Distinct clinical patterns of preterm infants vulnerable to intestinal injury identified by unsupervised clustering.

BACKGROUND: Current necrotizing enterocolitis (NEC) diagnostic criteria fail to capture the full spectrum of intestinal injury. We aimed to identify diagnosis-independent clinical patterns of intestinal injury among vulnerable very low birth weight (VLBW) infants using unsupervised hierarchical clustering. METHODS: Using prospectively collected data from 20,352 VLBW infants in the Korean Neonatal Network (2013-2022), unsupervised clustering was performed after excluding variables reflecting prematurity and NEC diagnosis. RESULTS: Eight primary clusters (C1-C8) were identified from the dataset without NEC-related labels. Cluster 8, which exhibited the highest prevalence of previously diagnosed NEC ≥ stage 2, was subdivided into five distinct clinical subclusters (SC23-SC27) based on feature importance: antenatal antibiotic exposure (SC23), assisted reproduction (SC24), pregnancy-induced hypertension (SC25), chorioamnionitis/PPROM (SC26), and severe postnatal morbidities (SC27). In terms of hierarchical structure validation, survival patterns across clusters showed strong concordance between Kaplan-Meier estimates and supervised survival model predictions. CONCLUSION: Unsupervised clustering successfully identified distinct clinical features among preterm infants vulnerable to intestinal injury, independent of traditional diagnostic labels. These nuanced clusters provide a valuable framework for developing a novel, more comprehensive taxonomy of intestinal injuries in this high-risk population. IMPACT: The newly proposed framework of intestinal injury in very low birth weight infants comprises clinically distinct phenotypes that are not adequately captured by diagnosis-based NEC classification. Using unsupervised hierarchical clustering in a large multicenter cohort, this study identifies diagnosis-independent phenotypic structure underlying neonatal intestinal injury. These findings reinforce the heterogeneity of intestinal injuries. They provide more nuanced clusters derived using unsupervised machine learning from a large database of patients and provide a framework for the development of more precise taxonomies in preterm infants that obviate the need for the misnomer currently termed NEC.