ArticleRheumatology (Oxford, England)2025
Preliminary nomogram model for predicting irreversible organ damage of patients with systemic sclerosis.
Article in Rheumatology (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
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7 authors.
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Abstract
objectiveTo investigate predictive factors for irreversible organ damage in systemic sclerosis (SSc) and establish a nomogram model.
methodsThis retrospective study included patients with SSc who were treated at our hospital between March 2013 and March 2023. Irreversible organ damage included heart failure, respiratory failure, renal failure, and gangrene of the hands and feet. Cox and LASSO regression analyses were performed to determine the predictive factors. Based on the results, a nomogram model was developed. The model was evaluated using the C-indices, calibration plots and DCA.
resultsA total of 361 patients with systemic sclerosis were randomly divided into the development (n = 181) and validation (n = 180) groups. Multivariate Cox regression analysis showed that age ≥65 years, weight loss, digital ulcers, mRSS ≥16, elevated creatinine, elevated myoglobin, elevated C-reactive protein, renal involvement and cardiac involvement were independent risk factors. Based on the LASSO analysis, a nomogram model of irreversible organ damage was established. The C-indices of the development group at 24, 60 and 96 m were 96.7, 84.5 and 85.7, whereas those of the validation group at 24, 60 and 96 m were 86.6, 79.1 and 78.5, respectively. The results of the DCA showed that the nomogram can be used as a valuable tool to predict irreversible organ damage in patients with SSc.
conclusionWe included commonly used clinical indicators. According to the nomogram, the probability of irreversible organ damage can be calculated and high-risk patients can be identified.
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