Abstract 4141460: Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements

Introduction: Neonatal hypothermia (< 36.5 o C) after CPB is considered benign despite lack of evidence on its prognostic significance. Question: Are group-based trajectory modeling (GBTM), k-means, and self-organizing map (SOM) clustering approaches clinically useful for evaluating their associations with important outcomes? Aims: Identify distinct postoperative temperature trajectories in neonates after CPB using novel machine learning (ML) clustering methods, corroborate findings, and evaluate prognostic value on outcomes. Methods: Secondary cohort analysis of prospectively collected data. Accessed data from a single pediatric referral center's CardioAccess registry consistent of postoperative neonates with congenital heart defects (CHD). 48-hourly postoperative temperature measurements were extracted from patient records. GBTM, SOM, and k-means clustering identified cluster membership and model fit was optimized for 3 clusters based on existing knowledge. The primary outcome was a complication composite of severe bleeding, illness severity, cardiac arrest, arrhythmia, delayed first successful extubation, prolonged ICU LOS, very poor weight gain, and 30-day mortality. 3 data driven techniques were compared and then associated with outcomes using adjusted multivariable binary logistic regression (LR). Results: 450 neonates ≥ 34 weeks gestation undergoing CPB between 2015 and 2019 met inclusion criteria. GBTM, SOM, and k-means clustering identified temperature group assignment for 3 clusters: 1) persistent hypothermia (n=38, 9% vs. n=49, 11% vs. n=40, 9%), 2) resolving hypothermia (n=233, 51% vs. n=227, 50% vs. n=147, 33%), and 3) normothermia (n=179, 40% vs. n=174, 39% vs. n=263, 58%). Concordance of techniques showed strong agreement between GBTM and SOM (kappa= 0.92). The kappa test comparing GBTM and k-means assignment was 0.41, indicating a weak to moderate agreement. After adjustment in the multivariable binary LR, persistently hypothermic neonates compared to normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (OR: 2.8 [1.0-7.3], p 0.041) and SOM (OR: 2.3 [1.0-5.4], p 0.045) models, but not the k-means model (OR: 1.4 [0.7-3.1], p 0.38). Conclusion: Exploring concordance between different unsupervised ML techniques, shows that neonatal temperature after CPB follows distinct postoperative trajectories, and those exhibiting a persistent hypothermia trend are at higher risk of adverse outcomes.