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Necrotizing Enterocolitis:

Patient --   Postnatal age:  weeks   Gender:    Ethnicity: 

Historic factors --  *Feeding intolerance?  

physical exam --  Abdominal wall discoloration?  

Medical history --  Was on a ventilator on the day he/she met the protocol definition of NEC     Thrombocytopenia(Platelet count < 100)?  

Radiographic findings --  Pneumatosis intestinalis?     Portal venous gas?  

Labs --   pH value  

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Background: 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. We hypothesized that statistical learning of clinical patterns would lead to an improved algorithm for NEC diagnostic function and prognostic forecasting.
Study design: 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.
Results: 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.

*Note: Feeding intolerance is defined as the inability to digest enteral feedings presented as GRV more than 50%, abdominal distention or emesis of both, and the disruption of the patient's feeding plan.

© 2014 Bruce Ling, Ph.D.

For publication, please reference the Stanford NEC diagnostic and prognostic algorithms.

Email Bruce Ling and let him know if you have any suggestions.

Last Modified: 1/18/2014