• Julie Morisset, Eric Vittinghoff, Brett M Elicker, Xiaowen Hu, Stephanie Le, Jay H Ryu, Kirk D Jones, Anna Haemel, Jeffrey A Golden, Francesco Boin, Brett Ley, Paul J Wolters, Talmadge E King, Harold R Collard, and Joyce S Lee.
• Department of Medicine, University of California, San Francisco, San Francisco, CA. Electronic address: julie.morisset@umontreal.ca.
• Chest. 2017 Nov 1; 152 (5): 999-1007.

BackgroundInterstitial 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.MethodsWe 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.ResultsThree 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.ConclusionsThe SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD.Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

42 keywords...

hide…