Abstract 17044: Gender Discrepancies in Management and Outcome of Hospitalized Heart Failure Patients
Bibliographic record
Abstract
Introduction: The incidence of heart failure (HF), particularly in women, is increasing rapidly. While earlier reports indicated that HF mortality was higher in men compared to women, recent studies have suggested higher HF mortality in women. The cause of this changing pattern is not well described. The purpose of this study was to explore different clinical factors and management patterns that could contribute to this. Methods: We conducted a retrospective chart review study of all the patients admitted to a tertiary academic hospital (Royal University Hospital, University of Saskatchewan, Canada) with a diagnosis of HF in 2015, with follow up analysis up to February 20, 2018. Results: In total, 379 patients were admitted with HF, of which 166 (43.8%) were women. Overall, the most important predictors of mortality on multivariate analysis include admission to non-cardiology services (NCS, p < 0.0001), age on admission (p<0.001), readmission (p=0.001), and haemoglobin on admission (P=0.004). Review of baseline characteristics showed that women with HF were older (p<0.001), and more likely to have HFpEF (26.3% vs. 46.4%, p<0.001) than men. However, women were less likely to have comorbidities including COPD, CKD, PVD, HTN, diabetes and dyslipidemia (p<0.001). In spite of this, there was a trend towards higher mortality among women over the follow up period (57.8% vs. 47.9%, p=0.055). Women were significantly less likely to be admitted to cardiology (62% vs. 71.4%, p=0.0084), and less likely to have follow up scheduled on discharge with either an internist or cardiologist (70.4% vs. 53.6%, p<0.001). Similar gender discrepancy in admission to cardiology was also observed among the subgroup of patients with HFrEF. Conclusions: In our study, women admitted with heart failure had worse overall prognosis than men, in spite of less associated comorbidities. Admission to cardiology service was the most significant positive prognostic factor overall. However, surprisingly, women were significantly less likely to be admitted to cardiology, possibly contributing to their observed poor outcome. Further studies to elucidate factors underlying this observed difference in admission pattern may help improve the management of women with HF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".