Singh Mandeep, Rihal Charanjit S, Lennon Ryan J, Spertus John, Rumsfeld John S, Holmes David R
Division of Cardiovascular Diseases, College of Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
Mayo Clin Proc. 2007 Jun;82(6):701-8. doi: 10.4065/82.6.701.
To derive risk models for percutaneous coronary intervention (PCI) outcomes from clinical and laboratory variables available before the procedure so they can be used for preprocedure risk stratification.
Using the Mayo Clinic registry, we analyzed 9035 PCIs on 7640 unique patients from January 1, 2000, through April 30, 2005. We included only the first PCI per patient (n=7457). Logistic regression was used to model the calculated risk score and major procedural complications. Separate risk models were made for mortality and major adverse cardiovascular events (MACE) derived solely from baseline and laboratory characteristics. Final risk scores for procedural death, defined as any death during the index hospitalization, and MACE contained the same 7 variables (age, myocardial infarction less than or equal to 24 hours, preprocedural shock, serum creatinine level, left ventricular ejection fraction, congestive heart failure, and peripheral artery disease).
Models had adequate goodness of fit, and areas under the receiver operating characteristic curve were 0.74 and 0.89 for MACE and procedural death, respectively, indicating excellent overall discrimination. The model was robust across many subgroups, including those undergoing elective PCI, those having diabetes mellitus, and elderly patients. Bootstrap analysis indicated that the model was not overfit to the available data set.
Before coronary angiography is performed, a risk-scoring system based on 7 variables can be used conveniently to predict cardiovascular complications after PCI. This model may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process.
根据手术前可用的临床和实验室变量推导经皮冠状动脉介入治疗（PCI）结果的风险模型，以便将其用于术前风险分层。
利用梅奥诊所登记处的数据，我们分析了2000年1月1日至2005年4月30日期间7640例独特患者的9035例PCI手术。我们仅纳入每位患者的首次PCI手术（n = 7457）。采用逻辑回归对计算出的风险评分和主要手术并发症进行建模。分别建立仅基于基线和实验室特征的死亡率和主要不良心血管事件（MACE）风险模型。将指数住院期间的任何死亡定义为手术死亡，其最终风险评分和MACE包含相同的7个变量（年龄、小于或等于24小时的心肌梗死、术前休克、血清肌酐水平、左心室射血分数、充血性心力衰竭和外周动脉疾病）。
模型具有良好的拟合优度，MACE和手术死亡的受试者工作特征曲线下面积分别为0.74和0.89，表明总体区分度良好。该模型在许多亚组中都很稳健，包括接受择期PCI的患者、糖尿病患者和老年患者。自助法分析表明该模型对可用数据集没有过度拟合。
在进行冠状动脉造影之前，基于7个变量的风险评分系统可方便地用于预测PCI术后的心血管并发症。作为知情同意过程的一部分，该模型可能有助于为患者提供个体化的、基于证据的手术风险评估。
