Development of prediction models to select older RA patients with comorbidities for treatment with chronic low-dose glucocorticoids

Abstract

Objective: To develop prediction models for individual patient harm and benefit outcomes in elderly patients with RA and comorbidities treated with chronic low-dose glucocorticoid therapy or placebo.

Methods: In the Glucocorticoid Low-dose Outcome in Rheumatoid Arthritis (GLORIA) study, 451 RA patients ≥65 years of age were randomized to 2 years 5 mg/day prednisolone or placebo. Eight prediction models were developed from the dataset in a stepwise procedure based on prior knowledge. The first set of four models disregarded study treatment and examined general predictive factors. The second set of four models was similar but examined the additional role of low-dose prednisolone. In each set, two models focused on harm [the occurrence of one or more adverse events of special interest (AESIs) and the number of AESIs per year) and two on benefit (early clinical response/disease activity and a lack of joint damage progression). Linear and logistic multivariable regression methods with backward selection were used to develop the models. The final models were assessed and internally validated with bootstrapping techniques.

Results: A few variables were slightly predictive for one of the outcomes in the models, but none were of immediate clinical value. The quality of the prediction models was sufficient and the performance was low to moderate (explained variance 12-15%, area under the curve 0.67-0.69).

Conclusion: Baseline factors are not helpful in selecting elderly RA patients for treatment with low-dose prednisolone given their low power to predict the chance of benefit or harm.

Trial registration: https://ichgcp.net/clinical-trials-registry/NCT02585258" title="See in ClinicalTrials.gov">NCT02585258.

Keywords: RA; adverse events of special interest; disease activity; glucocorticoids; joint damage progression; prediction models.

© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.

Figures

Figure 1.

Overview of main, exploratory and post hoc prediction models

Figure 2.

Interpretation of predictors in the harm and benefit models disregarding and examining the effect of study treatment (i.e. low-dose prednisolone). For the benefit model, only the model disregarding the effect of study treatment is shown (panel C), because no effect of study treatment was found. (A) Baseline predictive factors for harm, disregarding the effect of prednisolone (red: an increase in harm; green: a decrease in harm; white: the variable is not included in the model; ?: a counterintuitive relationship). (B) Baseline predictive factors for the harm prediction model and the interaction with prednisolone, with the addition of the variables that were found to be predictive in the models disregarding the effect of prednisolone (red: an increase in harm; green: a decrease in harm; white: the variable is not included in the model; ?: a counterintuitive relationship). (C) Baseline predictive factors for the benefit prediction model, disregarding the effect of prednisolone (red: less benefit; green: more benefit; white: the variable is not included in the model; ?: a counterintuitive relationship). Full colour figure is available at Rheumatology online. *Neutralized means that the addition of prednisolone to the model counteracted the adverse effect of the baseline predictive factor. In other words, more joint damage is associated with an increased likelihood of at least one AESI, but this increase is gone after the addition of prednisolone to the model. Similarly, no change of antirheumatic treatment at baseline is associated with a greater number of AESIs, but this increase is gone after the addition of prednisolone to the model. **More adherence was associated with a lower number of AESIs; the addition of prednisolone to the model partially counteracted this effect. EQ-5D: European Quality of Life 5-Dimensions questionnaire