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Hospital volume & PCI complications & hospital mortality

Hospital volume & PCI complications & hospital mortality
医院量
批准号:
6584836
负责人:
SEAN C BEINART
金额:
$2.72万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
未结题
起止时间:
2003-01-06 至

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中文摘要
翻译
描述(由申请人提供):目的:这项研究的目的是利用美国心脏病学会-国家心血管数据注册中心(ACC-NCDR)来确定医院操作量是否与经皮冠状动脉介入治疗(PCI)的并发症有关。零假设是风险调整后不良结果的发生频率将与每年的经皮冠状动脉介入治疗院内容量无关。背景:几项研究已经得出结论,医院的介入治疗数量和不良结果之间存在着相反的关系,导致ACC/AHA工作组最近增加了对接受介入治疗的机构的建议的最低程序要求。然而,这些研究受到数据的限制,这些数据缺乏实质性的临床变量,代表了有限的地理区域,或者没有反映当前的经皮冠状动脉介入治疗实践。ACC-NCDR解决了这些问题,并可以为这一具有广泛卫生政策含义的重要临床问题提供更多洞察力。方法:研究对象为来自全国350多家医院的连续冠脉介入治疗患者(n~169,000)。结果变量包括住院死亡率、Q波心肌梗死、实验室并发症和急诊冠状动脉搭桥术。主要自变量将是设施程序量。风险调整模型将使用Logistic回归进行拟合。每个模型中的候选变量将包括一组非常全面的人口统计学、临床和血管造影特征。将生成显示程序量和死亡率的最终调整效果的曲线图。将确定每个地点的估计风险调整死亡率。
英文摘要
DESCRIPTION (provided by applicant): Objective: The purpose of this study is to determine whether hospital procedure volume is associated with complications of percutaneous coronary interventions (PCI) using the American College of Cardiology - National Cardiovascular Data Registry (ACC-NCDR). The null hypothesis is that the frequency of risk-adjusted adverse outcomes will be independent of annual PCI hospital volume. Background: Several studies have concluded that an inverse relationship exists between hospital PCI volume and adverse outcomes causing a recent increase in the recommended minimum procedure requirements for PCI-performing institutions by the ACC/AHA Task Force. These studies, however, were limited by data that lacked substantial clinical variables, represented a confined geographic region, or were not reflective of current PCI practice. The ACC-NCDR resolves these concerns and could provide more insight into this important clinical question with broad health policy implications. Methods: The population, were consecutive PCI patients (n~169,000) from over 350 hospitals nationwide. Outcome variables will be in-hospital mortality, Q-wave MI, lab complications, and emergent CABG. The main independent variable will be facility procedure volume. Risk adjustment models will be fit using logistic regression. Candidate variables in each of the models will include a very comprehensive set of demographic, clinical, and angiographic characteristics. A plot showing final adjusted effect of procedural volume and mortality will be generated. Estimated risk-adjusted mortality for each site will be determined.
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