Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: A novel predictive analytic tool

Purpose: Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating dis-eases in preterm neonates, often requiring surgical treatment. Previous studies evaluated outcomes in peritoneal drain placement versus laparotomy, but the accuracy of the presumptive diagnosis remains unknown without bowel visualization. Predictive analytics provide the opportunity to determine the eti-ology of perforation and guide surgical decision making. The purpose of this investigation was to build and evaluate machine learning models to differentiate NEC and SIP. Methods: Neonates who underwent drain placement or laparotomy NEC or SIP were identified and grouped definitively via bowel visualization. Patient characteristics were analyzed using machine learn-ing methodologies, which were optimized through areas under the receiver operating characteristic curve (AUROC). The model was further evaluated using a validation cohort. Results: 40 patients were identified. A random forest model achieved 98% AUROC while a ridge logis-tic regression model reached 92% AUROC in differentiating diseases. When applying the trained random forest model to the validation cohort, outcomes were correctly predicted. Conclusions: This study supports the feasibility of using a novel machine learning model to differentiate between NEC and SIP prior to any intended surgical interventions. Level of Evidence: level II Type of Study: Clinical Research Pape (c) 2020 Elsevier Inc. All rights reserved.