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.

中文翻译：

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预测经皮冠状动脉介入治疗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侧重于单一结局，如死亡率或出血等，限制了其适用性。我们的目标是开发当代经皮冠状动脉介入治疗风险模型，该模型不仅可以确定住院死亡率，还可以通过一组常见变量预测术后出血、急性肾损伤和中风。方法：我们使用第一次经皮冠状动脉介入治疗的逻辑回归建立风险模型2000 年 1 月 1 日至 2016 年 12 月 31 日期间，使用 Mayo Clinic 登记处对每位患者的任何适应症（n = 19 322，70.6% 患有急性冠状动脉综合征）进行冠状动脉介入治疗。当前研究的批准获得了 Mayo Foundation 机构审查委员会. 结果缺失的患者 (n=4183) 和 18 岁以下的患者 (n=10) 被移除，结果样本为 15129。我们建立了两个模型，包括程序和血管造影变量（模型 A）和导管前模型（模型 B）。结果：死亡、出血、急性肾损伤和卒中分别发生在 247 人（1.6%）、650 人（4.3%）、1184 人（7.8%）和 67 人（0.4%）。模型 A 的测试数据集的 C 统计量分别为 0.92、0.70、0.77 和 0.71，模型 B 的院内死亡、出血、急性肾损伤和中风分别为 0.90、0.67、0.76 和 0.71。Bootstrap 分析表明模型没有过度拟合可用的数据集。如校准曲线所示，从模型估计的概率与观察到的数据很好地匹配。这些模型在许多亚组中都很稳健，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：新的风险评分模型基于导管前变量和模型，包括程序和血管造影变量，准确预测住院死亡率、出血、急性肾损伤和中风。其应用的简便性将为患者和医生提供有用的预后和治疗信息。