Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerative hierarchical clustering on principal components. Cluster validation was performed by cluster stability assessment with bootstrapping method. A total of 215 newborns (median gestational age 27 (26-29) weeks) were included in the final analysis. Six clusters with different clinical and laboratory characteristics were identified: the "Mature" (Cluster 1; n = 60, 27.9%), the mechanically ventilated with "adequate ventilation" (Cluster 2; n = 40, 18.6%), the mechanically ventilated with "poor ventilation" (Cluster 3; n = 39, 18.1%), the "extremely immature" (Cluster 4; n = 39, 18.1%%), the neonates requiring "Intensive Resuscitation" in the delivery room (Cluster 5; n = 20, 9.3%), and the "Early septic" group (Cluster 6; n = 17, 7.9%). In-hospital mortality rates were 11.7%, 25%, 56.4%, 61.5%, 45%, and 52.9%, while severe intraventricular hemorrhage rates were 1.7%, 5.3%, 29.7%, 47.2%, 44.4%, and 28.6% in clusters 1, 2, 3, 4, 5, and 6, respectively (p < 0.001).

Conclusion

Our cluster analysis in extremely preterm infants was able to characterize six distinct phenotypes. Future research should explore how better phenotypic characterization of neonates might improve care and prognosis. What is Known: • Patient heterogeneity is becoming more acknowledged as a cause of clinical trial failure. • Machine learning algorithms can find patterns within a heterogeneous group. What is New: • We identified six different phenotypes of extremely preterm infants who exhibited distinct clinical and laboratorial characteristics.