时间序列聚类捕捉可被树模型预测的早期免疫效应细胞相关血液毒性模式

Immune 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.

We 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%).

Within 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 v 0.034; average silhouette width: 0.12 v 0.010). In addition, the RF model identified patients in the good recovery cluster with intermediate overall survival (hazard ratio [HR], 1.70 [95% CI, 1.05 to 2.74]; P = .029; reference, very good), which was not captured by grade 2 eICAHT (HR, 1.37 [95% CI, 0.80 to 2.35]; P = .25; reference, grade 0-1).

Unsupervised 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.