Li Jieni, Huang Yinan, Hutton George J, Aparasu Rajender R
Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, TX, USA.
Department of Pharmacy Administration, College of Pharmacy, University of Mississippi, Oxford, MS, USA.
Explor Res Clin Soc Pharm. 2023 Jul 10;11:100307. doi: 10.1016/j.rcsop.2023.100307. eCollection 2023 Sep.
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.
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.
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, < 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.
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.
多发性硬化症（MS）患者经常因有效性和安全性问题更换疾病修正治疗药物（DMA）。本研究旨在开发随机森林（RF）机器学习（ML）模型并与逻辑回归（LR）模型进行比较，以预测MS患者的DMA更换情况。
这项回顾性纵向研究使用了来自联合电子病历（EMR）网络的TriNetX数据。在2010年9月至2017年5月期间，识别出年龄≥18岁且有≥1次DMA处方的成年MS患者，并将最早的DMA日期指定为索引日期。处方的DMA与索引DMA不同的患者被视为治疗更换。RF和LR模型基于72个基线特征构建，并在过采样后使用随机拆分数据的70%进行训练。曲线下面积（AUC）、准确率、召回率、G指标和F1分数用于评估模型性能。
在本研究中，识别出7258例有≥1次DMA的MS患者。两年内，16%的MS患者更换为不同的DMA。RF模型的判别能力明显优于LR模型（AUC = 0.65对0.63，<0.0001）；然而，就F指标和G指标而言，RF模型与LR模型的预测性能相似（RF分别为72%和73%，LR分别为72%和73%）。从RF模型中识别出的最具影响力的特征是年龄、索引药物类型和索引年份。
与LR模型相比，基于AUC指标，RF在预测MS患者的DMA更换方面表现更好；然而，根据F指标和G指标判断，RF模型与LR模型表现相似。需要进一步研究以了解ML技术在预测治疗结果以实现最佳治疗目标的决策过程中的作用。
