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Gao Y, Chen B, Han Y, Lu J, Li X, Tian A, Zhang L, Wang B, Hong Y, Liu J, Li Y, Bilige W, Zhang H, Zheng X, Li J. Prognostic Value of a Multi-mRNA Signature for 1-Year All-Cause Death in Hospitalized Patients With Heart Failure With a Preserved Ejection Fraction. Circ Heart Fail 2024; 17:e011118. [PMID: 38847104 DOI: 10.1161/circheartfailure.123.011118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 04/26/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Heart failure with preserved ejection fraction is a major global public health problem, while effective risk stratification tools are still lacking. We sought to construct a multi-mRNA signature to predict 1-year all-cause death. METHODS We selected 30 patients with heart failure with preserved ejection fraction who died during 1-year follow-up and 30 who survived in the discovery set. One hundred seventy-one and 120 patients with heart failure with preserved ejection fraction were randomly selected as a test set and a validation set, respectively. We performed mRNA microarrays in all patients. RESULTS We constructed a 5-mRNA signature for predicting 1-year all-cause death. The scores of the 5-mRNA signature were significantly associated with the 1-year risk of all-cause death in both the test set (hazard ratio, 2.72 [95% CI, 1.98-3.74]; P<0.001) and the validation set (hazard ratio, 3.95 [95% CI, 2.40-6.48]; P<0.001). Compared with a reference model, which included sex, ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) score, history of HF and NT-proBNP (N-terminal pro-B-type natriuretic peptide), the 5-mRNA signature had a better discrimination capability, with an increased area under the curve from 0.696 to 0.813 in the test set and from 0.712 to 0.848 in the validation set. A composite model integrating the 5-mRNA risk score and variables in the reference model demonstrated an excellent discrimination capability, with an area under the curve of 0.861 (95% CI, 0.784-0.939) in the test set and an area under the curve of 0.859 (95% CI, 0.755-0.963) in the validation set. The net reclassification improvement and integrated discrimination improvement indicated that the composite model significantly improved patient classification compared with the reference model in both sets (P<0.001). CONCLUSIONS The 5-mRNA signature is a promising predictive tool for 1-year all-cause death and shows improved prognostic power over the established risk scores and NT-proBNP in patients with heart failure with preserved ejection fraction.
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Affiliation(s)
- Yan Gao
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Bowang Chen
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Yi Han
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Jiapeng Lu
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Xi Li
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Aoxi Tian
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Lihua Zhang
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Bin Wang
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Yun Hong
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Jiamin Liu
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Yan Li
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Wuhan Bilige
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Haibo Zhang
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Xin Zheng
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
| | - Jing Li
- National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, China (Y.G., B.C., Y. Han, J. Lu, X. L., A.T., L.Z., B.W., Y. Hong, J. Liu, Y.L., W.B., H.Z., X.Z., J. Li)
- Central China Subcenter of National Center for Cardiovascular Diseases, Henan Cardiovascular Disease Center, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University (J. Li)
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Yuan Y, Niu Y, Ye J, Xu Y, He X, Chen S. Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis. BMC Med Genomics 2024; 17:52. [PMID: 38355637 PMCID: PMC10868111 DOI: 10.1186/s12920-024-01814-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/21/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Despite the advancements in heart failure(HF) research, the early diagnosis of HF continues to be a challenging issue in clinical practice. This study aims to investigate the genes related to myocardial fibrosis and conduction block, with the goal of developing a diagnostic model for early treatment of HF in patients. METHOD The gene expression profiles of GSE57345, GSE16499, and GSE9128 were obtained from the Gene Expression Omnibus (GEO) database. After merging the expression profile data and adjusting for batch effects, differentially expressed genes (DEGs) associated with conduction block and myocardial fibrosis were identified. Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, and gene set enrichment analysis (GSEA) were utilized for functional enrichment analysis. A protein-protein interaction network (PPI) was constructed using a string database. Potential key genes were selected based on the bioinformatics information mentioned above. SVM and LASSO were employed to identify hub genes and construct the module associated with HF. The mRNA levels of TAC mice and external datasets (GSE141910 and GSE59867) are utilized for validating the diagnostic model. Additionally, the study explores the relationship between the diagnostic model and immune cell infiltration. RESULTS A total of 395 genes exhibiting differential expression were identified. Functional enrichment analysis revealed that these specific genes primarily participate in biological processes and pathways associated with the constituents of the extracellular matrix (ECM), immune system processes, and inflammatory responses. We identified a diagnostic model consisting of 16 hub genes, and its predictive performance was validated using external data sets and a transverse aortic coarctation (TAC) mouse model. In addition, we observed significant differences in mRNA expression of 7 genes in the TAC mouse model. Interestingly, our study also unveiled a correlation between these model genes and immune cell infiltration. CONCLUSIONS We identified sixteen key genes associated with myocardial fibrosis and conduction block, as well as diagnostic models for heart failure. Our findings have significant implications for the intensive management of individuals with potential genetic variants associated with heart failure, especially in the context of advancing cell-targeted therapy for myocardial fibrosis.
