Predicting Same Hospital Readmission following Fontan Cavopulmonary Anastomosis using Machine Learning

Abstract

Hospital readmission after third-stage palliation for single ventricle physiology (Fontan cavopulmonary anastomosis) approaches 25%. The cause for readmissions is varied, and there is no clear way to predict those at risk for readmission. If an effective predictive model can be used, physicians can preempt readmission in either the inpatient or outpatient setting. In this study, we incorporated electronic health records (EHRs), such as vital signs, administered medications, laboratory results, and demographic information, to determine whether a patient will require readmission after undergoing Fontan surgery. The EHR data were extracted for 338 patients admitted to the Children’s Healthcare of Atlanta from May 2009 to August 2020 for Fontan surgery. 38.8% of the patients were readmitted. Machine learning models, such as extreme gradient boosting, random forest, decision tree, and logistic regression, were employed to make these predictions. The random forest classifier outperformed the rest of the models and achieved 89.6% accuracy [82.1%, 95.5%], 77.8% sensitivity [63.0%, 92.6%], 97.5% specificity [90%, 100%], 95.2% positive predictive value [83.7%, 100%], 86.7% negative predictive value [78.7%, 94.9%], 82.2% area under precision-recall curve [69.8%, 92.7%], 87.0% area under receiver operating characteristic [78.4%, 94.4%], and 85.1% F1 score [72.7%, 94.1%].

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