Development and Validation of a Multivariable Risk Prediction Model for Serious Infection in Patients with Psoriasis Receiving Systemic Therapy

Abstract

Background

Patients with psoriasis are often concerned about the associated risk of serious infection with systemic psoriasis treatments.

Objectives

To develop and externally validate a prediction model for serious infection in patients with psoriasis within one year of starting systemic therapies.

Methods

The risk prediction model was developed using the British Association of Dermatologists Biologic Interventions Register (BADBIR) and the German Psoriasis Registry PsoBest was used as the validation dataset. Model discrimination and calibration was assessed internally and externally using the C‐statistic, the calibration slope, and the calibration in the large.

Results

175 (1·7%) out of 10033 participants from BADBIR and 41 (1·7%) out of 2423 participants from PsoBest developed a serious infection within one year of therapy initiation. Selected predictors in a multiple logistic regression model included 9 baseline covariates, and starting infliximab was the strongest predictor. Evaluation of model performance showed a bootstrap optimism‐corrected C‐statistic of 0·64 (95% C.I. 0·60, 0·69), calibration in the large of 0·02 (95% confidence interval ‐0·14 to 0·17) and calibration slope of 0·88 (0·70, 1·07), while external validation performance was poor [C‐statistic 0·52 (95% C.I. 0·42 to 0·62), calibration in the large of 0·06 (95% C.I. ‐0·25 to 0·37), calibration slope 0·36 (95% C.I. ‐0·24, 0·97)].

Conclusions

We present first results of the development of a multivariable prediction model that may help patients and dermatologists in the United Kingdom and the Republic of Ireland identify modifiable risk factors and inform therapy choice in a shared decision‐making process.

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