Applying Artificial Intelligence Machine Learning to Discover Factors That Shorten Duration of Treatment in Patients with Multiple Myeloma Using Real-World Patient-Level Longitudinal Data from Medical Data Vision Database
Authors: Handa Hiroshi , Ishida Tadao , Ozaki Shuji , Iida Shinsuke , Mori Asuka , Sakai Chika , Kato Kenichi , Bin-Chia Wu David , Yu DaeYoung , Nemoto Shota , Yamashita Yasuho , Shibahara Takuma 
Published: December 2023

Introduction: To maximize the benefit of treatment in multiple myeloma (MM), it is important to continue treatment for appropriate duration . Our recent real-world study using the Medical Data Vision (MDV) claim database showed that median duration of treatment (DoT) of 1 line (L), 2L and 3L treatment was rather short (range: 6.7 to 8.6 months) in Japanese non-transplant patients with MM (Handa H, et al. Plos one. 2023). However, the broad range of treatment patterns in Japan makes it difficult to identify the cause of shortened DoT. Therefore, we assessed the predictive factors that shorten DoT at threshold of &lt;3, &lt;6 and &lt;12 months by applying machine learning (ML) model using real-world clinical data of MM patients from the MDV database.
Methods: This retrospective cohort study was conducted on 4848 individual treatments (2,762 patients; 1L for stem cell transplant-not conducted, 2L/3L) in patients (aged ≥18 years) with transplant-ineligible (newly diagnosed or relapsed/refractory) MM between 2003-21. An explainable deep learning model (point-wise linear model, PWL) was developed using 647 feature variables (evaluated before, during, and after treatment; Table) from the MDV database to identify the important predictors associated with DoT. The PWL model generated a custom-made logistic regression model for each sample, which was very familiar to medical researchers and allowed to analyze the importance of the feature variables. Predictive performance of the PWL model was compared with elastic-net regularized logistic regression and XGBoost models and calculated by area under the curve (AUC) and evaluated by 10-fold double cross validation. To stratify the patients with shorter DoT, a clustering analysis of 4848 individual treatment was conducted based on the similarity of coefficients of the custom-made logistic regression models generated from the PWL model. To understand characteristics of samples belonging to each cluster, the relationship between predicted DoT and MM treatment-related comorbidity management was investigated ( Table).
Results: The AUC score of the PWL model to predict DoT of &lt;3, &lt;6 and &lt;12 months was 0.61, 0.64 and 0.66, respectively ( Figure). As per clustering analysis, there were 2 clusters (cluster A and B) at DoT of &lt;3 months and 3 clusters (cluster A, B and C) each at DoT of &lt;6 and &lt;12 months, respectively. At &lt;3 months, cluster B showed lower median of prediction probability, and had a significantly (P&lt;0.01) higher previous treatment lines, pre-treatment Charlson Comorbidity Index (CCI) versus cluster A. At &lt;6 months, cluster C showed lower median of prediction probability, and had significantly (P&lt;0.01) higher CCI and comorbidities, and cluster B had significantly (P&lt;0.01) lower estimated glomerular filtration rate, among the clusters. At &lt;12 months, cluster C showed lower median of prediction probability, and had significantly (P&lt;0.01) higher lactate dehydrogenase (LD), previous treatment lines, pre-treatment CCI, degree of care requirement at diagnosis of MM, pre-treatment tube and parenteral nutrition, and comorbidities, among clusters. Additionally, in cluster B (at &lt;3 months), and cluster C (at &lt;6 and &lt;12 months), use of immunomodulatory drugs (IMiDs) in treatment for MM was significantly higher in patients who met predicted DoT at each threshold versus the ones who were not. However, age and history of infectious diseases (antibacterial agents, γ globulin, etc.) was not affected. In addition, IMiDs and aspirin use at &lt;3 and &lt;6 months, and IMiDs use at &lt;12 months had significant differences in events during and after treatment, which are important for considering treatment strategies for MM patients and may lead to the optimization of treatment plans. Study limitation included exclusion of important prognostic factors (cytogenetic profile, R-ISS stage, and presence of all plasmacytomas).
Conclusions: TheML technique with PWL model yielded efficient results to understand the trend associated with event in treatment and characteristics of Japanese patients with MM whose DoT were shortened. Patient's condition and management related factors might have associated with shortened DoT, in consistent with clinical findings. These results are critical to continually improving MM management, achieving optimal patients care, and understanding the burden of disease and health services available to Japanese patients with MM in the real-world.