关键词: 新生儿急性胆红素脑病, 诊断模型, 列线图, 多模态组合预测, 风险重新分类

Abstract:

Objective To investigate the risk stratification capability and diagnostic performance of a combined clinical， radiological， and electrophysiological model in neonatal acute bilirubin encephalopathy （ABE）. Methods We retrospectively analyzed clinical data， auditory brainstem response （ABR） parameters， amplitude-integrated electroencephalography （aEEG） findings， and cranial magnetic resonance imaging （MRI） features from 275 neonates with hyperbilirubinemia. The neonates were randomly allocated to a training cohort （n = 193） and a validation cohort （n = 82） in a 7∶3 ratio. Independent risk factors identified through univariate and multivariable logistic regression analyses were incorporated to construct a combined predictive nomogram. Model performance was evaluated using the area under the receiver operating characteristic （ROC） curve （AUC）， the Hosmer-Lemeshow （HL） goodness-of-fit test， calibration curves， and decision curve analysis （DCA） to assess discrimination， goodness-of-fit， calibration， and clinical utility， respectively. DeLong’s test was used to compare the AUC of the combined model with those of the individual predictors. Furthermore， the net reclassification improvement （NRI） was calculated to evaluate the incremental predictive value of the combined model. Results Glucose-6-phosphate dehydrogenase （G-6-PD） deficiency， abnormal ABR findings， and the globus pallidus/putamen （G/P） MRI signal intensity ratio were identified as independent risk factors and incorporated into the model. In the training cohort， the combined model achieved an AUC of 0.968， significantly outperforming any individual predictor （all P < 0.05）. In the validation cohort， the model maintained a high AUC of 0.945. Additionally， NRI analysis demonstrated that the combined model significantly improved risk reclassification （NRI = 1.25， P < 0.001）. The HL test （P > 0.05）， calibration curves， and DCA confirmed that the model exhibited satisfactory goodness-of-fit， excellent calibration， and favorable clinical utility in both cohorts. Conclusions The multimodal clinical-radiological-electrophysiological model significantly improves both diagnostic performance and risk stratification capabilities in ABE. It provides a reliable and objective tool for the early identification of high-risk neonates and the formulation of personalized clinical management strategies.

Key words: neonatal acute bilirubin encephalopathy, diagnostic model, nomogram, multimodal combined prediction, risk reclassification