Cambridge Vascular Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
Cambridge Vascular Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
J Vasc Surg. 2015 Jan;61(1):35-43. doi: 10.1016/j.jvs.2014.06.002. Epub 2014 Jun 28.
Accurate adjustment of surgical outcome data for risk is vital in an era of surgeon-level reporting. Current risk prediction models for abdominal aortic aneurysm (AAA) repair are suboptimal. We aimed to develop a reliable risk model for in-hospital mortality after intervention for AAA, using rigorous contemporary statistical techniques to handle missing data.
Using data collected during a 15-month period in the United Kingdom National Vascular Database, we applied multiple imputation methodology together with stepwise model selection to generate preoperative and perioperative models of in-hospital mortality after AAA repair, using two thirds of the available data. Model performance was then assessed on the remaining third of the data by receiver operating characteristic curve analysis and compared with existing risk prediction models. Model calibration was assessed by Hosmer-Lemeshow analysis.
A total of 8088 AAA repair operations were recorded in the National Vascular Database during the study period, of which 5870 (72.6%) were elective procedures. Both preoperative and perioperative models showed excellent discrimination, with areas under the receiver operating characteristic curve of .89 and .92, respectively. This was significantly better than any of the existing models (area under the receiver operating characteristic curve for best comparator model, .84 and .88; P < .001 and P = .001, respectively). Discrimination remained excellent when only elective procedures were considered. There was no evidence of miscalibration by Hosmer-Lemeshow analysis.
We have developed accurate models to assess risk of in-hospital mortality after AAA repair. These models were carefully developed with rigorous statistical methodology and significantly outperform existing methods for both elective cases and overall AAA mortality. These models will be invaluable for both preoperative patient counseling and accurate risk adjustment of published outcome data.
在外科医生层面报告的时代,准确调整手术结果数据以进行风险评估至关重要。目前用于腹主动脉瘤(AAA)修复的风险预测模型并不理想。我们旨在使用严格的现代统计技术处理缺失数据,为 AAA 干预后的住院死亡率开发可靠的风险模型。
我们使用英国国家血管数据库在 15 个月期间收集的数据,应用多元插补方法和逐步模型选择,使用可用数据的三分之二生成 AAA 修复后住院死亡率的术前和围手术期模型。然后通过接受者操作特征曲线分析评估剩余三分之一数据上的模型性能,并与现有的风险预测模型进行比较。通过 Hosmer-Lemeshow 分析评估模型校准。
在研究期间,国家血管数据库共记录了 8088 例 AAA 修复手术,其中 5870 例(72.6%)为择期手术。术前和围手术期模型均显示出出色的区分度,接受者操作特征曲线下面积分别为 0.89 和 0.92。这明显优于任何现有的模型(最佳比较模型的接受者操作特征曲线下面积,0.84 和 0.88;P<0.001 和 P=0.001,分别)。当仅考虑择期手术时,区分度仍然很好。Hosmer-Lemeshow 分析没有证据表明存在校准错误。
我们已经开发出准确的模型来评估 AAA 修复后住院死亡率的风险。这些模型是通过严格的统计方法精心开发的,对于择期病例和总体 AAA 死亡率,都明显优于现有的方法。这些模型对于术前患者咨询和准确调整已发表的结果数据的风险将非常有价值。