O27 Development and validation of a multivariable model for 5-year survival in systemic lupus erythematosus-associated pulmonary arterial hypertension: CSTAR-PAH cohort study

Background Pulmonary arterial hypertension (PAH) is the major mode of death in systemic lupus erythematosus (SLE), but there is no validated algorithm to identify those at highest risk. Methods A prognostic model was developed from a multicenter, longitudinal cohort study of 310 consecutively evaluated patients with SLE-associated PAH. The study was conducted from November 2006 to January2019. Death was the primary outcome. The model was developed using Cox proportional hazards regression modeling. We developed a prognostic index (PI), summing the number of risk points corresponding to weighted covariates, which were used to configure the nomogram. Internal validation of the nomogram was assessed by discrimination and calibration using bootstrapping. Results Of the 310 patients included in the study, 68 deaths (22.2%) occurred at a median follow-up of 4.9 (interquartile range [IQR] 3.2–6.3 years) years. The final prognostic model included 6 variables: N terminal-pro brain natriuretic peptide (NT-proBNP), Lactic Dehydrogenase (LDH), Direct Bilirubin (DBIL), 6-minute walking distance (6MWD), serositis and alopecia. 5-year survival probability-predictive nomogram with PI in the main analysis was established (figure 1). The model’s ability to predict risk was validated with C statistic (0.82[95%CI, 0.73–0.91]) and calibration curve. Risk stratification was made based on PI to improve the primary prevention and management of SLE-associated PAH. Conclusions This new, validated risk stratification model for SLE-PAH may provide individualized estimates of risk at 5 years using readily obtained clinical risk factors. External validation studies are required to demonstrate the accuracy of this model’s predictions in diverse patient populations. Acknowledgements This work was supported by the Chinese National Key Technology R&D Program, Ministry of Science and Technology (2017YFC0907601, 2017YFC0907602) and the Chinese National High Technology Research and Development Program, Ministry of Science and Technology (2012AA02A513).