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Developing a resiliency model for survival without major morbidity in preterm infants

Authors :
Martina A. Steurer
Kelli K. Ryckman
Rebecca J. Baer
Jean Costello
Scott P. Oltman
Charles E. McCulloch
Laura L. Jelliffe-Pawlowski
Elizabeth E. Rogers
Source :
Journal of perinatology : official journal of the California Perinatal Association, vol 43, iss 4
Publication Year :
2023
Publisher :
eScholarship, University of California, 2023.

Abstract

Objective Develop and validate a resiliency score to predict survival and survival without neonatal morbidity in preterm neonates Study design Models using maternal, perinatal, and neonatal variables were developed using LASSO method in a population based Californian administrative dataset. Outcomes were survival and survival without severe neonatal morbidity. Discrimination was assessed in the derivation and an external dataset from a tertiary care center. Results Discrimination in the internal validation dataset was excellent with a c-statistic of 0.895 (95% CI 0.882–0.908) for survival and 0.867 (95% CI 0.857–0.877) for survival without severe neonatal morbidity, respectively. Discrimination remained high in the external validation dataset (c-statistic 0.817, CI 0.741–0.893 and 0.804, CI 0.770–0.837, respectively). Conclusion Our successfully predicts survival and survival without major morbidity in preterm babies born at

Details

Database :
OpenAIRE
Journal :
Journal of perinatology : official journal of the California Perinatal Association, vol 43, iss 4
Accession number :
edsair.doi.dedup.....1b7ff31fa362621ffce18ba8a3594d4a