Gender differences in all-cause mortality in patients with heart failure and stroke
Bibliographic record
Abstract
Abstract Purpose to analyse gender differences in all-cause mortality in patients with HF and previous stroke. Methods From February 2017 to January 2022, we analysed mortality and predictors of all-cause death in women and men with HF. Baseline data included clinical features and echocardiographic findings. The analysed comorbidities were previous stroke, previous myocardial infarction (MI), diabetes, atrial fibrillation (AF), and chronic kidney disease (CKD). We used the Kaplan-Meier method (K-M) and Cox proportional hazards methods to analyse mortality rates. Predictors of death were obtained using the score chi-square of Cox regression. Results We analysed, in a mean follow-up period of 2.2±0.9 years, 11,282 patients with a mean age of 63.9 ± 14.4 years, and 6,256 (55.4%) were men. Patients with previous strokes were older (66.1±13.7 vs. 63.8±14.4 years; p<0.0001), had lower initial left ventricular ejection fraction (44.4 ±16.4% vs. 46.3±16%; p=0.009), and greater initial left ventricular diastolic diameter (58.5±10.4 vs. 57.2±9.6 mm; p=0.010). The prevalence of ischemic cardiomyopathy was higher in men (p=0.010), while hypertensive cardiomyopathy (CMP) (p=0.029) and valvular CMP (p=0.025) were more prevalent in women. The prevalence of heart failure with reduced ejection fraction was higher in men (p<0.001), whereas heart failure with preserved ejection fraction was more common in women (p<0.001). The cumulative incidence of death was higher in men without stroke (p=0.040) compared to women without stroke, and in women with stroke (p<0.001) compared to men with stroke (Figure). Cox regression for death, adjusted for age, gender, initial LVEF, ischemic CMP, idiopathic CMP, hypertensive CMP, valvular CMP, diabetes, CKD, AF, and stroke, showed, in decreasing order of importance, CKD [HR=2.76 (95% CI: 2.51-3.03); p<0.001], stroke [HR=2.49 (95% CI: 2.20-2.81); p<0.001], diabetes [HR=2.06 (95% CI: 1.88-2.26); p<0.001], AF [HR=1.87 (95% CI: 1.71-2.04); p<0.001], age [HR=1.02 (95% CI: 1.01-1.02); p<0.001], LVEF [HR=0.77 (95% CI: 0.73-0.81); p<0.001], and valvular CMP [HR=1.65 (95% CI: 1.45-1.87); p<0.001] as independent variables for death in the total population. For patients with HF and stroke, Cox regression adjusted for the same variables showed CKD [HR=1.43 (95% CI: 1.12-1.83); p<0.001], valvular CMP [HR=1.71 (95% CI: 1.25-2.36); p=0.001], diabetes [HR=1.51 (95% CI: 1.19-1.92); p=0.001], LVEF [HR=0.75 (95% CI: 0.66-0.86); p=0.001], and age [HR=1.02 (95% CI: 1.01-1.03); p=0.005] as independent variables for death. Conclusion Stroke was one of the leading independent variables associated with mortality from all causes in the analysed sample, with a particularly unfavourable prognosis in patients with valvular CMP and in women with stroke. These findings highlight the importance of preventive and therapeutic strategies aimed at reducing the impact of stroke, especially in high-risk populations.Figure
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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 | 0.000 | 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) | 0.002 | 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".