Allison C Lure1, Xinsong Du2, Erik W Black3, Raechel Irons4, Dominick J Lemas2, Janice A Taylor5, Orlyn Lavilla4, Diomel de la Cruz4, Josef Neu4. 1. University of Florida College of Medicine, Department of Pediatrics, 1600 SW Archer Rd, Gainesville, FL 32610, United States. Electronic address: allison.lure@ufl.edu. 2. University of Florida College of Medicine, Department of Health Outcomes & Biomedical Informatics, 2004 Mowry Rd, Gainesville, FL 32610, United States. 3. University of Florida College of Medicine, Department of Pediatrics, 1600 SW Archer Rd, Gainesville, FL 32610, United States; University of Florida College of Education, 1221 SW 5th Ave, Gainesville, FL 32601, United States. 4. University of Florida College of Medicine, Department of Pediatrics, 1600 SW Archer Rd, Gainesville, FL 32610, United States. 5. University of Florida College of Medicine, Department of Surgery, 1600 SW Archer Rd, Gainesville, FL 32610, United States.
Abstract
PURPOSE: Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating diseases 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 etiology 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 learning 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 logistic 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 Paper.
PURPOSE:Necrotizing enterocolitis (NEC) and spontaneous intestinal perforation (SIP) are devastating diseases 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 etiology 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 learning 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 logistic 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 Paper.