Systemic sclerosis & Raynaud's
Prediction of scleroderma renal crisis in patients of SSc: insight from the Chinese Rheumatism Data Center (CRDC) cohort study
Authors' conclusion
A Nomogram with improved predictive performance compared with the previous one was developed in a larger sample size in China.
Study context
- Population
- SSc patients registered in the Chinese Rheumatism Data Center (CRDC) database
- Exposure
- New multi-predictor Nomogram vs an existing model for predicting scleroderma renal crisis (SRC)
- Outcome
- Prediction of SRC risk by a new multi-predictor Nomogram
Methodological detailsprediction model development and validation, matched 1:4 controls, LASSO regression, bootstrap resampling for internal and external validation
- Prediction model development and validation
- Matched 1:4 controls
- LASSO regression
- Bootstrap resampling for internal and external validation
Main finding
As reported in the abstract
Nomogram performance for predicting SRC
- Predictors: myocardial involvement, SSc subtype, anaemia, platelet count and disease duration
- AUC showed strong discrimination in both training and validation datasets
- Calibration curves and Hosmer-Lemeshow test indicated good agreement between predicted and actual outcomes
- Decision curve analysis (DCA) demonstrated greater clinical net benefit
- Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed significant improvement over the previous model
Observational data: associations, not effects.
Original publication
Prediction of scleroderma renal crisis in patients of SSc: insight from the CRDC cohort study. Rheumatology (Oxford). Published 21 January 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →