Long-Term Healthcare Utilization and Outcomes in Patients Hospitalized for Heart Failure With and Without Atrial Fibrillation
Bibliographic record
Abstract
The long-term association between AF and clinical outcomes, healthcare resource utilization, and healthcare costs among patients with HF remains underexplored. We conducted an exploratory analysis of 5-year outcomes among patients enrolled in the patient-centered care transitions in HF (PACT-HF) stepped-wedge cluster randomized trial who were hospitalized for HF and discharged alive between February 2015 and March 2016. Patients were stratified by baseline AF status. Administrative health databases were linked to assess mortality, rehospitalizations, emergency department visits, healthcare utilization, and costs. Mortality was analyzed using Cox proportional hazards models adjusted for baseline comorbidities. Healthcare utilization and cost differences were assessed using Wilcoxon and generalized linear models. Among 4,441 patients, 2,151 patients (48.4%) had AF at baseline. Patients with AF were older and had a higher prevalence of hypertension, stroke, and vascular disease. Patients with AF had a shorter life span (mean [SD] days alive 957.9 [697.8] vs 1,119.6 [698.2], p <0.01) and a higher 5-year all-cause mortality (adjusted HR 1.09 95% CI 1.01 to 1.17; p = 0.03) relative to those without AF. Patients with AF experienced more all-cause rehospitalizations (mean [SD] 3.2 [8.3] vs 2.6 [7.9], p = 0.03) and longer hospital length of stays (mean [SD] days 29.6 [48.6] vs 24.8 [46.9], p <0.01), but no difference in HF rehospitalizations (mean [SD] 0.9 [4.5] vs 0.8 [3.6], p = 0.24) than those without AF. Annual healthcare costs were greater in the AF cohort (mean [SD] $83,748 [114,398] vs $77,792 [114,874]) CAD. In conclusion, despite only modestly increased mortality, patients with AF experienced substantially greater healthcare utilization and cost, largely unrelated to HF-specific care. These exploratory findings underscore the need for a multidisciplinary approach to reduce morbidity and optimize care delivery for patients with HF and comorbid AF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".