Key result
An at-admission pediatric readmission prediction model demonstrated comparable performance to standard at-discharge models, with SVM with Polynomial Kernel outperforming other algorithms.
Why the study?
Most pediatric readmissions occur shortly after discharge, leaving limited time to plan interventions, yet existing readmission prediction models are restricted to post-discharge timing.
Does an at-admission readmission prediction model perform comparably to an at-discharge model in pediatric patients?
Observational
Yes
Does an at-admission readmission prediction model perform comparably to an at-discharge model in pediatric patients?
An at-admission pediatric readmission prediction model performs comparably to traditional at-discharge models, providing earlier opportunities for intervention planning.
Early identification of readmission risk may allow targeted interventions from day one; leaves open whether prospective implementation improves.
The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms-logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting-were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients' demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children's overall health.
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Symum et al. (2021) conducted an observational in Pediatric hospital readmission. At-admission readmission prediction model vs. Standard at-discharge readmission prediction model was evaluated on Model performance measured by the area under the curve (AUC) for 30-day readmission. An at-admission pediatric readmission prediction model demonstrated comparable performance to standard at-discharge models, with SVM with Polynomial Kernel outperforming other algorithms.
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