Predicting risk factors for postoperative intestinal stenosis in neonates with necrotizing enterocolitis: development...

The aim of this study was to develop and validate an intestinal stenosis prediction model for postoperative newborns with neonatal necrotizing enterocolitis (NEC).

Clinical information was collected on neonates who had undergone anastomosis or enterostomy because of NEC. The least absolute shrinkage and selection operator regression was applied to identify risk factors included in the model for postoperative intestinal stenosis. Multivariate logistic regression analysis was used to develop a predicting model regression based on the selected variables. Then internal validation was assessed using the bootstrapping validation. The accuracy and applicability of the model are assessed by C-index, calibration and decision curve.

Predictors incorporated into the model were a weight on admission, hematochezia, duration of abnormal C-reactive protein, lactate, intestinal peristalsis vanish, operation methods and duration of surgery. The regression equation was logit (P) = -0.001X₁ + 1.566X₂ + 0.185X₃ + 0.304X₄ + 1.34X₅ - 2.932X₆ + 0.015X₇ - 3.193, where X₁ was weight on admission (g), X₂ was hematochezia (yes = 1, no = 0), X₃ was duration of abnormal C-reactive protein (days), X₄ was lactate (mmol/L), X₅ was intestinal peristalsis vanish (yes = 1, no = 0), X₆ was primary anastomosis (yes = 1, no = 0), X₇ was duration of surgery (min). The model displayed good discrimination with a C-index of 0.879 (0.827,0.932) by random sampling for 1000 times. The calibration curve excluded the overfitting performance, and the decision curve confirmed the clinical application capacity of the model.

This nomogram of intestinal stenosis incorporating the use of weight on admission, hematochezia, duration of abnormal C-reactive protein, lactate, intestinal peristalsis vanish, operation methods and duration of surgery could be conveniently used to facilitate the intestinal stenosis risk prediction in postoperative-NEC-patients.