Machine Learning Approaches to Predict Sepsis Risk in Multiple Myeloma Patients Treated with Bispecific Antibodies

Treatment of multiple myeloma (MM) with bispecific antibodies (bsAbs) results in an increased risk of infection, including infection related mortality; predicting this risk is a significant unmet need. We aimed to develop machine learning models to predict risk of infection in MM patients receiving bsAb therapy.

Clinical data was retrospectively collected in a multi-institutional cohort study (n=353), enrolling patients treated with at least one full dose of teclistamab or talquetamab. Using Python, AutoGluon-Tabular, and PyTorch, a range of machine learning approaches were developed considering infection and severe infection (CTCAE Grade ≥3) as binary problems. To avoid overfitting and address imbalanced data, we used k-fold bagging, automatic sample weighting, and out-of-fold predictions. Feature importance was assessed using SHapley Additive exPlanations.

We included a total of 353 patients - 195 (55%) were male, 258 (73%) were Caucasian, 69 (20%) were African American, 275 patients (78%) had high-risk disease at diagnosis, and 327 patients (93%) had triple-class refractory disease. A majority (81%, n=287) underwent prior autologous stem cell transplantation (ASCT). Patients treated with teclistamab were more likely to develop infection within 365 days as compared to talquetamab (n=98/201 [48.8%] vs. n=41/152 [27.0%], p=0.0002). Neural networks identified patients at risk of developing severe infection (grade ≥3) within 365 days of bsAb therapy with ROC/AUC of 0.78; LightGBMs identified patients at risk of developing severe infection within 90 days of bsAb therapy with ROC/AUC of 0.86; Stacked ensemble models identified patients at risk of developing severe infection within 90 days of bsAb therapy with ROC/AUC of 0.88 at risk of overfitting given cohort size. Cumulative dose, lymphocyte count, and number of prior ASCT had the largest impact on risk prediction.

To our knowledge, this is the first ML model that predicts infection risk within 90/365 days of initiating bsAbs for MM. Future research will focus on validating these findings in larger cohorts, and evaluating newer techniques.

Neural Network

Sepsis

Multiple Myeloma

Bispecific Antibodies 

AI and LLM Applications

Symposium on Data Science and Statistics (SDSS) 2026