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

• Published in: Frontiers in pediatrics 2023-04, Vol.11, p.1097950-1097950
• Main Authors: Liu, Tai-Xiang, Zheng, Jin-Xin, Chen, Zheng, Zhang, Zi-Chen, Li, Dan, Shi, Li-Ping
• Format: Article
• Language: English
• Subjects: interpretable machine learning; nonsteroidal anti-inflammatory drugs; patent ductus arteriosus; Pediatrics; predictive model; preterm infant
• ISSN: 2296-2360; 2296-2360
• DOI: 10.3389/fped.2023.1097950

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. 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 performance was assessed by the area under the receiver operating characteristic curve (AUC). Variable-importance and marginal-effect plots were constructed to explain the predictive model. The model was validated using an external cohort of two preterm infants who received ibuprofen (p.o.) to treat hsPDA. Eighty-three cases (45.6%) were in the success group and 99 (54.4%) in the failure group. Infants in the success group were associated with maternal chorioamnionitis (  = 0.002), multiple births (  = 0.007), gestational age at birth (  = 0.020), use of indometacin (  = 0.007), use of inotropic agents (