A preliminary nomogram model for predicting organ damage of patients with diffuse Systemic sclerosis

Abstract

Abstract Background The clinical manifestations of SSc are highly heterogeneous, and there is still no clinical predictive model that can accurately predict prognosis and guide treatment decision-making. Therefore, it is of great clinical significance to explore effective and non-invasive biomarkers which can be efficiently used in the clinical management of patients with SSc. Objective To investigate the predictive factors of organ damage in systemic sclerosis and establish a nomogram model. Methods This project is a retrospective study. A total of 331 SSc patients treated in our hospital from September 2012 to September 2022 were included. Gender, age, course of disease, mRSS, OPN, KL-6, IL-6, Dlco% and other relevant data were collected. Cox regression analysis and lasso regression analysis were performed to determine the predictive factors. Based on the results, a nomogram model was established. The model were evaluated by C-indices, calibration plot and DCA. Results Univariate Cox regression analysis showed that age ≥ 66 years old, course of disease ≥ 10 months, mRSS ≥ 14, DUs, elevated myoglobin, OPN ≥ 25ng/ml were independent risk factors for organ damage in patients with SSc (P < 0.05). According to lasso analysis, a nomogram model of organ damage was established. The C-indices of the development group at 24m, 48m and 72m were 64.4, 63.1 and 64.6, while the C-indices of the validation group at 24m, 48m and 72m were 63.7, 64.2 and 64.1, respectively.The results of DCA show that the nomogram can be used as a valuable predictive tool to predict irreversible organ damage in SSc patients. Conclusion OPN is an independent risk factor for organ damage in SSc. We included OPN and several other commonly used clinical indicators and constructed a nomogram model. According to the nomogram, we can calculate the probability of organ damage, identify high-risk patients, and improve the prognosis.