Predicting survivorship and caregiver burden in multiple myeloma (MM) using machine learning (ML): Insights from a large, national, prospective study.

e13661 Background: MM survival has improved significantly, resulting in increased survivorship and caregiver burden and is crucial to understand the real-world impact of MM. We analyzed results from a large, prospective MM patient survey using ML models to identify key predictive factors for survivorship and caregiver burden. Methods: A 45-item questionnaire spanning sociodemographics, treatment preferences, symptom burden and caregiver impact was distributed to MM patients at Mayo Clinic and International Myeloma Foundation support groups. Questions about quality of life (QOL) and treatment satisfaction (TS) represented survivorship outcomes while caregiver workday loss (WL) and job loss (JL) were surrogates for caregiver burden. 6 ML models (multinomial, random forest (RF), XGB (XGBoost), k-NN, GBM, Naïve Bayes) with a training: testing cohort of 80:20 were used to predict each of the 4 outcomes independently, considering model accuracy, C-index, precision, recall, and F1 score (Table), and using R studio (V4.4.1) and Bluesky Statistics (V10.3.4). Results: 2,239 participants (54% male, 89% Whites) with median age 68 yrs completed the survey. 71% respondents had a college degree, 44% resided in Midwest states, 62% reported household income >$75,000, and 67% had Medicare/Medicaid. Median age at MM diagnosis was 67 yrs with 58% diagnosed within the past 5 yrs and 73% currently receiving MM treatment. 72% reported their spouse as the primary caregiver. XGB was the best predictive model for QOL with gender, household income, stem cell transplant, CAR-T therapy, support from medical staff, and symptom burden (pain, neuropathy, mental well-being) as significant predictors. The RF model was the best for TS with pain and neuropathy levels as the most impactful predictors. For caregiver burden, XGB was the best model for WL with gender, household income, CAR-T therapy, financial strain, lack of support from medical staff, self-workdays missed, self-job loss, and pain and neuropathy levels as significant predictors. For JL, RF was the best model with significant variables being workdays missed by self, increased healthcare needs, symptom burden (pain, neuropathy, and mental well-being), and restricted physical activity. Conclusions: Our study shows the significant impact of MM survivorship and efficiently identifies predictive factors associated with survivorship and caregiver burden from a large prospective dataset using validated ML models. These findings highlight the complex challenges from MM and provide contemporary ML methods to better understand their interplay such that a framework of real-world interventions can be developed. Outcome Best ML Model Accuracy C-Index Precision Recall F1 score QOL XG 0.996 0.996 0.996 0.997 0.996 TS RF 0.984 0.900 0.937 0.948 0.937 WL XGB 0.984 1.000 0.944 0.947 0.945 JL RF 0.989 0.800 0.829 0.848 0.835