Time-Series Clustering Captures Patterns of Early Immune Effector Cell-Associated Hematotoxicity That Are Predictable Using Tree-Based Models.

purposeImmune effector cell-associated hematotoxicity (ICAHT) is a major cause of nonrelapse mortality after chimeric antigen receptor (CAR) T-cell therapy. We hypothesized that unsupervised time-series clustering could better identify archetypal patterns of early hematotoxicity compared to the early ICAHT (eICAHT) grading system.

methodsWe applied unsupervised k-means time-series clustering based on Euclidean distances to longitudinal absolute neutrophil count (ANC) data from days +0 through +30 post-CAR T-cell infusion in 691 patients treated at our center (training set: n = 483, 70%; test set: n = 208, 30%).

resultsWithin our training set, we identified an optimal cluster solution based on four ANC recovery clusters, which were labeled as very good, good, poor, and very poor. We trained a random forest (RF) model including the top five most important features (day +3, +4, +5, +26, and +27 ANC values) to predict the cluster assignments. Within our test set, we applied the RF model to predict cluster assignments. Compared with the eICAHT criteria, the RF-predicted clusters were more compact and better separated (Dunn index: 0.078

conclusionUnsupervised time-series clustering identified distinct and clinically relevant patterns of hematotoxicity after CAR T-cell therapy. We trained and tested an RF model that accurately predicted cluster assignments using only five features. Predictions can be generated using our online web application.