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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 蔡佳醍 | zh_TW |
dc.contributor.advisor | Chia-Ti Tsai | en |
dc.contributor.author | 陳郁安 | zh_TW |
dc.contributor.author | Yu-An Chen | en |
dc.date.accessioned | 2024-02-23T16:19:12Z | - |
dc.date.available | 2024-02-24 | - |
dc.date.copyright | 2024-02-23 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-02-01 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91857 | - |
dc.description.abstract | 背景:
冠狀動脈疾病(coronary artery disease,CAD)是一種常見的心血管疾病,也是全球主要死因之一,對人類健康造成重大影響。経皮冠狀動脈介入治療(percutaneous coronary intervention,PCI)包括球囊擴張術(balloon angioplasty)和支架置放手術等,對於打通阻塞的冠狀動脈並減少心臟肌肉損傷扮演著關鍵角色。儘管塗藥支架(drug-eluting stents,DESs)在明顯減少冠狀動脈支架內再狹窄(in-stent restenosis,ISR)方面已經有不錯的療效,但支架內再狹窄(ISR)仍然是一個嚴峻的臨床挑戰。如果能夠對支架內再狹窄(ISR)相關的臨床預測因子做更深入的了解,應能有助於再狹窄的早期檢測與治療。因此,本研究旨在探討冠狀動脈支架內再狹窄(ISR)的臨床預測因子。 方法: 本研究是單中心(single-center)、回顧性(retrospective)的隊列研究(cohort study),收錄了517名曾經接受冠狀動脈支架植入術的患者,於西元2019年1月至2022年12月期間,在國立臺灣大學醫學院附設醫院追蹤發生穩定心絞痛(stable angina)合併履帶式跑步機運動心電圖(treadmill exercise test)或核醫心肌灌注掃描(myocardial perfusion imaging)顯示陽性心肌缺氧;或者發生急性冠狀動脈症候群(acute coronary syndrome,ACS),進而接受心導管檢查。我們將患者分為兩組:一組是發生支架內再狹窄(ISR)並接受塗藥氣球(drug-coated balloon,DCB)治療的患者(177位),一組是無發生明顯支架內再狹窄(non-ISR)的患者(340位)。我們仔細檢閱病歷資料並分析其冠狀動脈造影(coronary angiography)的結果,並且結合機器學習演算模型,來評估冠狀動脈支架內再狹窄(ISR)的臨床預測因子。 結果: 研究結果顯示,發生冠狀動脈支架內再狹窄(ISR)的患者,普遍年紀較大,較多罹有複雜性冠狀動脈疾病包括左主冠狀動脈疾病(LM disease)、三條冠狀動脈疾病(three-vessel disease,3VD)、冠狀動脈慢性全阻塞(CTO),並且較多罹患第二型糖尿病(type II diabetes mellitus)、末期腎病(end stage renal disease,ESRD)以及曾經接受冠狀動脈繞道手術(coronary artery bypass graft,CABG)。此外,高齡(advanced age)(調整過的勝算比[aOR] 1.02,95%信賴區間[CI] 1.00-1.04,p = 0.037)和三條冠狀動脈疾病(3VD)患者(調整過的勝算比[aOR] 2.15,95% 信賴區間[CI] 1.03-4.46,p = 0.039)與冠狀動脈支架內再狹窄(ISR)風險的增加有顯著相關。另外,機器學習演算法得出支架內再狹窄(ISR)的最具影響力預測因子為結合三支冠狀動脈疾病(3VD)併糖尿病及年齡超過66.5歲,且BMI介於22.3至25.2 kg/m²,或年齡超過73.5歲合併冠狀動脈慢性全阻塞(CTO)。 結論: 高齡(advanced age)和複雜性冠狀動脈疾病是冠狀動脈支架內再狹窄(ISR)的獨立臨床預測因子。了解這些預測因子將有助於臨床醫師識別高風險族群,從而改善患者的照護策略。 | zh_TW |
dc.description.abstract | Background:
Coronary artery disease (CAD) is a globally acknowledged cardiovascular affliction, standing as the primary cause of mortality worldwide and impacting individuals on a global scale. Percutaneous coronary intervention (PCI), encompassing procedures such as balloon angioplasty and stenting, assumes a pivotal role in unblocking arteries and mitigating cardiac muscle damage. Despite the efficacy of drug-eluting stents (DESs) in significantly reducing coronary restenosis, in-stent restenosis (ISR) persists as a formidable clinical challenge. A profound comprehension of clinical predictors linked to ISR is indispensable for early detection and tailored treatment. Thus, this study endeavors to scrutinize the clinical predictors of ISR, focused on Taiwanese patients. Methods: A single-center, retrospective cohort study was executed, involving 517 CAD patients who had undergone prior coronary stenting and exhibited stable angina with a positive stress test or acute coronary syndrome (ACS) during follow-up between January 2019 and December 2022 at National Taiwan University Hospital. The patients were stratified into two groups: the ISR group, comprising individuals subjected