Division of Medicine, Centre for Rheumatology and Clinical Immunology, University of Turku and Turku University Hospital, Kiinamyllynkatu 4-6, P.O. Box 52, 20521, Turku, Finland.
Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, 20520, Turku, Finland.
Sci Rep. 2023 Aug 9;13(1):12943. doi: 10.1038/s41598-023-39694-2.
Frequent laboratory monitoring is recommended for early identification of toxicity when initiating conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). We aimed at developing a risk prediction model to individualize laboratory testing at csDMARD initiation. We identified inflammatory joint disease patients (N = 1196) initiating a csDMARD in Turku University Hospital 2013-2019. Baseline and follow-up safety monitoring results were drawn from electronic health records. For rheumatoid arthritis patients, diagnoses and csDMARD initiation/cessation dates were manually confirmed. Primary endpoint was alanine transaminase (ALT) elevation of more than twice the upper limit of normal (ULN) within 6 months after treatment initiation. Computational models for predicting incident ALT elevations were developed using Lasso Cox proportional hazards regression with stable iterative variable selection (SIVS) and were internally validated against a randomly selected test cohort (1/3 of the data) that was not used for training the models. Primary endpoint was reached in 82 patients (6.9%). Among baseline variables, Lasso model with SIVS predicted subsequent ALT elevations of > 2 × ULN using higher ALT, csDMARD other than methotrexate or sulfasalazine and psoriatic arthritis diagnosis as important predictors, with a concordance index of 0.71 in the test cohort. Respectively, at first follow-up, in addition to baseline ALT and psoriatic arthritis diagnosis, also ALT change from baseline was identified as an important predictor resulting in a test concordance index of 0.72. Our computational model predicts ALT elevations after the first follow-up test with good accuracy and can help in optimizing individual testing frequency.
在开始使用传统合成疾病修饰抗风湿药物（csDMARD）时，建议频繁进行实验室监测，以便及早发现毒性。我们旨在开发一种风险预测模型，以实现 csDMARD 起始时的个体化实验室检测。我们确定了 2013-2019 年在图尔库大学医院开始使用 csDMARD 的炎症性关节疾病患者（N=1196）。从电子健康记录中提取基线和随访安全性监测结果。对于类风湿关节炎患者，手动确认了诊断和 csDMARD 起始/停止日期。主要终点是治疗开始后 6 个月内丙氨酸氨基转移酶（ALT）升高超过正常上限（ULN）的两倍以上。使用带有稳定迭代变量选择（SIVS）的 Lasso Cox 比例风险回归开发了预测 ALT 升高事件的计算模型，并在未用于训练模型的随机选择的测试队列（数据的 1/3）中进行了内部验证。主要终点在 82 名患者（6.9%）中达到。在基线变量中，使用 SIVS 的 Lasso 模型预测随后的 ALT 升高>2×ULN，使用较高的 ALT、除甲氨蝶呤或柳氮磺胺吡啶以外的 csDMARD 和银屑病关节炎诊断作为重要预测因素，在测试队列中的一致性指数为 0.71。分别地，在第一次随访时，除了基线 ALT 和银屑病关节炎诊断外，还发现从基线开始的 ALT 变化也是一个重要的预测因素，导致测试一致性指数为 0.72。我们的计算模型以较高的准确性预测了首次随访测试后的 ALT 升高，并有助于优化个体测试频率。
