Abstract Prognostic indices such as the CLL International Prognostic Index (CLL-IPI) and the International Prognostic Score for Early-stage CLL (IPS-E) can predict time to first treatment (TTFT) in patients with early-stage CLL. Their ability to predict risk of infection - a leading contributor to morbidity and mortality in CLL - remains uncertain. The CLL-TIM is a machine-learning model developed in Europe that integrates clinical, laboratory, and infection-related data to predict TTFT or infection risk within 2-years of diagnosis, Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) 0.74; Agius et al., Nat Comm 11, 363 2020. We conducted the first validation of CLL-TIM in a US cohort of newly diagnosed CLL patients and compared its performance with the CLL-IPI and IPS-E indices. Adults with newly diagnosed CLL (2000-2020) were identified through the Rochester Epidemiology Project using International Classification of Disease (ICD) codes; all diagnoses were confirmed. We replicated the variable selection from the original CLL-TIM model and applied it to our cohort. The primary endpoint was a 2-year composite of either TTFT or incident infection (defined as having blood cultures drawn). Model performance was evaluated using ROC-AUC consistent with the original CLL-TIM methods. CLL-IPI and IPS-E performance was assessed using time-dependent ROC-AUC; pairwise differences in discrimination were tested using DeLong. We identified 454 CLL patients with a median age of 72 years range, 30-97, 166 (37%) were female, Rai Stage was 0-II in 399 (92%) patients. IGHV genes were unmutated in 130/305 (42%) patients and TP53 disruption (either del17p by FISH or TP53 mutation) was present in 24/329 (8%) patients. At 2-years, 55 (12%) patients received CLL therapy, and 56 (12%) patients had an infection. The 2-year composite endpoint was observed in 94 (21%) patients. The ROC-AUC was 0.74 (95% CI 0.68-0.80) for CLL-TIM, 0.69 for CLL-IPI (95% CI 0.63-0.75, p=0.11 compared to CLL-TIM) , and 0.68 for IPS-E (95% CI 0.62-0.74, p=0.02 compared to CLL-TIM). We also evaluated the ROC-AUC using all the prognostic models for each individual endpoints. The corresponding ROC-AUC for TTFT at 2 years were 0.79, 0.74, and 0.76 for CLL-TIM, CLL-IPI, and IPS-E, respectively; and for infection at 2 years were 0.68, 0.57, and 0.52, respectively. This is the first validation of the CLL-TIM in a US-based cohort of newly diagnosed CLL patients. Although the CLL-TIM model exceeded the minimally meaningful discrimination threshold (AUC 0.7) for individual patient-level risk prediction, there was no statistical difference in AUC between CLL-TIM and CLL-IPI, suggesting either model can predict the composite endpoint at 2 years. Most of the observed discrimination was driven by prediction of TTFT rather than infection, underscoring the need to develop more infection-specific risk models for newly diagnosed CLL. Citation Format: Raphael Mwangi, Tait D. Shanafelt, Soren Basnet, Emily L. West, Owen Keegan, Timothy G. Call, Yao Yuan, Bryan Alexis Vallejo, Paul J. Hampel, Lindsey E. Roeker, Yucai Wang, Saad J. Kenderian, Sara J. Achenbach, Aaron D. Norman, Kari G. Rabe, Neil E. Kay, James R. Cerhan, Curtis A. Hanson, Susan L. Slager, Sameer A. Parikh. Validation of the CLL treatment infection model (CLL-TIM) in patients with newly diagnosed chronic lymphocytic leukemia (CLL) abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4228.
Mwangi et al. (Fri,) studied this question.