Machine Learning Approaches to Predict Infection Risk in Multiple Myeloma Patients Receiving Bispecific Antibody Therapy

Overview

Background: Treatment of Multiple Myeloma (MM) with bispecific antibodies (bsAb) results in an increased risk of infection, and predicting this risk is a significant unmet need. Aim: Develop machine learning (ML) models to predict risk of infection in MM patients receiving bsAb therapy. Methods: 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, ML models were developed considering infection and severe infection (= CTCAE Grade 3) as binary problems. To avoid overfitting and address imbalanced data, k-fold bagging, automatic sample weighting, and out-of-fold predictions were used. Feature importance was assessed using SHapley Additive exPlanations. Results: Patients treated with teclistamab were more likely to develop infection as compared to talquetamab (p=0.0001). Cumulative dose of bsAbs, number of prior ASCT, and absolute lymphocyte count had the largest impact on predicted risk. A neural network identified patients at risk of developing severe infection within 365 days of bsAb therapy with AUC of 0.78; a LightGBM identified patients at risk of developing severe infection within 90 days of bsAb therapy with AUC of 0.86; AUC was improved to 0.88 for predicting severe infections within 90 days with a stacked ensemble model, at risk of overfitting given cohort size. Conclusions: To our knowledge, this is the first ML model that predicts infection risk within 90 days of initiating bsAb for MM, and will be validated in larger patient cohorts.

Competition history

ISEF 2026 Translational Medical Science · Entry TMED036 Affiliated fair in WI, United States of America

Resources

ISEF abstract page

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