Roni Shouval

Patients who develop chimeric antigen receptor (CAR) T-cell-related severe cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) exhibit hemodynamic instability and endothelial activation. The EASIX (Endothelial Activation and Stress Index) score (lactate dehydrogenase [LDH; U/L] × creatinine [mg/dL]/platelets [PLTs; 109 cells/L]) is a marker of endothelial damage that correlates with outcomes in allogeneic hematopoietic cell transplantation. Elevated LDH and low PLTs have been associated with severe CRS and ICANS, as has C-reactive protein (CRP), while increased creatinine is seen only in a minority of advanced severe CRS cases. We hypothesized that EASIX and 2 new modified EASIX formulas (simplified EASIX, which excludes creatinine, and modified EASIX [m-EASIX], which replaces creatinine with CRP [mg/dL]), calculated peri-CAR T-cell infusion, would be associated with development of severe (grade ≥ 3) CRS and ICANS. We included 118 adults, 53 with B-acute lymphoblastic leukemia treated with 1928z CAR T cells (NCT01044069) and 65 with diffuse large B-cell lymphoma treated with axicabtagene ciloleucel or tisagenlecleucel. The 3 formulas showed similar predictive power for severe CRS and ICANS. However, low PLTs and high CRP values were the only variables individually correlated with these toxicities. Moreover, only m-EASIX was a significant predictor of disease response. m-EASIX could discriminate patients who subsequently developed severe CRS preceding the onset of severe symptoms (area under the curve [AUC] at lymphodepletion, 80.4%; at day -1, 73.0%; and at day +1, 75.4%). At day +3, it also had high discriminatory ability for severe ICANS (AUC, 73%). We propose m-EASIX as a clinical tool to potentially guide individualized management of patients at higher risk for severe CAR T-cell-related toxicities.

PURPOSE: Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction. PATIENTS AND METHODS: This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data. RESULTS: OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; P<.001). Calibration was excellent. Scores assigned were also predictive of secondary objectives. CONCLUSION: The alternating decision tree model provides a robust tool for risk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT.