty became more prevalent over time (prevalence increased from 31.9% in 2002 to 46.5% in 2012). The presence of frailty was associated with increased risk of CVD mortality at every degree of frailty and year, as shown in the Table. Discussion: Frailty is highly prevalent in the VA population, and both the presence and severity of frailty are tightly correlated with CV death. This study is the largest and most contemporary evaluation of the relationship between frailty and CV mortality to date. Further work is needed to understand how this risk can be diminished.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".