Sex-Based Differences in Referral of Heart Failure Patients to Outpatient Clinics: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend that hospitalized patients newly diagnosed with HF be referred to an outpatient HF clinic (HFC) within 2 weeks of discharge. Our study aims were (i) to assess the current literary landscape on the impact of patient sex on HFC referral and outcomes and (ii) to provide a qualitative overview of possible considerations for the impact of sex on referral patterns and HF characteristics including aetiology, symptom severity, investigations undertaken and pharmacologic therapy. METHODS AND RESULTS: We conducted a scoping review using the Arksey and O'Malley framework and searched Medline, EMBASE, PsychINFO, Cochrane Library, Ageline databases and grey literature. Eligible articles included index HF hospitalizations or presentations to the Emergency Department (ED), a description of the HFC referral of patients not previously followed by an HF specialist and sex-specific analysis. Of the 11 372 potential studies, 8 met the inclusion criteria. These studies reported on a total of 11 484 participants, with sample sizes ranging between 168 and 3909 (25.6%-50.7% female). The included studies were divided into two groups: (i) those outlining the referral process to an HFC and (ii) studies which include patients newly enrolled in an HFC. Of the studies in Group 1, males (51%-82.4%) were more frequently referred to an HFC compared with females (29%-78.1%). Studies in Group 2 enrolled a higher proportion of males (62%-74% vs. 26%-38%). One study identified independent predictors of HFC referral which included male sex, younger age, and the presence of systolic dysfunction, the latter two more often found in males. Two studies, one from each group reported a higher mortality amongst males compared with females, whereas another study from Group 2 reported a higher hospitalization rate amongst females following HFC assessment. CONCLUSIONS: Males were more likely than females to be referred to HFCs after hospitalization and visits to the Emergency Department, however heterogeneity across studies precluded a robust assessment of sex-based differences in outcomes. This highlights the need for more comprehensive longitudinal data on HF patients discharged from the acute care setting to better understand the role of sex on patient outcomes.
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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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".