Establishment and Evaluation of Influencing Factors and Risk Prediction Model of Severe Neonatal Hyperbilirubinemia Complicated with Acute Bilirubin Encephalopathy.

Abstract

Objective To explore the influencing factors of severe hyperbilirubinemia in neonates complicated with acute bilirubin encephalopathy (ABE) and then build relevant prediction models and evaluate the prediction performance of the models. Methods The data of 120 neonates with severe hyperbilirubinemia were collected by retrospective analysis. Univariate and multivariate analysis methods were used to analyze the data of 120 children. R software was used to visualize the results of multivariate analysis, and a nomogram model was obtained. The receiver operating characteristic curve (ROC), calibration curve, and decision-making curve (DC) were used to evaluate the discrimination, accuracy, and clinical net profit rate of the model. Results Multivariate analysis showed that nonfull breastfeeding, high-risk symptoms, and pregnancy complications were independent risk factors for ABE in neonates with severe hyperbilirubinemia. At the same time, the risk of ABE in neonates with severe hyperbilirubinemia increased with the increase of B/A and Hb levels. The ROC curve showed that the area under the curve for the model was 0.908 (95% CI: 0.839–0.960). The calibration curve shows that the actual prediction curve of the model is in good agreement with the corrected prediction curve. Using the cutoff value of the ROC curve as the diagnostic criterion, the threshold probability of the model was calculated to be 38%. The decision curve shows that when 38% is used as the basis for judging whether to take measures to intervene, the profit rate is 61%. Conclusion The occurrence of ABE in neonates with severe hyperbilirubinemia is affected by many factors, and there is a certain degree of interaction between these factors. Combining multiple factors to construct a risk nomogram model can provide a reference for early clinical detection of high-risk neonates.