ArticleMayo Clinic proceedings. Digital health2025
Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To identify distinct postoperative temperature trajectories in neonates with congenital heart defects after cardiopulmonary bypass (CPB), using advanced unsupervised machine learning clustering methods, corroborate findings, and evaluate their prognostic value on outcomes. Patients and Methods: A secondary cohort analysis of prospective data collected from a single pediatric referral center's CardioAccess data registry, consistent of neonates who underwent CPB between January 1, 2015, and January 1, 2019, was performed. Postoperative temperatures were extracted from medical records (48 hours). Group-based trajectory modeling (GBTM) performance was compared with self-organizing maps (SOM) and k-means clustering. Cluster membership and model fit were optimized for 3 temperature clusters per method. The primary outcome was a composite of postoperative complications. Clustering techniques were compared and associated with outcomes using adjusted multivariable binary logistic regression. Results: Neonates of ≥34 weeks' gestation underwent CPB ( Conclusion: Exploring concordance between different machine learning techniques shows that temperature in neonates after CPB follows unique trajectories. Those exhibiting persistent hypothermia trends are at higher risk of adverse outcomes.
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