Multicenter Study Develops Prognostic Nomogram for MDA5-Positive Dermatomyositis with Lung Disease
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A new clinical tool developed from a four-hospital Chinese study can predict survival outcomes in patients with MDA5-positive dermatomyositis who also have interstitial lung disease, a complication associated with significantly higher mortality. Researchers identified four measurable factors, including the presence of rapidly progressive lung disease, age, and two blood markers, that together predict who is most at risk. The resulting prediction tool was validated across multiple institutions and may help clinicians identify and act earlier for patients facing the highest danger.
Abstract
In dermatomyositis with interstitial lung disease (DM-ILD), particularly in patients positive for the anti-MDA5 (anti-melanoma differentiation-associated gene 5) antibody, mortality rises substantially during the early disease course. This multicenter retrospective cohort study gathered patients from four Chinese hospitals to develop and validate a prognostic model for early identification of individuals at heightened mortality risk.
Patients from Nanjing Drum Tower Hospital were randomly divided into training and internal validation sets at a 7:3 ratio. Univariable and multivariable Cox regression analyses identified four independent prognostic factors: rapidly progressive interstitial lung disease (RP-ILD), age, erythrocyte sedimentation rate (ESR), and neutrophil-to-lymphocyte ratio (NLR). These four predictors were integrated into a prognostic nomogram for estimating overall survival, with performance assessed through the concordance index (C-index), calibration curves, and decision curve analysis.
The nomogram achieved a C-index of 0.807 (95% CI 0.76 to 0.854) in the training cohort, 0.844 (95% CI 0.777 to 0.911) in internal validation, and 0.794 (95% CI 0.716 to 0.872) in external validation. Calibration plots demonstrated good alignment between predicted and observed outcomes, and decision curve analysis supported clinical utility. Risk stratification effectively distinguished patients with varying mortality risks, offering a practical early-warning tool for this high-risk patient group.