How blood pressure predicts frailty transitions in older adults in a population-based cohort study: a multi-state transition model
Bibliographic record
Abstract
BACKGROUND: Low blood pressure (BP) is associated with frailty in older adults. Our aim was to explore how BP predicts transitions between frailty states. METHODS: We used data from the Lausanne cohort Lc65+, a population-based cohort of older adults randomly drawn from a population registry in Switzerland, in 2004, 2009 and 2014. BP was measured using a clinically validated oscillometric automated device and frailty was defined using Fried's phenotype, every 3 years. We used an illness-death discrete multi-state Markov model to estimate hazard ratios of forward and backward transitions between frailty states (outcome) in relation to BP categories (predictor of interest) with adjustment for sex, age and antihypertensive medication (other predictors). RESULTS: Among 4200 participants aged 65-70 years (58% female) at baseline, 70% were non-frail, 27% pre-frail and 2.0% frail. Over an average follow-up of 5.8 years, 2422 transitions were observed, with 1575 (65%) forward and 847 (35%) backward. Compared with systolic BP (SBP) <130 mmHg, the hazard ratio (95% confidence interval) of the transition from non-frail to pre-frail was 0.86 (0.74 to 1.00) for SBP 130-150 mmHg, and 0.89 (0.74 to 1.06) for SBP ≥150 mmHg. Compared with SBP <130 mmHg, the hazard ratio of the transition from pre-frail to frail was 0.71 (0.50 to 1.01) for SBP 130-150 mmHg, and 0.90 (0.62 to 1.32) for SBP ≥150 mmHg. Diastolic BP was a weaker predictor of forward transitions. CONCLUSIONS: BP categories had no strong relationship with either forward transitions or backward transitions in frailty states. If our findings are confirmed with greater precision and assuming a causal relationship, they would suggest that there is no well-defined optimal BP level to prevent frailty among older adults.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".