Predicting future response to certolizumab pegol in rheumatoid arthritis patients: features at 12 weeks associated with low disease activity at 1 year

Abstract

Objective: To determine the prognostic significance of data collected early after starting certolizumab pegol (CZP) to predict low disease activity (LDA) at week 52.

Methods: Data from 703 CZP-treated patients in the Rheumatoid Arthritis Prevention of Structural Damage 1 (RAPID 1) trial through week 12 were used as variables to predict LDA (Disease Activity Score in 28 joints-erythrocyte sedimentation rate ≤3.2) at week 52. We identified variables, developed prediction models using classification trees, and tested performance using training and testing data sets. Additional prediction models were constructed using the Clinical Disease Activity Index (CDAI) and an alternate outcome definition (composite of LDA or American College of Rheumatology criteria for 50% improvement [ACR50]).

Results: Using week 6 and 12 data and across several different prediction models, response (LDA) and nonresponse at 1 year were predicted with relatively high accuracy (70-90%) for most patients. The best performing model predicting nonresponse by 12 weeks was 90% accurate and applied to 46% of the population. Model accuracy for predicted responders (30% of the RAPID 1 population) was 74%. The area under the receiver operating curve was 0.76. Depending on the desired certainty of prediction at 12 weeks, ~12-25% of patients required >12 weeks of treatment to be accurately classified. CDAI-based models and those evaluating the composite outcome (LDA or ACR50) achieved comparable accuracy.

Conclusion: We could accurately predict within 12 weeks of starting CZP whether most established rheumatoid arthritis (RA) patients with high baseline disease activity would likely achieve/not achieve LDA at 1 year. Decision trees may be useful to guide prospective management for RA patients treated with CZP and other biologics.

Trial registration: ClinicalTrials.gov NCT00152386.

Figure 1

Graph showing probability of LDA at 1 year (A) versus absolute DAS28 and/or (B) vs. DAS28 improvement from baseline at Week 12.

Figure 2

A-priori model: constructed based only on clinical input using DAS28 change from baseline of >1.2 units at Weeks 4 and 12 as predictors.

Figure 3

CART Model 1: all potential predictors (measured at baseline and at Weeks 4, 6, 8, and 12) included to predict low disease activity (DAS28 ≤3.2) at 1 year. Variables and cut points were derived empirically using CART. Results shown are from the testing dataset only since performance of the models using the training dataset was generally superior.

Figure 4

CART Model 2: variation on Model 1 (Figure 3), 3:1 misclassification cost applied to maximize accuracy for predicted nonresponders. Outcome is low disease activity (DAS28 ≤3.2) at 1 year. Variables and cut points were derived empirically using CART. Results shown are from the testing dataset only since performance of the models using the training dataset was generally superior.

Figure 5

CART Model 3: selected predictors easy to measure in daily practice. Variables and cut points were derived empirically using CART but included only those that were more easily measured in clinical practice (e.g. CDAI, SJC; not DAS28). Results shown are from the testing dataset only since performance of the models using the training dataset was generally superior.