A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis

• Published in: PloS one Vol. 9; no. 2; p. e89860
• Main Authors: Ji, Jun, Ling, Xuefeng B., Zhao, Yingzhen, Hu, Zhongkai, Zheng, Xiaolin, Xu, Zhening, Wen, Qiaojun, Kastenberg, Zachary J., Li, Ping, Abdullah, Fizan, Brandt, Mary L., Ehrenkranz, Richard A., Harris, Mary Catherine, Lee, Timothy C., Simpson, B. Joyce, Bowers, Corinna, Moss, R. Lawrence, Sylvester, Karl G.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 28.02.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology; Birth weight; Colleges & universities; Computer science; Consortia; Data processing; Development and progression; Diagnosis; Diagnostic systems; Discriminant analysis; Disease; Enterocolitis; Enterocolitis, Necrotizing - diagnosis; Enterocolitis, Necrotizing - pathology; Epidemiology; Female; Gangrene; Gastrointestinal diseases; Health aspects; Health risks; Hospitals; Humans; Infant, Newborn; Infants; Laboratories; Learning algorithms; Machine learning; Male; Medical diagnosis; Medical prognosis; Medicine; Morbidity; Mortality; Necrosis; Necrotizing enterocolitis; Neonates; Newborn babies; Pediatrics; Process controls; Prognosis; Risk; Smartphones; Surgery; Training; Urine; Ohio; California; United States > US; Texas; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0089860

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.