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.