Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.

Claudine Irles,Sandra Carrera Muiños,Anahid Martínez-Zepeda,Guadalupe Cordero González,Estibalitz Laresgoiti Servitje,Carolina Michel Macias,César Sánchez Gómez,Gabriela González-Pérez

doi:10.3390/ijerph15112509

Abstract

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R2 > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO2 and HCO3); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.

Highlights

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC), known as surgicalNEC, is one of the leading causes of death in preterm infants, with up to 30–50% mortality compared with only 21% in those medically treated for NEC [1,2,3,4,5]
Learning to estimate perforation depends on all variables from individual cases data were working together in a multidimensional process in order to obtain a pattern of forecasting, and allowing working together relations in a multidimensional process indetermined order to obtain a pattern of forecasting, and for the non-linear between variables to be during the learning process, make allowing for the non-linear relations between variables to be determined during the learning process, these artificial neural networks (ANN) models highly valuable for clinicians since prediction approaches personalized medicine
We found that male gender was a highly predictive parameter for intestinal perforation associated with NEC compared to only NEC

Summary

Introduction

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC), known as surgicalNEC, is one of the leading causes of death in preterm infants, with up to 30–50% mortality compared with only 21% in those medically treated for NEC [1,2,3,4,5]. Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC), known as surgical. Global prevalence of intestinal perforation due to NEC is 27–52% [6] with 90% of cases in premature infants [7,8,9,10,11]. Survivors are affected by significant complications and comorbidities, including sepsis, short bowel syndrome, adhesions, cholestasis and impaired neurodevelopment [14,15,16,17]. These evidences highlight the burden of intestinal perforation by NEC. Public Health 2018, 15, 2509; doi:10.3390/ijerph15112509 www.mdpi.com/journal/ijerph

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