Abstract P011: Predictive Ability of 35 Frailty Scores for Cardiovascular Events in the General Population
Bibliographic record
Abstract
Introduction: Frailty is a state of vulnerability in elderly people linked to higher mortality risk. Cardiovascular disease (CVD) is highly prevalent in aged populations and associated with frailty. Thus, frailty state could predict higher risk of CVD. Many frailty scores (FS) have been developed, but none of them is considered the gold standard. We aimed to compare predictive and discriminative ability of an extensive list of FS with regard to incidence of CVD in a sample of the general elderly population in England. We assessed the hypothesis that some FS will have better predictive ability than others, depending on their characteristics. Methods: We performed a prospective analysis of the association between 35 FS in participants free of CVD at baseline wave 2 of the English Longitudinal Study of Ageing (2004-2005), and incident CVD assessed until February 2012. The sample consisted of 4,177 participants (43.0 % men). Hazard ratios (HR) and 95% confidence intervals (95% CI) were calculated for each FS using Cox proportional hazards model, adjusted for demographic, lifestyle and comorbidity variables. FS were analyzed on a continuous scale and using original cutoffs. The added predictive ability of FS beyond a basic model consisting of sex and age was studied using Harrel’s C statistic (the higher the better). Results: The median follow-up was 5.8 years, the incidence rate of CVD events was 301.2 /10,000 person-years and CVD represented 28% of the total cause of death. The mean age was 70.5 (SD: ±7.8) years. In fully-adjusted models with demographics, lifestyles and comorbidity, HRs ranged from: 1.0 (0.7; 1.6) to 12.7 (5.5; 29.3). Using cutoffs, HRs ranged from 0.7 (0.2; 1.9) to 1.8 (1.3; 2.5). Adjusted for sex and age, delta Harrel’s C statistic ranged from -0.8 (-3.4; 1.8) to 3.0 (-0.4; 6.4). The best CVD predictive ability was found for the Frailty Index with 70 variables and the Comprehensive Geriatric Assessment screening FS for continuous and cutoff analyses respectively. In conclusion, there is high variability in the association between different published FS and incident CVD. FS have better predictive ability used as continuous variable. Although most of the analyzed FS have good predictive ability with regard to incident CVD, they do not significantly improve on the discriminative capacity of a basic model. Our results will help to guide clinicians, researchers and public health practitioners in choosing the most informative frailty assessment tool.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".