Incidence and predictors of 30‐day postoperative readmission in children
Bibliographic record
Abstract
BACKGROUND: Hospital readmissions are being used as a quality metric for hospital reimbursement without a clear understanding of the factors that contribute to readmission. OBJECTIVE: The objective of this study was to report the incidence of 30-day postsurgical readmission in children, identify the predictors for readmission, and create an algorithm to identify high-risk children. METHODS: Data from the 2012-2014 Pediatric database of the American College of Surgeons National Surgical Quality Improvement Program were analyzed using univariable and multivariable logistical regression analysis. RESULTS: Among 182 589 children included in the 2012-2014 American College of Surgeons National Surgical Quality Improvement Program Pediatric database, 4.8% (8815/182 589) experienced a readmission within 30 days. Four significant predictors were retained in the multivariable logistic regression model: American Society of Anesthesiologists physical status ≥ 3 (OR: 1.9, 95% CI: 1.8-2.0), presence of congenital heart disease (OR: 1.66, 95% CI: 1.31-2.11), inpatient status at time of surgery (OR: 3.5, 95% CI: 3.3-3.7), and at least 1 postoperative complication (neurologic, renal, wound, cardiac, bleeding, or pulmonary) (OR: 3.14, 95% CI: 2.92-3.34). The multivariable logistic regression model showed reasonably good discrimination in predicting 30-day readmissions with receiver operating characteristic area under the curve of 0.747 (95% CI: 0.73-0.75) and good calibration (Brier score: 0.044). We created a predictive algorithm of 30-day readmission based on the 4 significant predictors. CONCLUSION: Children with congenital heart disease, high American Society of Anesthesiologist physical class, inpatient status, and at least 1 postoperative complication of any kind are at high risk for postsurgical readmissions. We provide an algorithm for quantifying this risk with the goal of reducing the number of readmissions, improving the care of patients with complex chronic illnesses, and reducing hospital costs.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".