to drug-coated balloon (DCB) treatment (n=177), and the non-ISR group, encompassing patients devoid of DCB treatment (n=340). We carefully reviewed medical records, analyzed the results of coronary angiography, and combined machine learning algorithm models to assess the clinical predictive factors for coronary in-stent restenosis. Results: Patients having coronary in-stent restenosis (ISR) were generally older and more likely to have complex coronary artery diseases, including left main coronary artery disease (LM disease), three-vessel disease (3VD), and chronic total occlusion (CTO). Moreover, they were more prone to having type II diabetes mellitus (T2DM), end-stage renal disease (ESRD), and a history of coronary artery bypass graft surgery (CABG). Additionally, the multivariable analysis unveiled that age (adjusted odds ratio [aOR] 1.02, 95% confidence interval [CI] 1.00 - 1.04, p = 0.037) and the presence of 3VD (aOR 2.15, 95% CI 1.03 - 4.46, p = 0.039) were significantly correlated with an escalated risk of coronary ISR. In addition, the machine learning algorithm identified the most influential predictor for in-stent restenosis (ISR) was the combination of three-vessel coronary artery disease (3VD), type 2 diabetes mellitus(T2DM), and age over 66.5 years, with a body mass index (BMI) ranging between 22.3 and 25.2 kg/m². Additionally, age over 73.5 years combined with chronic total occlusion (CTO) in the coronary artery was another significant predictor. Conclusions: Advanced age and complex coronary artery disease are independent clinical predictors of in-stent restenosis. Understanding these predictors holds the potential to guide clinicians in identifying high-risk individuals for targeted interventions, thereby refining patient care strategies. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-23T16:19:12Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-02-23T16:19:12Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 v 目錄 vii 圖目錄 ix 表目錄 x 第一章 緒論 1 1.1 研究背景 1 1.1-1 冠狀動脈介入治療的演進 1 1.1-2 冠狀動脈支架內再狹窄的機轉 1 1.2 研究目的 2 第二章 研究方法 3 2.1 研究設計 3 2.2 病患基本特性分析 3 2.3 冠狀動脈造影分析 4 2.4 決策樹模型預測支架內再狹窄 4 2.5 統計方法 5 第三章 研究結果 6 3.1 病患基本特性分析結果 6 3.2 冠狀動脈支架內再狹窄的臨床獨立預測因子 8 3.3 冠狀動脈造影與血管病灶特性 11 3.4 決策樹模型預測冠狀動脈支架內再狹窄的結果 13 第四章 討論 15 第五章 結論 18 第六章 參考文獻 19 | - |
dc.language.iso | zh_TW | - |
dc.title | 冠狀動脈疾病患者支架內再狹窄之臨床預測因子 | zh_TW |
dc.title | Clinical Predictors for In-Stent Restenosis in Patients with Coronary Artery Disease Receiving Coronary Stent Implantation | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 王怡智 | zh_TW |
dc.contributor.coadvisor | Yi-Chih Wang | en |
dc.contributor.oralexamcommittee | 吳卓鍇;柯文欽 | zh_TW |
dc.contributor.oralexamcommittee | Cho-Kai Wu;Wen-Chin Ko | en |
dc.subject.keyword | 冠狀動脈疾病,塗藥氣球,支架內再狹窄,預測因子,機器學習演算法, | zh_TW |
dc.subject.keyword | Coronary artery disease,drug-coated balloon,in-stent restenosis,predictors,machine learning algorithm, | en |
dc.relation.page | 21 | - |
dc.identifier.doi | 10.6342/NTU202400356 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2024-02-02 | - |
dc.contributor.author-college | 醫學院 | - |
dc.contributor.author-dept | 臨床醫學研究所 | - |
顯示於系所單位: | 臨床醫學研究所 |
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