Identification and Prediction of Severe Hematologic Toxicity after CAR T-Cell Therapy Using Machine Learning-Based Time-Series Clustering

Severe hematologic toxicity is a significant complication associated with CAR T-cell therapy, leading to infections, transfusion dependency, and mortality. Using data from>400 CAR T-cell patients (pts), we hypothesized a time-series clustering-based approach could: i) automate the identification of pts with impaired absolute neutrophil count (ANC) recovery, ii) enable the identification of factors associated with ANC recovery, and iii) assess predictive models of hematologic toxicity after CAR T-cell therapy.

Source: Biology of Blood and Marrow Transplantation - February 1, 2024 Category: Hematology Authors: Emily C. Liang, Aya Albittar, Andrew J. Portuguese, Jennifer J. Huang, Natalie Wuliji, Qian Wu, Joseph De Los Reyes, Nikki Pin, Aiko Torkelson, Delaney R. Kirchmeier, Abigail Chutnik, Barbara S. Pender, Joshua A. Hill, Rahul Banerjee, Andrew J. Cowan, Dam Tags: 234 Source Type: research