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

<div><p>Background</p><p>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.</p><p>Study design</p><p>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.</p><p>Results</p><p>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.</p><p>Algorithm availability</p><p><a href="http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl" target="_blank">http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl</a> and smartphone application upon request.</p></div

A decision tree to guide the manual assignment of the modified Bell’s staging criteria to the study subjects.

<p>A decision tree to guide the manual assignment of the modified Bell’s staging criteria to the study subjects.</p

Clinical variable’s contribution (LD1) to the NEC outcome LDA model.

<p>LDA: Linear discriminant analysis. LD1: first discriminant variable.</p

NEC outcome predictive LDA models with reduced number of variables (listed in descending order from right to left in Figure 3 by the absolute value of their weights).

<p>The model performance was gauged by ROC analysis. Vertical dotted line: the model performance deteriorates when the model’s panel size is less than 7 parameters.</p

NEC outcome predictive results.

<p>A. ROC AUC analysis. To gauge the impact of different training/testing cohort partition on the statistical learning, we performed a bootstrapping analysis that randomly partitioned the cohorts into 100 different training/testing sets. The distribution of 100 ROC curves, training and testing respectively, are illustrated. B. Use of the NEC outcome prediction metric to risk-stratify NEC subjects into low, intermediate and high risk groups.</p

Demographics of NEC patients by Bell’s staging criteria.

†<p>Chi-square test is used. N is reported with percentages in parentheses.</p>‡<p>Fisher's exact test is used. N is reported with percentages in parentheses.</p>§<p>Kruskal-Wallis test is used. Median is reported with IQR in parentheses.</p

Automated NEC staging assignment results.

<p>Left: modeling training. Right: blind testing. Bottom: manual versus automated NEC staging assignment comparative analysis. To gauge the impact of different training/testing cohort partition on the statistical learning, we performed a bootstrapping analysis that randomly partitioned the cohorts into 100 different training/testing sets. Results were summarized where median and interquartile range (IQR) values were calculated for each comparative category.</p