Multicenter preoperative stroke risk index for patients undergoing coronary artery bypass graft surgery. Multicenter Study of Perioperative Ischemia (McSPI) Research Group.
Multicenter preoperative stroke risk index for patients undergoing coronary artery bypass graft surgery. Multicenter Study of Perioperative Ischemia (McSPI) Research Group.
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发表时间：
1996-11
期刊：
影响因子：
37.8
通讯作者：
M. Newman;R. Wolman;M. Kanchuger;K. Marschall;Christina T. Mora-Mangano;G. Roach;L. Smith;A. Aggarwal;N. Nussmeier;A. Herskowitz;D. Mangano
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作者：
M. Newman;R. Wolman;M. Kanchuger;K. Marschall;Christina T. Mora-Mangano;G. Roach;L. Smith;A. Aggarwal;N. Nussmeier;A. Herskowitz;D. Mangano
背景技术目前心脏手术的矛盾之处在于，与药物治疗相比，更多老年和虚弱的患者从心脏手术中受益最多，但他们在心脏手术后承受着更大的发病率和死亡率的总体风险。本研究的目的是制定一个术前指数，预测接受冠状动脉搭桥手术的患者围术期主要神经系统事件。方法和结果 作为前瞻性、多中心、观察性研究（McSPI 研究组）的一部分，我们在美国 24 个学术医疗中心招募了 2417 名患者。术中死亡或同时进行心脏直视手术的患者被排除在分析之外，总共有 2107 名患者可供分析。 68 名患者 (3.2%) 出现不良神经系统事件，定义为脑血管意外、短暂性脑缺血发作 (TIA) 或持续性昏迷。应用双变量分析来确定术前变量和神经系统事件之间的关联。确定显着的双变量预测因子，然后进行逻辑分组，并根据主成分计算每个聚类的分数。关键预测变量是年龄、既往神经系统疾病史、糖尿病、血管疾病史、既往冠状动脉手术、不稳定心绞痛和肺部疾病史，其系数用于制定术前卒中风险指数，并通过 bootstrap 进行验证（c 指数 = 0.778）。然后可以确定每位患者的中风风险，计算患者在 95% 置信区间内的中风风险。结论 通过本研究中开发的 McSPI 卒中风险指数，可以评估神经系统风险，并确定最合适的围手术期治疗组。然而，该指数的进一步完善和验证是必要的，并且当前的研究正在进行中。
BACKGROUND The paradox of present cardiac surgery is that the more elderly and debilitated patients benefit most from cardiac surgery compared with medical therapy, yet they sustain greater overall risk for morbidity and mortality after cardiac surgery. The goal of the present study was to develop a preoperative index predicting major perioperative neurological events in patients undergoing coronary artery bypass graft surgery. METHODS AND RESULTS As part of a prospective, multicenter, observational study (McSPI Research Group), we enrolled 2417 patients at 24 academic medical centers in the United States. Patients who died intraoperatively or had concomitant open-heart procedures were excluded from analysis, resulting in a total of 2107 for analysis. Sixty-eight patients (3.2%) developed adverse neurological events, defined as cerebrovascular accident, transient ischemic attack (TIA), or persistent coma. Bivariate analysis was applied to determine associations between preoperative variables and neurological events. Significant bivariate predictors were identified then logically grouped, and for each cluster, a score was calculated based on principal components. Key predictor variables were age, history of previous neurological disease, diabetes, history of vascular disease, previous coronary artery surgery, unstable angina, and history of pulmonary disease, the coefficients for which were used to develop a preoperative stroke risk index that was validated by bootstrap (c-index = 0.778). Stroke risk could then be determined for each patient, calculating a patient's risk for stroke within 95% confidence intervals. CONCLUSIONS With the McSPI stroke risk index developed in this study, neurological risk can be estimated, and the most appropriate group for perioperative therapy can be identified. Further refinement and validation of this index, however, are necessary and are under way in current studies.