Jingge Qu1, Mengtao Li1, Yanhong Wang2, Xinwang Duan3, Hui Luo4, Cheng Zhao5, Feng Zhan6, Zhenbiao Wu7, Hongbin Li8, Min Yang9, Jian Xu10, Wei Wei11, Lijun Wu12, Yongtai Liu13, Hanxiao You1, Juyan Qian1, Xiaoxi Yang1, Can Huang1, Jiuliang Zhao1, Qian Wang1, Xiaomei Leng1, Xinping Tian1, Yan Zhao1, Xiaofeng Zeng1. 1. Department of Rheumatology and Clinical Immunology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital (PUMCH), Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China. 2. Department of Epidemiology and Bio-statistics, Institute of Basic Medical Sciences, Academy of Medical Sciences &, Peking Union Medical College, Beijing, China. 3. Department of Rheumatology, The Second Affiliated Hospital of Nanchang University, Nanchang, China. 4. Department of Rheumatology, Xiangya Hospital, Central South University, Changsha, China. 5. Department of Rheumatology and Clinical Immunology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. 6. Department of Rheumatology, Hainan General Hospital, Hainan affiliated Hospital of Hainan Medical University, Haikou, China. 7. Department of Clinical Immunology and Rheumatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China. 8. Department of Rheumatology, Affiliated Hospital of Inner Mongolia Medical College, Hohhot, China. 9. Department of Rheumatology and Immunology, Nanfang Hospital, Southern Medical University, Guangzhou, China. 10. Department of Rheumatology, First Affiliated Hospital of Kunming Medical University, Kunming, China. 11. Department of Rheumatology, Tianjin Medical University General Hospital, Tianjin, China. 12. Department of Rheumatology, People Hospital of Xinjiang Uygur Autonomous Region, Urumchi, China. 13. Department of Cardiology, Union Medical College Hospital (PUMCH), Center for Rare Diseases Research, Peking Union Medical College &, Chinese Academy of Medical Sciences, Beijing, China.
Abstract
OBJECTIVES: Pulmonary arterial hypertension is a life-threatening complication of systemic lupus erythematosus. However, there is no algorithm to identify those at high risk. We aimed to develop a prediction model for pulmonary arterial hypertension in lupus patients that provides individualized risk estimates. METHODS: A multicenter, longitudinal cohort study was undertaken from January 2003 to January 2020. The study collected data on 3,624 consecutively evaluated patients diagnosed with lupus. The diagnosis of pulmonary arterial hypertension was confirmed by right heart catheterization. Cox proportional hazards regression and least absolute shrinkage and selection operator were used to fit the model. Model discrimination, calibration, and decision curve analysis were assessed for validation. RESULTS: Ninety-two lupus patients developed pulmonary arterial hypertension (2.54%) at a median follow-up of 4.84 years (interquartile range, 2.42-8.84). The final prediction model included five clinical variables (acute/subacute cutaneous lupus, arthritis, renal disorder, thrombocytopenia, and interstitial lung disease) and three autoantibodies (anti-RNP, anti-Ro/SSA and anti-La/SSB). A 10-year pulmonary arterial hypertension probability-predictive nomogram was established. The model was internally validated by C statistic (0.78), the Brier score (0.03), and a satisfactory calibration curve. According to the net benefit and predicted probability thresholds, we recommend annual screening in high-risk (> 4.62 %) lupus patients. CONCLUSION: We developed a risk stratification model using routine clinical assessments. This new tool may effectively predict the future risk of pulmonary arterial hypertension in patients with systemic lupus erythematosus. This article is protected by copyright. All rights reserved.
OBJECTIVES:Pulmonary arterial hypertension is a life-threatening complication of systemic lupus erythematosus. However, there is no algorithm to identify those at high risk. We aimed to develop a prediction model for pulmonary arterial hypertension in lupuspatients that provides individualized risk estimates. METHODS: A multicenter, longitudinal cohort study was undertaken from January 2003 to January 2020. The study collected data on 3,624 consecutively evaluated patients diagnosed with lupus. The diagnosis of pulmonary arterial hypertension was confirmed by right heart catheterization. Cox proportional hazards regression and least absolute shrinkage and selection operator were used to fit the model. Model discrimination, calibration, and decision curve analysis were assessed for validation. RESULTS: Ninety-two lupuspatients developed pulmonary arterial hypertension (2.54%) at a median follow-up of 4.84 years (interquartile range, 2.42-8.84). The final prediction model included five clinical variables (acute/subacute cutaneous lupus, arthritis, renal disorder, thrombocytopenia, and interstitial lung disease) and three autoantibodies (anti-RNP, anti-Ro/SSA and anti-La/SSB). A 10-year pulmonary arterial hypertension probability-predictive nomogram was established. The model was internally validated by C statistic (0.78), the Brier score (0.03), and a satisfactory calibration curve. According to the net benefit and predicted probability thresholds, we recommend annual screening in high-risk (> 4.62 %) lupuspatients. CONCLUSION: We developed a risk stratification model using routine clinical assessments. This new tool may effectively predict the future risk of pulmonary arterial hypertension in patients with systemic lupus erythematosus. This article is protected by copyright. All rights reserved.