Multimorbidity and Mortality Models to Predict Complications Following Percutaneous Coronary Interventions.

Mandeep Singh, Rajiv Gulati, Bradley R Lewis, Zhaoliang Zhou, Mohamad Alkhouli, Paul Friedman, Malcolm R Bell
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引用次数: 3

Abstract

Background: Previous percutaneous coronary intervention risk models were focused on single outcome, such as mortality or bleeding, etc, limiting their applicability. Our objective was to develop contemporary percutaneous coronary intervention risk models that not only determine in-hospital mortality but also predict postprocedure bleeding, acute kidney injury, and stroke from a common set of variables.

Methods: We built risk models using logistic regression from first percutaneous coronary intervention for any indication per patient (n=19 322, 70.6% with acute coronary syndrome) using the Mayo Clinic registry from January 1, 2000 to December 31, 2016. Approval for the current study was obtained from the Mayo Foundation Institutional Review Board. Patients with missing outcomes (n=4183) and those under 18 (n=10) were removed resulting in a sample of 15 129. We built both models that included procedural and angiographic variables (Models A) and precatheterization model (Models B).

Results: Death, bleeding, acute kidney injury, and stroke occurred in 247 (1.6%), 650 (4.3%), 1184 (7.8%), and 67 (0.4%), respectively. The C statistics from the test dataset for models A were 0.92, 0.70, 0.77, and 0.71 and for models B were 0.90, 0.67, 0.76, and 0.71 for in-hospital death, bleeding, acute kidney injury, and stroke, respectively. Bootstrap analysis indicated that the models were not overfit to the available dataset. The probabilities estimated from the models matched the observed data well, as indicated by the calibration curves. The models were robust across many subgroups, including women, elderly, acute coronary syndrome, cardiogenic shock, and diabetes.

Conclusions: The new risk scoring models based on precatheterization variables and models including procedural and angiographic variables accurately predict in-hospital mortality, bleeding, acute kidney injury, and stroke. The ease of its application will provide useful prognostic and therapeutic information to both patients and physicians.

预测经皮冠状动脉介入术后并发症的多发病率和死亡率模型。
背景:以往的经皮冠状动脉介入治疗风险模型多集中于单一结局,如死亡率或出血等,限制了其适用性。我们的目的是建立当代经皮冠状动脉介入手术风险模型,不仅可以确定住院死亡率,还可以从一组共同的变量中预测术后出血、急性肾损伤和中风。方法:利用梅奥诊所2000年1月1日至2016年12月31日登记的每位患者(n=19 3222, 70.6%为急性冠状动脉综合征)首次经皮冠状动脉介入治疗的logistic回归建立风险模型。目前的研究得到了梅奥基金会机构审查委员会的批准。结果缺失的患者(n=4183)和18岁以下的患者(n=10)被剔除,样本为15129。我们建立了两个模型,包括手术和血管造影变量(模型A)和导管预置模型(模型B)。结果:分别有247例(1.6%)、650例(4.3%)、1184例(7.8%)和67例(0.4%)发生死亡、出血、急性肾损伤和中风。模型A在住院死亡、出血、急性肾损伤和中风方面的C统计量分别为0.92、0.70、0.77和0.71,模型B在住院死亡、出血、急性肾损伤和中风方面的C统计量分别为0.90、0.67、0.76和0.71。Bootstrap分析表明,模型与现有数据集没有过拟合。校正曲线表明,模型估计的概率与观测数据吻合良好。该模型在许多亚组中都是稳健的,包括女性、老年人、急性冠状动脉综合征、心源性休克和糖尿病。结论:基于置管前变量和包括程序和血管造影变量的新风险评分模型能准确预测院内死亡率、出血、急性肾损伤和脑卒中。其应用的便利性将为患者和医生提供有用的预后和治疗信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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