• Daniel Vo, David Zurakowski, and David Faraoni.
• Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
• Paediatr Anaesth. 2018 Jan 1; 28 (1): 63-70.

BackgroundHospital readmissions are being used as a quality metric for hospital reimbursement without a clear understanding of the factors that contribute to readmission.ObjectiveThe 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.MethodsData 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.ResultsAmong 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.ConclusionChildren 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.© 2017 John Wiley & Sons Ltd.

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