Rapid paper
A multi-predictor model to predict risk of scleroderma renal crisis in systemic sclerosis: a multicentre, retrospective, cohort study
D. Xu1, L. Zhu2, R. Cai3, Z. Yi4, H. Zhang5, G. Guo6, S. Liu7, J. Xu8, Q. Wang9, Y. Su10, X. Li11, J. Zhao12, R. Mu13
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Hebei Yiling Hospital, Shijiazhuang, China
- Department of Rheumatology and Immunology, Hebei Yiling Hospital, Shijiazhuang, China.
- Department of Rheumatology and Immunology, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
- Department of Rheumatology and Immunology, the First Affiliated Hospital of Kunming Medical University, Kunming, China.
- Department of Rheumatology and Immunology, Peking University Shenzhen Hospital, Shenzhen, China.
- Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China.
- Department of Rheumatology and Immunology, Peking University Third Hospital, Beijing, China. murongster@gmail.com
CER14566
2021 Vol.39, N°4
PI 0721, PF 0726
Rapid paper
Free to view
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PMID: 34001308 [PubMed]
Received: 25/02/2021
Accepted : 28/04/2021
In Press: 14/05/2021
Published: 08/07/2021
Abstract
OBJECTIVES:
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.
CONCLUSIONS:
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.