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Affiliation(s)
- Yonghua Yuan
- Department of Pediatrics, Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Pediatric Cardiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Yiwei Niu
- Department of Pediatric Cardiology, Xinhua hospital, School of medicine, Shanghai Jiaotong university, Shanghai, China
| | - Jiajun Ye
- Department of Pediatric Cardiology, Xinhua hospital, School of medicine, Shanghai Jiaotong university, Shanghai, China
| | - Yuejuan Xu
- Department of Pediatric Cardiology, Xinhua hospital, School of medicine, Shanghai Jiaotong university, Shanghai, China
| | - Xuehua He
- Department of Pediatric Cardiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Sun Chen
- Department of Pediatric Cardiology, Xinhua hospital, School of medicine, Shanghai Jiaotong university, Shanghai, China.
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Wang X, Xi H, Geng X, Li Y, Zhao M, Li F, Li Z, Ji H, Tian H. Artificial Intelligence-Based Prediction of Lower Extremity Deep Vein Thrombosis Risk After Knee/Hip Arthroplasty. Clin Appl Thromb Hemost 2023; 29:10760296221139263. [PMID: 36596268 PMCID: PMC9830569 DOI: 10.1177/10760296221139263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Deep vein thrombosis (DVT) is a common postoperative complication of knee/hip arthroplasty. There is a continued need for artificial intelligence-based methods of predicting lower extremity DVT risk after knee/hip arthroplasty. In this study, we performed a retrospective study to analyse the data from patients who underwent primary knee/hip arthroplasty between January 2017 and December 2021 with postoperative bilateral lower extremity venous ultrasonography. Patients' features were extracted from electronic health records (EHRs) and assigned to the training (80%) and test (20%) datasets using six models: eXtreme gradient boosting, random forest, support vector machines, logistic regression, ensemble, and backpropagation neural network. The Caprini score was calculated according to the Caprini score measurement scale, and the corresponding optimal cut-off Caprini score was calculated according to the largest Youden index. In total, 6897 cases of knee/hip arthroplasty were included (average age, 65.5 ± 8.9 years; 1702 men), among which 1161 (16.8%) were positive and 5736 (83.2%) were negative for deep vein thrombosis. Among the six models, the ensemble model had the highest area under the curve [0.9206 (0.8956, 0.9364)], with a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of 0.8027, 0.9059, 0.6100, 0.9573 and 0.7003, respectively. The corresponding optimal cut-off Caprini score was 10, with an area under the curve, sensitivity, specificity, positive predictive value, and negative predictive values of 0.5703, 0.8915, 0.2491, 0.1937, 0.9191, and 0.3183, respectively. In conclusion, machine learning models based on EHRs can help predict the risk of deep vein thrombosis after knee/hip arthroplasty.
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Affiliation(s)
- Xinguang Wang
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Hanxu Xi
- Information Management and Big Data Centre, Peking University Third Hospital, Beijing, China
| | - Xiao Geng
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Yang Li
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Minwei Zhao
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Feng Li
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Zijian Li
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China
| | - Hong Ji
- Information Management and Big Data Centre, Peking University Third Hospital, Beijing, China
| | - Hua Tian
- Department of Orthopedics, Peking University Third Hospital, Beijing, China,Engineering Research Centre of Bone and Joint Precision Medicine, Beijing, China,Hong Ji, Information Management and Big Data Centre, Peking University Third Hospital, No.49 North Garden Road, Haidian District, Beijing 100191, China.
Hua Tian, Department of Orthopaedics, Peking University Third Hospital; Engineering Research Center of Bone and Joint Precision Medicine, No. 49 North Garden Road, Haidian District, Beijing 100191, China.
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