Division of Rheumatology, Columbia University Irving Medical Center, New York, New York.
Garden State Rheumatology Consultants, Union, New Jersey.
JAMA Netw Open. 2022 Mar 1;5(3):e222312. doi: 10.1001/jamanetworkopen.2022.2312.
Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond or not respond to TNFis could provide opportunities to personalize treatment strategies.
To develop models of the probability of short-term response to TNFi treatment in individual patients with active AS.
DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective cohort study using data of the TNFi group (ie, treatment group) from 10 randomized clinical trials (RCTs) of TNFi treatment among patients with active AS, conducted from 2002 to 2016. Participants were adult patients with active AS who failed nonsteroidal anti-inflammatory drugs. Included RCTs were phase 3 and 4 studies that assessed the efficacy of an originator TNFi at week 12 and/or week 24, either compared with placebo or an antirheumatic drug. The cohort was divided into a training and a testing set. Data analysis was conducted from July 1, 2019, to November 30, 2020.
All included patients received an originator TNFi for at least 12 weeks.
Outcomes included major response and no response based on the change of AS Disease Activity Score at 12 weeks. Machine learning algorithms were applied to estimate the probability of having major response and no response for individual patients.
The study included 1899 participants from 10 trials. The training set included 1207 individuals (mean [SD] age, 39 [12] years; 908 [75.2%] men), of whom 407 (33.7%) had major response and 414 (34.3%) had no response. In the reduced logistic regression models, accuracy was 0.74 for major response and 0.75 for no response. The probability of major response increased with higher C-reactive protein (CRP) level, patient global assessment (PGA), and Bath AS Disease Activity Index (BASDAI) question 2 score and decreased with higher body mass index (BMI) and Bath AS Functional Index (BASFI) score. The probability of no response increased with age and BASFI score, and decreased with higher CRP level, BASDAI question 2 score, and PGA. In the testing set (692 participants; mean [SD] age, 38 [11] years; 533 [77.0%] men), models demonstrated moderate to high accuracy.
In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making.
肿瘤坏死因子抑制剂 (TNFis) 彻底改变了强直性脊柱炎 (AS) 的治疗方法；然而，大约一半的患者没有明显的临床反应，这表明治疗反应存在重要的异质性。确定患者对 TNFis 有反应或无反应的可能性，可以为个性化治疗策略提供机会。
为患有活动性 AS 的个体患者建立短期接受 TNFi 治疗反应的概率模型。
设计、设置和参与者：这是一项回顾性队列研究，使用了来自 2002 年至 2016 年期间 10 项 TNFis 治疗活动性 AS 患者的随机临床试验 (RCT) 的 TNFi 组（即治疗组）的数据。参与者为服用非甾体抗炎药失败的活动性 AS 成年患者。纳入的 RCT 为 3 期和 4 期研究，评估了在第 12 周和/或第 24 周时，一种原研 TNFis 与安慰剂或抗风湿药物相比的疗效。队列分为训练集和测试集。数据分析于 2019 年 7 月 1 日至 2020 年 11 月 30 日进行。
所有纳入的患者至少接受了 12 周的原研 TNFis 治疗。
结果包括主要反应和无反应，根据第 12 周时 AS 疾病活动评分的变化来判断。机器学习算法用于估计个体患者出现主要反应和无反应的概率。
该研究纳入了来自 10 项试验的 1899 名参与者。训练集包括 1207 人（平均[标准差]年龄为 39[12]岁；908[75.2%]为男性），其中 407 人（33.7%）有主要反应，414 人（34.3%）无反应。在简化的逻辑回归模型中，主要反应的准确率为 0.74，无反应的准确率为 0.75。主要反应的概率随着 C 反应蛋白（CRP）水平、患者整体评估（PGA）和 Bath AS 疾病活动指数（BASDAI）问题 2 评分的升高而增加，随着体重指数（BMI）和 Bath AS 功能指数（BASFI）评分的升高而降低。无反应的概率随着年龄和 BASFI 评分的增加而增加，随着 CRP 水平、BASDAI 问题 2 评分和 PGA 的升高而降低。在测试集（692 名参与者；平均[标准差]年龄为 38[11]岁；533[77.0%]为男性）中，模型表现出中等至高度的准确性。
在这项队列研究中，从基线变量预测了对 TNFis 的初始反应概率，这可能有助于制定个性化的治疗决策。
