Stroke Incidence in Patients With Hypertension According to Cardiorespiratory Fitness
Bibliographic record
Abstract
BACKGROUND: Hypertension and physical inactivity are risk factors for stroke. The effect of cardiorespiratory fitness (CRF) on stroke risk in patients with hypertension has not been assessed. We evaluated stroke incidence in patients with hypertension according to CRF and changes in CRF. METHODS: We included 483 379 patients with hypertension (mean age±SD; 59.4±9.0 years) and no evidence of unstable cardiovascular disease as indicated by a standardized exercise treadmill test. Patients were assigned to 5 age- and sex-specific CRF categories based on peak metabolic equivalents achieved at the initial exercise treadmill test and in 4 categories based on metabolic equivalent changes over time (n=110 576). Multivariable Cox models, adjusted for age, and comorbidities were used to estimate hazard ratios and 95% CIs for stroke risk. RESULTS: During a median follow-up of 10.6 (interquartile range, 6.6-14.6) years, 15 925 patients developed stroke with an average yearly rate of 3.1 events/1000 person-years. Stroke risk declined progressively with higher CRF and was 55% lower for the High-fit individuals (hazard ratio, 0.45 [95% CI, 0.42-0.48]) compared with the Least-fit. Similar associations were observed across the race, sex, and age spectra. Poor CRF was the strongest predictor of stroke risk of all comorbidities studied (hazard ratio, 2.24 [95% CI, 2.10-2.40]). Changes in CRF reflected inverse and proportional changes in stroke risk. CONCLUSIONS: Poor CRF carried a greater risk than any of the cardiac risk factors in patients with hypertension, regardless of age, race, or sex. The lower stroke risk associated with improved CRF suggests that increasing physical activity, even later in life, may reduce stroke risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".