Abstract 15017: Early Post-discharge Follow-up Delay After Hospitalization for Heart Failure With Preserved Ejection Fraction Reduces Readmission for Heart Failure in the Following Year
Bibliographic record
Abstract
Introduction: Early physician follow-up after hospital discharge for acute decompensated heart failure (ADHF) is recommended by the AHA to prevent early hospital readmission. This recommendation has not been specifically evaluated for heart failure with preserved ejection fraction (HFpEF). Hypothesis: Earlier follow-up should lead to decrease in readmissions for ADHF in the HFpEF population even when adjusting for confounding factors. Methods: Consecutive ADHF hospitalisation that occurred inclusively between 2015 and 2018 were reviewed. Main inclusion criterion was left ventricular ejection fraction ≥45%. The major exclusion criteria were: severe valvulopathy, hypertrophic cardiomyopathy, acute coronary syndrome 3 months before hospitalisation, chronic kidney failure (eGFR <30 ml/min), severe chronic respiratory disease and death before discharge. Follow-up delay after discharge was dichotomized (early vs late/no follow up) by using the median delay. Main outcome was hospital readmission in the year following discharge. Multivariate logistic regression was performed for main outcome according to follow-up delay and adjusted for age, sex, medication at discharge and major comorbid conditions. Results: A total of 163 heart failure readmission (37% of patients) occurred in 438 patients in the year following the first hospitalisation. Median readmission delay was of 62 days. Median dedicated follow-up delay was 30 days and was arranged in 68% of cases. After adjusting for confounding variables, early follow-up was significantly associated with fewer readmission (adjusted odds ratio 0.57, 95% CI; 0.34-0.97). Conclusions: Early dedicated follow-up after discharge for HFpEF was associated with fewer readmission over the year following discharge even when adjusting for major confounding variables.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".