A multi-predictor model to predict risk of scleroderma renal crisis in systemic sclerosis: A multicentre, retrospective, cohort study

Abstract

Objective. Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome. Therefore, it is of great interest to provide a personalised tool to predict risk of SRC in systemic sclerosis (SSc). Methods. We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimise disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration, with further assessment by external validation in a validation cohort composed of 400 consecutive SSc patients from other 4 tertiary hospitals. Results. A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained, including disease duration <2 years, cardiac involvement, anaemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797–0.882) and good calibration. High AUC value of 0.854 (95% CI: 0.690–1.000) could still be achieved in the external validation. The model is now available online for research use. Conclusion. The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalised medicine.