An interpretable machine-learning model for predicting the efficacy of nonsteroidal anti-inflammatory drugs for closing hemodynamically significant patent ductus arteriosus in preterm infants.

ABSTRACT:

Background

Nonsteroidal anti-inflammatory drugs (NSAIDs) have been widely used in the closure of ductus arteriosus in premature infants. We aimed to develop and validate an interpretable machine-learning model for predicting the efficacy of NSAIDs for closing hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants.

Methods

We assessed 182 preterm infants ≤ 30 weeks of gestational age first treated with NSAIDs to close hsPDA. According to the treatment outcome, patients were divided into a "success" group and "failure" group. Variables for analysis were demographic features, clinical features, as well as laboratory and echocardiographic parameters within 72 h before medication use. We developed the machine-learning model using random forests. Model per

SUBMITTER: Liu TX 

PROVIDER: S-EPMC10110971 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature