Factors That Influence 7- and 30-day Readmissions After Heart Failure Hospitalization
Bibliographic record
Abstract
Patients with heart failure (HF) frequently return to hospital within days of discharge, yet contributing factors have not been fully explored. Hospitalizations place stress on the patient, family, and healthcare system, and require closer examination to determine potential avoidability and targets for intervention. Thus, current factors that influence readmissions after HF hospitalization in Alberta were examined. A two-phased case-control design was used to compare patients who were readmitted and not readmitted after hospitalization for HF. In Phase One, an 8-year period of hospital discharge abstract data was analyzed. The rate of unplanned all-cause readmission was 6% and 18% within 7 and 30 days respectively after discharge. After risk adjustment for age, sex, and year, all-cause readmission within 7 days after discharge was associated with having kidney disease, and readmission within 30 days was associated with having cancer, pulmonary, liver, and kidney disease. At both time intervals, discharge with homecare services was associated with increased risk of readmission, and discharge from a hospital with HF services was associated with lower risk of readmission. In Phase Two, a health record audit was undertaken for a more detailed examination of factors associated with readmission within 7 days of discharge and potential avoidability. Matched pairs of patients discharged from Calgary hospitals were identified from the Phase One sample. Patients who were frail or had a specialist as attending physician were more likely to be readmitted. Patients who were instructed to see a physician within 1 week of discharge were less likely to be readmitted. Common reasons for readmission included HF then gastrointestinal, other cardiac, and respiratory diagnoses. Almost 60% of readmissions were deemed potentially avoidable based on explicit criteria developed from past research. Several factors were associated with readmission within the 2 time intervals studied. Despite care by specialists and referral to HF clinics, complex frail patients were discharged with unresolved symptoms or inadequate community support. It is important that criteria be developed to screen for frailty, discharge readiness, and to determine avoidability.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".