Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

Jieni Li; Yinan Huang; George J Hutton; Rajender R Aparasu

doi:10.1016/j.rcsop.2023.100307

Assessing treatment switch among patients with multiple sclerosis: A machine learning approach

Explor Res Clin Soc Pharm. 2023 Jul 10:11:100307. doi: 10.1016/j.rcsop.2023.100307. eCollection 2023 Sep.

Authors

Jieni Li¹, Yinan Huang², George J Hutton³, Rajender R Aparasu¹

Affiliations

¹ Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA.
² Department of Pharmacy Administration, College of Pharmacy, University of Mississippi, Oxford, MS, USA.
³ Baylor College of Medicine, Houston, TX, USA.

Abstract

Background: Patients with multiple sclerosis (MS) frequently switch their Disease-Modifying Agents (DMA) for effectiveness and safety concerns. This study aimed to develop and compare the random forest (RF) machine learning (ML) model with the logistic regression (LR) model for predicting DMA switching among MS patients.

Methods: This retrospective longitudinal study used the TriNetX data from a federated electronic medical records (EMR) network. Between September 2010 and May 2017, adults (aged ≥18) MS patients with ≥1 DMA prescription were identified, and the earliest DMA date was assigned as the index date. Patients prescribed any DMAs different from their index DMAs were considered as treatment switch. . The RF and LR models were built with 72 baseline characteristics and trained with 70% of the randomly split data after up-sampling. Area Under the Curves (AUC), accuracy, recall, G-measure, and F-1 score were used to evaluate the model performance.

Results: In this study, 7258 MS patients with ≥1 DMA were identified. Within two years, 16% of MS patients switched to a different DMA. The RF model obtained significantly better discrimination than the LR model (AUC = 0.65 vs. 0.63, p < 0.0001); however, the RF model had a similar predictive performance to the LR model with respect to F- and G-measures (RF: 72% and 73% vs. LR: 72% and 73%, respectively). The most influential features identified from the RF model were age, type of index medication, and year of index.

Conclusions: Compared to the LR model, RF performed better in predicting DMA switch in MS patients based on AUC measures; however, judged by F- and G-measures, the RF model performed similarly to LR. Further research is needed to understand the role of ML techniques in predicting treatment outcomes for the decision-making process to achieve optimal treatment goals.

Keywords: Machine learning; Multiple sclerosis; Real-world evidence; Treatment switching.