PREDICTION OF MAJOR CARDIOVASCULAR AND CEREBROVASCULAR OUTCOMES FOR OLDER PEOPLE WITH HYPERTENSION AND FRAILTY: LINKED PRIMARY CARE ELECTRONIC HEALTH RECORDS COHORT STUDY
Bibliographic record
Abstract
Objective: For older people, the association of blood pressure (BP) with cardiovascular outcomes may vary according to the degree of frailty. We investigated whether frailty improved the prediction of major adverse cardiovascular or cerebrovascular events (MACCE) among older people with hypertension treated for primary prevention. Design and method: Retrospective cohort study using population-scale electronic health record data from the Welsh Secure Anonymised Information Linkage (SAIL) databank, linked to hospital outcomes from Patient Episode Database for Wales (PEDW), and death outcomes from Office for National Statistics. Inclusion criteria were age > 65 years, BP recorded in primary care in 2007, and diagnosis of hypertension. Exclusion criteria were established cardiovascular disease (stroke, myocardial infarction or heart failure). We extracted the minimum BP measurement on the day the patient first attended primary care in 2007, and BP-lowering treatment over one year prior. Cardiovascular risk was measured using QRISK-3, and frailty using the electronic frailty index (eFI). Time-to-event analysis measured first ever MACCE (stroke, myocardial infarction, heart failure or cardiovascular death) through 10-years follow-up. Results: The analytic cohort comprised 145,598 patients, registered in 502 general practices. The mean age was 75-years (SD 7); 62% were female; 17% were in the most deprived Townsend quintile. Using the QRISK-3 cardiovascular risk score, 29% (IQR 21 – 39) were predicted to suffer coronary heart disease or stroke over 10-years. The unadjusted risks for MACCE were all increased with increasing severity of frailty (Figure). Compared with robust participants, living with frailty was associated with significantly higher MACCE events despite adjustment for known cardiovascular risk factors (increased risk of 38% in mild frailty, 84% in moderate frailty and 117% in severe frailty). The addition of frailty to a model that was already adjusted for cardiovascular risk factors and BP-lowering treatment improved measures of model fit (AIC reduced from 248,829 to 247,409; BIC reduced from 249,023 to 247,579). Conclusions: Our study provides population-based evidence that the degree of frailty is a useful prognostic factor in older people who are at high risk of cardiovascular outcomes in the management of hypertension.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".