Assessment of Predictive Factors That Shorten Duration of Treatment in Patients With Multiple Myeloma Using AI: Real-World Longitudinal Study Using Data From Medical Data Vision Claims Database
Abstract
Background: With the availability of newer therapies, the duration of therapy (DoT) shortens with each increasing line of treatment in Japanese patients with multiple myeloma (MM).
Objective: This study aimed to identify factors that shorten DoT in patients with MM using a machine learning (ML) procedure from the Medical Data Vision (MDV) database.
Methods: This nationwide, retrospective observational real-world study was conducted using anonymized patient data from the MDV claims database from 2003 to 2022. Patients (≥18 y) with transplant-ineligible newly diagnosed MM (continued first line therapy), or relapsed or refractory MM (continued second or third line therapies) were included. To identify important predictive factors, an explainable deep learning model was created using 647 extracted variables (continuous, binary, and nominal categorical) from the MDV database, and the extracted data were used to train ML algorithms to build point-wise linear (PWL) models for predicting DoT. Predictive performance of the PWL model was compared with the elastic net (regularized logistic regression) and the extreme gradient boosting models, and calculated by area under the curve and evaluated by 10-fold double cross-validation. A clustering analysis (k-means method) of 4848 individual samples assessed the relationship between each sample and DoT (3, 6, and 12 months). The characteristics of clusters and sample features belonging to each cluster during and after treatment were…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence.
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