Developing and validating an explainable digital mortality prediction tool for extremely preterm infants.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41370313.
- Also identified by DOI 10.1371/journal.pdig.0000955 and PMC identifier 12694798.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Decision-making in perinatal management of extremely preterm infants is challenging. Mortality prediction tools may support decision-making. We used population-based routinely entered electronic patient record data from 25,902 infants born between 23+0-27+6 weeks' gestation and admitted to 185 English and Welsh neonatal units from 2010-2020 to develop and internally validate an online tool to predict mortality before neonatal discharge. Comparing nine machine learning approaches, we developed an explainable tool based on stepwise backward logistic regression (https://premoutcome.shinyapps.io/Death/). The tool demonstrated good discrimination (area under the receiver operating characteristics curve (95% confidence interval) of 0.746 (0.729-0.762)) and calibration with superior net benefit across probability thresholds of 10%-70%. Our tool also demonstrated superior calibration and utility performance than previously published models. Acceptable performance was demonstrated in a multinational, external validation cohort of preterm infants. This tool may be useful to support high-risk perinatal decision-making following further evaluation.