Factors Influencing Thirty-Day Readmission Rate in Patients With Heart Failure Exacerbation
Bibliographic record
Abstract
Background: The purpose of this study is to further investigate the leading causes of readmission at 30 days in heart failure exacerbation patients, along with associations to mortality and intensive care unit (ICU) admissions. Methods: A retrospective data analysis was performed on a total of 33,400 patients between January 1, 2016, and December 31, 2020. The primary endpoints were to determine whether guideline-directed medical therapy (GDMT), length of stay, and time to first diuretic affect readmission rates. Secondary endpoints include time to first chest X-ray, time to first echocardiogram, administration of intravenous fluids, diet, presence of cardiology consult, and ICU admission. Results: Patients who received GDMT had decreased likelihood of mortality (odds ratio (OR): 0.518; 95% confidence interval (CI): 0.394 - 0.682; P < 0.001). Patients who had an echocardiogram done within 1 day of admission had less likelihood of death (OR: 0.606; 95% CI: 0.483 - 0.759; P < 0.001). In addition, patients who had a cardiac diet during their hospitalization were 0.632 times less likely to experience mortality (95% CI: 0.502 - 0.797; P < 0.001). Patients that received their first intravenous diuretic 2 h or more after admission were 1.290 times as likely to be readmitted within 30 days (95% CI: 1.018 - 1.634; P = 0.035). In addition, patients that did not receive intravenous diuretics were even more likely to be readmitted within 30 days (OR: 1.555; 95% CI: 1.237 - 1.955; P < 0.01). Patients who were treated with GDMT had a decreased chance of being readmitted within 30 days (OR: 0.781; 95% CI: 0.647 - 0.944; P = 0.01). Conclusions: This study stresses the importance of initiating GDMT, cardiac diet, diuretics, and echocardiogram in timely manner.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".