Transitions From Frailty States to Cardiovascular Events: An 11-Year Prospective Study of Community-Dwelling Older People
Bibliographic record
Abstract
BACKGROUND: This study aims to quantify transition probabilities between frailty states (not-frail, pre-frail, frail) and from different frailty states to the occurrence of a cardiovascular disease (CVD) event; and to examine how age, sex and sociodemographic factors influence these transitions. METHODS: 18,077 initially healthy individuals (91% Caucasians, 56% females) aged ≥65 years enrolled in the ASPREE study who had no prior CVD event and ADL (Activity of Daily Living) disability at recruitment were followed for a median of 7.4 years. Frailty was annually assessed using a 64-item Frailty Index. Continuous-time multi-state Markov modelling was used. RESULTS: The estimated transition probabilities from frail to CVD increased over time (11% at five years, and 18% at ten years) and consistently exceeded the corresponding five- and ten-year transition probabilities from pre-frail to CVD (8% and 14% respectively). Compared to males, females had a 26% higher relative risk of progressing to a pre-frail/frail state; however, they had about 50% lower relative risk of transitioning from pre-frail/frail to CVD. Older age, socioeconomic and geographic disparities were associated with up to 38% higher relative risk of worsening frailty and progression to CVD. Similar findings were observed when using the Fried phenotype. CONCLUSION: The probability of transiting from pre-frail/frail to CVD increased over time, even among initially healthy older people. Targeted prevention strategies may be helpful to delay frailty progression and reduce CVD risk in older age, particularly socioeconomically disadvantaged individuals or those residing outside major cities, and different approaches may be required in females and males.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".