Development and validation of predictive models of early immune effector cell–associated hematotoxicity - Emily C. Liang; Jennifer J. Huang; Andrew J. Portuguese; Valentín Ortiz-Maldonado; Aya Albittar; Natalie Wuliji; Ryan Basom; Yein Jeon; Qian Wu; Aiko Torkelson; Delaney Kirchmeier; Abigail Chutnik; et al. (Blood Advances)
Abstract: Immune effector cell–associated hematotoxicity (ICAHT) is associated with morbidity and mortality after chimeric antigen receptor (CAR) T-cell therapy. To date, the factors associated with ICAHT are poorly characterized, and there is no validated predictive model of ICAHT as defined by current consensus criteria. Therefore, we performed comprehensive univariate analyses to identify factors associated with severe (grade 3-4) early ICAHT (eICAHT) in 691 patients who received commercial or investigational CAR T-cell therapy for hematologic malignancies. In univariate logistic regression, preinfusion factors associated with severe eICAHT included disease type (acute lymphoblastic leukemia), prelymphodepletion (pre-LD) blood counts including absolute neutrophil count (ANC), lactate dehydrogenase (LDH), and inflammatory (C-reactive protein [CRP], ferritin, and interleukin-6 [IL-6]) and coagulopathy biomarkers (D-dimer). Postinfusion laboratory markers associated with severe eICAHT included early and peak levels of inflammatory biomarkers (CRP, ferritin, and IL-6), coagulopathy biomarkers (D-dimer), peak cytokine release syndrome grade, and peak neurotoxicity grade. We trained (n = 483) and validated (n = 208) 2 eICAHT prediction models (eIPMs): eIPMPre including preinfusion factors only (disease type and pre-LD ANC, platelet count, LDH, and ferritin) and eIPMPost containing both preinfusion (disease type and pre-LD ANC, platelet count, and LDH) and early postinfusion (day +3 ferritin) factors. Both models generated calibrated predictions and high discrimination (area under the receiver operating characteristic curve in test set, 0.87 for eIPMPre and 0.88 for eIPMPost), with higher net benefit in decision curve analysis for eIPMPost. Individualized predictions of severe eICAHT can be generated from both eIPMs using our online tool (available at https://eipm.fredhutch.org).