From the Rheumatology Unit, Department of Emergency and Organ Transplantations, University of Bari "Aldo Moro," Bari, Italy.
J Clin Rheumatol. 2022 Mar 1;28(2):e334-e339. doi: 10.1097/RHU.0000000000001720.
In this longitudinal study, patients with RA who started a biological disease-modifying antirheumatic drug (bDMARD) in a tertiary care center were analyzed. Demographic and clinical characteristics were collected at treatment baseline, 12-month, and 24-month follow-up. A wrapper feature selection algorithm was used to determine an attribute core set. Four different ML algorithms, namely, LR, random forest, K-nearest neighbors, and extreme gradient boosting, were then trained and validated with 10-fold cross-validation to predict 24-month sustained DAS28 (Disease Activity Score on 28 joints) remission. The performances of the algorithms were then compared assessing accuracy, precision, and recall.
Our analysis included 367 patients (female 323/367, 88%) with mean age ± SD of 53.7 ± 12.5 years at bDMARD baseline. Sustained DAS28 remission was achieved by 175 (47.2%) of 367 patients. The attribute core set used to train algorithms included acute phase reactant levels, Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, as well as several clinical characteristics. Extreme gradient boosting showed the best performance (accuracy, 72.7%; precision, 73.2%; recall, 68.1%), outperforming random forest (accuracy, 65.9%; precision, 65.6%; recall, 59.3%), LR (accuracy, 64.9%; precision, 62.6%; recall, 61.9%), and K-nearest neighbors (accuracy, 63%; precision, 61.5%; recall, 54.8%).
We showed that ML models can be used to predict sustained remission in RA patients on bDMARDs. Furthermore, our method only relies on a few easy-to-collect patient attributes. Our results are promising but need to be tested on longitudinal cohort studies.
本纵向研究分析了在一家三级护理中心开始使用生物改善病情抗风湿药物（bDMARD）的 RA 患者。在治疗基线、12 个月和 24 个月随访时收集人口统计学和临床特征。使用包装特征选择算法确定属性核心集。然后使用 10 倍交叉验证训练和验证四种不同的 ML 算法，即逻辑回归（LR）、随机森林、K-最近邻和极端梯度提升，并预测 24 个月持续 DAS28（28 个关节疾病活动评分）缓解。然后通过评估准确性、精度和召回率比较算法的性能。
我们的分析包括 367 名（女性 323/367，88%）患者，bDMARD 基线时的平均年龄±标准差为 53.7±12.5 岁。367 名患者中有 175 名（47.2%）达到持续 DAS28 缓解。用于训练算法的属性核心集包括急性期反应物水平、临床疾病活动指数、健康评估问卷残疾指数以及一些临床特征。极端梯度提升表现出最佳性能（准确性 72.7%、精度 73.2%、召回率 68.1%），优于随机森林（准确性 65.9%、精度 65.6%、召回率 59.3%）、LR（准确性 64.9%、精度 62.6%、召回率 61.9%）和 K-最近邻（准确性 63%、精度 61.5%、召回率 54.8%）。
我们表明 ML 模型可用于预测 RA 患者接受 bDMARD 治疗后的持续缓解。此外，我们的方法仅依赖于少数易于收集的患者属性。我们的结果很有前途，但需要在纵向队列研究中进行测试。
