Dynamic Prediction of Pulmonary Hypertension in Systemic Sclerosis Using Landmark Analysis

Objective Pulmonary hypertension (PH) is a serious complication of systemic sclerosis (SSc). In this study, we explored the prediction of short‐term risk for PH using serial pulmonary function tests (PFTs) and other disease features. Methods SSc patients in whom disease onset occurred ≥10 years prior to data retrieval and for whom autoantibody specificity and PFT data were available were included in this study. Mixed‐effects modeling was used to describe changes in PFTs over time. Landmarking was utilized to include serial assessments and stratified Cox proportional hazards regression analysis with landmarks as strata was used to develop the PH prediction models. Results We analyzed data from 1,247 SSc patients, 16.3% of whom were male and 35.8% of whom had diffuse cutaneous SSc. Anticentromere, antitopoisomerase, and anti–RNA polymerase antibodies were observed in 29.8%, 22.0%, and 11.4% of patients, respectively, and PH developed in 13.6% of patients. Over time, diffusing capacity for carbon monoxide (DL co ) and carbon monoxide transfer coefficient (K co ) declined in all SSc patients (up to 1.5% per year) but demonstrated much greater annual decline (up to 4.5% and 4.8%, respectively) in the 5–7 years preceding PH diagnosis. Comparisons between multivariable models including either DL co , K co , or forced vital capacity (FVC)/DL co ratio, demonstrated that both absolute values and change over the preceding year in those measurements were strongly associated with the risk of PH (hazard ratio [HR] 0.93 and 0.76 for K co and its change; HR 0.90 and 0.96 for DL co and its change; and HR 1.08 and 2.01 for FVC/DL co ratio and its change; P  < 0.001 for all). The K co ‐based model had the greatest discriminating ability (Harrell's C‐statistic 0.903). Conclusion Our findings strongly support the importance of PFT trends over time in identifying patients at risk of developing PH.