Abstract P180: Hospital Readmission After Pediatric Congenital Heart Surgery: A Systematic Review And Meta-analysis Of Potential Risk Factors
Bibliographic record
Abstract
Objective: Over 40,000 infants are born annually with a heart defect; 25% require surgery and of those 20% result in hospital readmissions. We sought to identify risk factors for short- and long-term readmission following pediatric congenital heart surgery (CHS) to reduce avoidable future admissions. Methods: A systematic approach was used to search four electronic databases and retrieve articles published through 05/2020. We included observational and experimental studies that observed factors associated with 30-day or 1-year readmission after CHS. Studies with a composite outcome of readmission and death were excluded. For each independent risk factor, we assessed the pooled effect size and heterogeneity using a random-effects model. Risk of bias was assessed via the Newcastle-Ottawa scale. Results: After removing 970 duplicates, we screened 5,084 studies; 17 were included in the systematic review and 15 (N= 82,794; 9,856 readmitted) in the meta-analysis. Hospital readmission was significantly and positively associated with gestational age, non-white race, Hispanic ethnicity, government insurance, genetic abnormality, renal dysfunction, failure to thrive, mechanical ventilation, intraoperative ventricular dysfunction, RACHS score, STAT mortality score, cross clamp time, gastroesophageal reflux disease, postoperative arrhythmia, valve regurgitation, feeding difficulties, and ICU and hospital length of stay (LOS). Readmission definition (i.e., 1-yr vs 30-day) and LOS dichotomization (i.e., ≥ 10 or ≥ 14) resulted in significant subgroup differences for age at surgery and LOS. Five studies had higher potential for risk of bias. Conclusions: This is the first meta-analysis to identify patient and clinical factors associated with short and long-term readmission after pediatric CHS. Findings may support clinical decisions before undergoing surgery and identify patients that may benefit from receiving more aggressive care transitions prior to discharge to reduce avoidable hospital readmissions.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".