Analysis of risk factors for adverse events associated with cardiogenic syncope due to coronary artery disease in the elderly
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: To analyze risk factors for adverse events associated with syncope due to coronary artery disease (CAD) in the elderly. METHODS: Two hundred eight patients with CAD who were hospitalized for cardiogenic syncope in our hospital from September 2022 to September 2023 were included in this study. Based on the follow-up results, 208 patients with cardiogenic syncope due to geriatric coronary artery disease were classified into the no-adverse group (n = 171), and the adverse group (n = 37), and the risk factors for the occurrence of adverse events in cardiogenic syncope in both groups were analyzed. RESULTS: The age differences, history of heart failure, cardiac troponin I (hs-TnT) level, N-terminal B-type natriuretic peptide proteins (NT-proBNP) level, heart rate, left ventricular ejection fraction (LVEF), and QTC abnormality between the two groups were statistically significant (P < 0.05). The COX multifactorial regression analysis revealed that hs-TnT, NT-proBNP, QTC abnormality prolongation and LVEF were all identified as risk factors for poor prognosis in elderly CAD patients (P < 0.05). proBNP, abnormal prolongation of QTC, and LVEF were identified as risk factors for cardiogenic syncope in elderly CAD patients, leading to a poor prognosis (P < 0.05). ROC curve analysis demonstrated that combining hs-TnT, NT-proBNP, QTC, and LVEF tests resulted in higher diagnostic accuracy than a single test alone, significantly improving the diagnostic accuracy (P < 0.05). CONCLUSION: High hs-TnT and NT-proBNP levels, abnormally prolonged QTC, and LVEF > 50% are risk factors for cardiogenic syncope leading to adverse events in elderly CAD patients. The clinical Trial Number of this study is CLTR202356423, and it was registered in 2023.
Source: BMC Cardiovascular Disorders
Authors: Yangyang Liang, Xinping Li, Shuguang Chen, Qiaoying Cha, LiYa Yang
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,090
OpenAlex ID: W4410096367