Development and internal validation of a clinical prediction model for the needed level of care in preterm neonates

Abstract

Purpose: To address capacity problems at tertiary-level neonatal intensive care units (NICUs) within current staffing limitations, our study aims to demonstrate the feasibility of identifying very preterm neonates not in need of highly specialised, tertiary-level, NICU care. Methods: We developed and internally validated a clinical prediction model to identify very preterm neonates in need of tertiary-level NICU care within the first 72 h after birth in the Netherlands. The outcome was defined as one or more of: 1) endotracheal surfactant administration, 2) endotracheal/mechanical ventilation, and 3) inotropic administration. Multivariable logistic regression, with a priori selected predictors, was used on a retrospective cohort of very preterm neonates admitted to the tertiary-level NICU of Erasmus MC Sophia Children’s Hospital, between January 2018 and December 2022. Bootstrapping was used for internal validation. Results: Of 654 included neonates, 45.1% (n = 295) needed tertiary-level NICU care. The final model included six predictors. Evaluating the model’s discriminative performance resulted in an area under the receiver operating characteristics (ROC) curve of 0.77 [95%CI: 0.73–0.80]. A low-risk classification threshold of 20% yielded high sensitivity (93% [95%CI 90–96%]) and a specificity of 26% [95%CI: 22–31%], predicting a low risk of needing tertiary-level NICU care for 114 neonates, accurately selecting 94 of them. Conclusion: This prediction model demonstrates the feasibility of perinatal identification of very preterm neonates not in need of tertiary-level NICU care. Future research should focus on updating the model to a source population of women with imminent preterm birth.