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.