Cargando…
Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model
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-P...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463535/ https://www.ncbi.nlm.nih.gov/pubmed/37612772 http://dx.doi.org/10.1186/s13075-023-03139-y |
_version_ | 1785098255457058816 |
---|---|
author | Dai, Mengmeng Zhang, Chunyi Li, Chaoying Wang, Qianqian Gao, Congcong Yue, Runzhi Yao, Menghui Su, Zhaohui Zheng, Zhaohui |
author_facet | Dai, Mengmeng Zhang, Chunyi Li, Chaoying Wang, Qianqian Gao, Congcong Yue, Runzhi Yao, Menghui Su, Zhaohui Zheng, Zhaohui |
author_sort | Dai, Mengmeng |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03139-y. |
format | Online Article Text |
id | pubmed-10463535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104635352023-08-30 Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model Dai, Mengmeng Zhang, Chunyi Li, Chaoying Wang, Qianqian Gao, Congcong Yue, Runzhi Yao, Menghui Su, Zhaohui Zheng, Zhaohui Arthritis Res Ther Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03139-y. BioMed Central 2023-08-23 2023 /pmc/articles/PMC10463535/ /pubmed/37612772 http://dx.doi.org/10.1186/s13075-023-03139-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Dai, Mengmeng Zhang, Chunyi Li, Chaoying Wang, Qianqian Gao, Congcong Yue, Runzhi Yao, Menghui Su, Zhaohui Zheng, Zhaohui Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title | Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title_full | Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title_fullStr | Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title_full_unstemmed | Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title_short | Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
title_sort | clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10463535/ https://www.ncbi.nlm.nih.gov/pubmed/37612772 http://dx.doi.org/10.1186/s13075-023-03139-y |
work_keys_str_mv | AT daimengmeng clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT zhangchunyi clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT lichaoying clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT wangqianqian clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT gaocongcong clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT yuerunzhi clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT yaomenghui clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT suzhaohui clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel AT zhengzhaohui clinicalcharacteristicsandprognosisinsystemiclupuserythematosusassociatedpulmonaryarterialhypertensionbasedonconsensusclusteringandriskpredictionmodel |