Work

Development and Validation of a Logistic-Bayes Method for In-Hospital Mortality Discrimination in Neonatal Heart Failure: A Real-World Study

Year: 2026

Type: article

Abstract: Background: Neonatal heart failure is a severe condition with high mortality, posing a significant burden on families and healthcare systems. Accurately discriminating whether a neonate with heart failure will die in hospital is essential for improving outcomes. Objective: To develop and validate a discriminant model for identifying neonates with heart failure who will experience in-hospital death. Methods: This real-world study included 600 neonates with heart failure treated at the First Affiliated Hospital of Xinjiang Medical University between 2012 and 2025. Demographic, clinical, perinatal, laboratory, and first-24-hour medication data were extracted. Candidate variables were screened by LASSO regression and between-group comparisons; eight discriminators retained by multivariable logistic regression were used to construct the Logistic-Bayes model, and the cohort was randomly split in a 7:3 ratio into training and internal validation sets. Results: The final discriminators were urea, fibrinogen, poor postnatal response, oliguria, cedilanid use, dopamine use, epinephrine use, and digoxin use. The model achieved area under the receiver operating characteristic curve (AUC) values of 0.87 (95% CI 0.82-0.91) in the training set and 0.86 (95% CI 0.78-0.92) in the internal validation set, with accuracy of 0.87 in both. Conclusion: On internal validation, the model demonstrated high specificity and high negative predictive value, supporting early identification of low-risk neonates with heart failure; prospective multicenter validation is required before routine clinical use.

Source: International Journal of General Medicine

DOI: 10.2147/ijgm.s627564