Development and validation of a multivariable risk prediction model for serious infection in patients with psoriasis receiving systemic therapy. (1st April 2019)

• Record Type: Journal Article
• Authors: Yiu, Z.Z.N.; Sorbe, C.; Lunt, M.; Rustenbach, S.J.; Kühl, L.; Augustin, M.; Mason, K.J.; Ashcroft, D.M.; Griffiths, C.E.M.; Warren, R.B.; Ormerod, Anthony D.; Barker, Jonathan N.W.N.; Evans, Ian; McElhone, Kathleen; Smith, Catherine H.; Reynolds, Nick J.; Murphy, Ruth; Benham, Marilyn; David Burden, A.; Hussain, Sagair; Kirby, Brian; Lawson, Linda; Owen, Caroline M.
• Is Part Of: British journal of dermatology. Volume 180:Number 4(2019)
• Page Start: 894
• Page End: 901
• Publication Date: 2019-04-01
• Subjects: Dermatology -- Periodicals; Skin -- Diseases -- Periodicals; 616.5
• DOI: 10.1111/bjd.17421
• Languages: English
• ISSNs: 0007-0963

Abstract

Summary: Background: Patients with psoriasis are often concerned about the risk of serious infection associated with systemic psoriasis treatments. Objectives: To develop and externally validate a prediction model for serious infection in patients with psoriasis within 1 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 were assessed internally and externally using the C ‐statistic, the calibration slope and the calibration in the large. Results: Overall 175 (1·7%) out of 10 033 participants from BADBIR and 41 (1·7%) out of 2423 participants from PsoBest developed a serious infection within 1 year of therapy initiation. Selected predictors in a multiple logistic regression model included nine baseline covariates, and starting infliximab was the strongest predictor. Evaluation of model performance showed a bootstrap optimism‐corrected C ‐statistic of 0·64 [95% confidence interval (CI) 0·60–0·69], calibration in the large of 0·02 (95% CI −0·14 to 0·17) and a calibration slope of 0·88 (95% CI 0·70–1·07), while external validation performance was poor, with C ‐statistic 0·52 (95% CI 0·42–0·62), calibration in the large 0·06 (95% CI −0·25 to 0·37) and calibration slope 0·36 (95% CI −0·24 to 0·97). Conclusions: We present the first results of the development of a multivariable prediction model. This model may help patients and dermatologists in the U.K. and the Republic of Ireland to identify modifiable risk factors and inform therapy choice in a shared decision‐making process.