Arthritis Research & Therapy (Aug 2023)

Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model

  • Mengmeng Dai,
  • Chunyi Zhang,
  • Chaoying Li,
  • Qianqian Wang,
  • Congcong Gao,
  • Runzhi Yue,
  • Menghui Yao,
  • Zhaohui Su,
  • Zhaohui Zheng

DOI
https://doi.org/10.1186/s13075-023-03139-y
Journal volume & issue
Vol. 25, no. 1
pp. 1 – 13

Abstract

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Abstract Background Pulmonary arterial hypertension (PAH) is a severe complication of systemic lupus erythematosus (SLE). This study aims to explore the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model. Methods A total of 205 PAH (including 163 SLE-PAH and 42 idiopathic PAH) patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan–Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator combined with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model. Results Clustering analysis defined two subtypes, cluster 1 (n = 134) and cluster 2 (n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation function markers, with higher SLE disease activity, less frequency of PAH medications, and lower survival rate within 2 years (86.2% vs. 92.8%) (P < 0.05). The risk prediction model was also constructed, including older age at diagnosis (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus, and platelet distribution width (PDW). Conclusions Consensus clustering identified two distinct SLE-PAH subtypes which were associated with survival outcomes. Four prognostic factors for death were discovered to construct the SLE-PAH risk prediction model.

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