Ada Man1, Todd Davidyock2, Laura T Ferguson3, Michael Ieong2, Yuqing Zhang4, Robert W Simms3. 1. Section of Rheumatology, adamanemail@gmail.com. 2. Section of Pulmonology, Allergy, Sleep, and Critical Care Medicine and. 3. Section of Rheumatology. 4. Clinical Epidemiology Unit, Boston University School of Medicine, Boston, MA, USA.
Abstract
OBJECTIVE: An accelerated rate of decline in forced vital capacity (FVC) affects >50% of patients with SSc but data on the variability and determinants of this change are scarce. We sought to identify trajectories of FVC and their associated variables in SSc patients over a 12-year period. METHODS: Clinical and pulmonary function data were retrospectively collected. SSc patients with three or more FVC values were included. Group-based modelling was used to cluster similar FVC patterns into trajectories. Baseline variables were associated with the trajectories using multinomial logistic regression. The effect of CYC on FVC was examined with each trajectory as a time-varying covariate. RESULTS: In 254 SSc patients we identified seven distinct FVC trajectories: very low slow decline (5.5%), very low improve (13.8%), low fast decline (9.5%), low stable (19.7%), low-normal improve (31.1%), normal improve (16.1%) and normal stable (4.3%). Younger age and the presence of pulmonary hypertension, Interstitial lung disease and shortness of breath at baseline significantly increased the odds of declining trajectories vs the reference trajectory (low-normal improve). CYC was associated with FVC improvement in the low fast decline trajectory. CONCLUSION: The course of FVC in SSc was highly variable, with improvement and stability experienced even by those with low baseline FVC. Trajectory modelling was able to identify SSc patients who were most likely to experience FVC decline and thus could be a useful tool for patient management as well as clinical trial design.
OBJECTIVE: An accelerated rate of decline in forced vital capacity (FVC) affects >50% of patients with SSc but data on the variability and determinants of this change are scarce. We sought to identify trajectories of FVC and their associated variables in SSc patients over a 12-year period. METHODS: Clinical and pulmonary function data were retrospectively collected. SSc patients with three or more FVC values were included. Group-based modelling was used to cluster similar FVC patterns into trajectories. Baseline variables were associated with the trajectories using multinomial logistic regression. The effect of CYC on FVC was examined with each trajectory as a time-varying covariate. RESULTS: In 254 SSc patients we identified seven distinct FVC trajectories: very low slow decline (5.5%), very low improve (13.8%), low fast decline (9.5%), low stable (19.7%), low-normal improve (31.1%), normal improve (16.1%) and normal stable (4.3%). Younger age and the presence of pulmonary hypertension, Interstitial lung disease and shortness of breath at baseline significantly increased the odds of declining trajectories vs the reference trajectory (low-normal improve). CYC was associated with FVC improvement in the low fast decline trajectory. CONCLUSION: The course of FVC in SSc was highly variable, with improvement and stability experienced even by those with low baseline FVC. Trajectory modelling was able to identify SSc patients who were most likely to experience FVC decline and thus could be a useful tool for patient management as well as clinical trial design.
Authors: 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; Joyce S Lee Journal: Chest Date: 2017-06-16 Impact factor: 9.410