Identifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learning

Sistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSPMATSUSHITA, Felipe YuKREBS, Vera Lucia JornadaCARVALHO, Werther Brunow de2024-02-152024-02-152023EUROPEAN JOURNAL OF PEDIATRICS, v.182, n.5, p.2173-2179, 20230340-6199https://observatorio.fm.usp.br/handle/OPI/57990To use unsupervised machine learning to identify potential subphenotypes of preterm infants with patent ductus arteriosus (PDA). The study was conducted retrospectively at a neonatal intensive care unit in Brazil. Patients with a gestational age < 28 weeks who had undergone at least one echocardiogram within the first two weeks of life and had PDA size > 1.5 or LA/AO ratio > 1.5 were included. Agglomerative hierarchical clustering on principal components was used to divide the data into different clusters based on common characteristics. Two distinct subphenotypes of preterm infants with hemodynamically significant PDA were identified: ""inflamed,"" characterized by high leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio, and ""respiratory acidosis,"" characterized by low pH and high pCO(2) levels. Conclusions: This study suggests that there may be two distinct subphenotypes of preterm infants with hemodynamically significant PDA: ""inflamed"" and ""respiratory acidosis."" By dividing the population into different subgroups based on common characteristics, it is possible to get a more nuanced understanding of the effectiveness of PDA interventions. What is Known: Treatment of PDA in preterm infants has been controversial. Stratification of preterm infants with PDA into subgroups is important in order to determine the best treatment. What is New: Unsupervised machine learning was used to identify two subphenotypes of preterm infants with hemodynamically significant PDA. The 'inflamed' cluster was characterized by higher values of leukocyte, neutrophil, and neutrophil-to-lymphocyte ratio. The 'respiratory acidosis' cluster was characterized by lower pH values and higher pCO2 values.engrestrictedAccessPatent ductus arteriosusPreterm infantsUnsupervised machine learningAgglomerative hierarchical clusteringSubphenotypesIdentifying two distinct subphenotypes of patent ductus arteriosus in preterm infants using machine learningarticleCopyright SPRINGER10.1007/s00431-023-04882-9Pediatrics1432-1076