Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis

Abstract

Importance: 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.

Objective: 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.

Exposures: All included patients received an originator TNFi for at least 12 weeks.

Main outcomes and measures: 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.

Results: 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.

Conclusions and relevance: In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making.

Figure.. Permutation Feature Importance in Logistic Regression Models and Random Forest Models

Each graph illustrates the ranking of the most important 10 variables in the corresponding model, and decrease of the model performance (ie, accuracy) when the variable is randomly shuffled. BASDAI indicates Bath ankylosing spondylitis disease activity index; BASFI, Bath ankylosing spondylitis function index; BMI, body mass index; CRP, C-reactive protein; HLA-B27, human leukocyte antigen B27; PGA, patient global assessment; TBP, total back pain.