Dewey Todd M, Brown David, Ryan William H, Herbert Morley A, Prince Syma L, Mack Michael J
Cardiopulmonary Research Science and Technology Institute, Dallas, Tex, USA.
J Thorac Cardiovasc Surg. 2008 Jan;135(1):180-7. doi: 10.1016/j.jtcvs.2007.09.011. Epub 2007 Nov 26.
Risk algorithms were used to identify a high-risk population for transcatheter aortic valve implantation instead of standard aortic valve replacement in patients with aortic stenosis. We evaluated the efficacy of these methods for predicting outcomes in high-risk patients undergoing aortic valve replacement.
Data were collected on 638 patients identified as having isolated aortic valve replacement between January 1, 1998 and December 31, 2006, using The Society of Thoracic Surgeons (STS) database. Long-term survival was determined from the Social Security Death Index or family contact. Operative risk was calculated using the STS Predicted Risk of Mortality, the EuroSCORE logistic and additive algorithms, and the Ambler Risk Score. Patients at or above the 90th percentile of risk (8.38% for STS, 33.47% for logistic, 12% for additive, 14.3% for Ambler) were identified as high risk. We then compared actual with predicted mortality and each algorithm's ability to identify patients with the worst long-term survival.
Operative mortality was 24 of 638 (3.76%). An additional 121 (19.0%) patients died during the follow-up study period (mean 4.2 +/- 2.7 years). Overall mortality was 145 of 638 (22.7%). Expected versus observed mortality for the high-risk group by algorithm was 13.3% versus 18.8% for STS, 50.9% versus 15.6% for logistic, 14.0% versus 11.9% for additive, and 19.0% versus 13.4% by Ambler. Long-term mortality, per high-risk group, was 64.1% in the STS Predicted Risk of Mortality, 45.3% in the logistic, 45.2% in the additive, and 40.2% in Ambler Risk Score. Logistic regression showed that the STS algorithm was the most sensitive in defining the patients most at risk for long-term mortality.
The STS Predicted Risk of Mortality most accurately predicted perioperative and long-term mortality for the highest risk patients having aortic valve replacement.
使用风险算法来识别主动脉瓣狭窄患者中适合经导管主动脉瓣植入而非标准主动脉瓣置换的高危人群。我们评估了这些方法对预测接受主动脉瓣置换的高危患者预后的有效性。
利用胸外科医师协会（STS）数据库收集了1998年1月1日至2006年12月31日期间638例被确定为单纯主动脉瓣置换患者的数据。通过社会保障死亡指数或与家属联系来确定长期生存率。使用STS预测死亡风险、欧洲心脏手术风险评估系统（EuroSCORE）逻辑回归和相加算法以及安布勒风险评分来计算手术风险。风险处于或高于第90百分位数（STS为8.38%，逻辑回归为33.47%，相加算法为12%，安布勒为14.3%）的患者被确定为高危患者。然后我们比较了实际死亡率与预测死亡率以及每种算法识别长期生存率最差患者的能力。
638例患者中有24例（3.76%）手术死亡。在随访研究期间，另有121例（19.0%）患者死亡（平均4.2±2.7年）。总体死亡率为638例中的145例（22.7%）。按算法计算，高危组预期死亡率与观察到的死亡率相比，STS为13.3%对18.8%，逻辑回归为50.9%对15.6%，相加算法为14.0%对11.9%，安布勒为19.0%对13.4%。每个高危组的长期死亡率，STS预测死亡风险为64.1%，逻辑回归为45.3%，相加算法为45.2%，安布勒风险评分为40.2%。逻辑回归显示，STS算法在定义长期死亡率风险最高的患者方面最敏感。
STS预测死亡风险最准确地预测了接受主动脉瓣置换的最高危患者的围手术期和长期死亡率。
