Julie Morisset1, Eric Vittinghoff2, Brett M Elicker3, Xiaowen Hu4, Stephanie Le5, Jay H Ryu4, Kirk D Jones6, Anna Haemel7, Jeffrey A Golden5, Francesco Boin5, Brett Ley5, Paul J Wolters5, Talmadge E King5, Harold R Collard5, Joyce S Lee8. 1. Department of Medicine, University of California, San Francisco, San Francisco, CA. Electronic address: julie.morisset@umontreal.ca. 2. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA. 3. Department of Radiology, University of California, San Francisco, San Francisco, CA. 4. Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN. 5. Department of Medicine, University of California, San Francisco, San Francisco, CA. 6. Department of Pathology, University of California, San Francisco, San Francisco, CA. 7. Department of Dermatology, University of California, San Francisco, San Francisco, CA. 8. Department of Medicine, University of California, San Francisco, San Francisco, CA; Department of Medicine, University of Colorado Denver, Denver, CO.
Abstract
BACKGROUND: Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course. METHODS: We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index. RESULTS: Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years. CONCLUSIONS: The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD.
BACKGROUND:Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course. METHODS: We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index. RESULTS: Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years. CONCLUSIONS: The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD.
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