Using Machine Learning Analysis to Assist in Differentiating between Necrotizing Enterocolitis and Spontaneous Intestinal Perforation: A Novel Predictive Analytic Tool
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In brief
In brief
Machine learning models achieved 92-98% accuracy in differentiating necrotizing enterocolitis from spontaneous intestinal perforation in preterm neonates prior to surgery. Random forest and ridge logistic regression algorithms analyzed patient characteristics to predict disease etiology, offering a novel tool to guide surgical decision-making between peritoneal drainage and laparotomy.
Written by the GCMD Library team from the article.
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