Sex differences in long-term outcomes among hospitalized heart failure patients across the spectrum of ejection fraction: findings from the Get with the guidelines - heart failure registry
Bibliographic record
Abstract
Abstract Background Sex differences in 5-year outcomes across heart failure (HF) ejection fraction (EF) subtypes are not well known. Purpose To assess the interaction between sex and EF for risk of long-term adverse outcomes after hospitalization with HF. Methods Patients from American Heart Association’s Get With The Guidelines – Heart Failure registry enrolled between 1/1/2006 – 12/31/2014 with age ≥ 65 years with available 5-year follow-up data, ascertained through linkage with Medicare fee-for-service Part A administrative claims, were included. HF subtypes included HF with reduced EF (HFrEF) with EF ≤ 40%, HF with mildly reduced EF (HFmrEF) with EF 41-49%, and HF with preserved EF (HFpEF) with EF ≥ 50%. Sex differences in 5-year all-cause mortality and readmission for each HF subtype were assessed using unadjusted cumulative incidence methods and adjusted Cox models. Median survival across HF subtypes was compared to median survival of U.S. adults. Results 155,670 patients (mean age 81 years, 53.4% females) were included. Male patients were younger and had a higher prevalence of prior myocardial infarction or coronary artery bypass graft surgery and were more likely to have HFrEF, while women were more likely to have history of hypertension and HFpEF. The median post-hospitalization survival of patients with HF was substantially lower than the age- and sex-specific U.S. life expectancy across each HF subtype (Figure 1).Patients with HF had high 5-year mortality rates (HFrEF male: 81.3%, female: 78.4%; HFpEF male: 80.5% vs female 79.5%). In adjusted analysis, female (vs. male) patients had a significantly lower 5-year mortality risk (HR [95%CI]: 0.89 [0.87 – 0.90], p<0.01) and a higher 5-year readmission risk (all-cause: 1.03 [1.02 – 1.04]), CV: 1.05 [1.04 – 1.07]), HF: 1.06 [1.04 – 1.08], p<0.01 for each). HF subtype modified the association between sex and 5-year outcomes (pinteraction <0.05 for mortality and CV and HF readmission), with the greatest risk reduction of mortality for female vs. male patients with HFrEF and the greatest risk increase of readmission (CV and HF) among female vs. male patients with HFmrEF and HFpEF (Figure 2). Conclusion Among patients with HF, the overall survival post-HF hospitalization is very low for each HF subtype. Female patients have a lower 5-year mortality risk but a higher risk of HF or CV readmission regardless of EF.Figure 1Figure 2
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".