Assessment of frailty and quality of life in octogenarians with symptomatic coronary artery disease : the FRAIL HEART study
Bibliographic record
Abstract
BACKGROUND:Symptomatic coronary artery disease (CAD) is an increasing problem for older adults, however little is known about the relationship between frailty and health outcomes.PURPOSE:To determine the prevalence of, and relationship between frailty, quality of life (QoL) and major adverse cardiac events (MACE) in older adults with CAD.METHODS:A narrative literature review confirmed a knowledge gap. We therefore conducted an exploratory, prospective observational study of in- or out-patients (≥80years) with symptomatic CAD recruited between June 2016 and January 2017. Participants were evaluated for frailty (Fried Frailty Phenotype, Edmonton Frailty scale), quality of life (QoL; SF-12), clinico-demographic characteristics, including treatments received. Data were collected at baseline and 4 months and descriptive statistics applied. Regression techniques were used to explore relationships between variables.RESULTS:Consecutive participants (n=150; mean age 83.7±3.2 years; 99 (66%) men; acute coronary syndrome 82 (54.7%)) were treated with: PCI (51; 34%); CABG (15;10%); medical (84; 56%). About one quarter were frail (26% EFS; 28% FFP). Frailty was inversely related to SF-12 (PCS 30.5±7.1 vs 43.5±7.6, p=0.005, MCS 47.4±i12.8 vs 57.1±6.4, p=0.003) and directly related to comorbidity (7.5±2.4 vs 5.9±1.6, p=0.005) at baseline. Follow up at 114 days (50-243) showed overall MACE (24.7%) and poorer survival amongst frail participants (Dead/frail 50.0% versus alive/frail 26.2%, p=0.002).CONCLUSIONS:About one quarter of older adults with CAD have frailty. Our data show that frailty is inversely related with QoL and clinical outcomes. This data suggest that frailty is an important therapeutic target in this age group. A larger cohort is needed to confirm these exploratory findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".