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血管研究组新英格兰风险预测模型在外科学会血管质量倡议中的择期腹主动脉瘤修复术后死亡率的外部验证,并与现有模型进行比较。

External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

机构信息

Division of Vascular Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pa.

Department of Biostatistics, Boston University School of Public Health, Boston, Mass.

出版信息

J Vasc Surg. 2018 Jan;67(1):143-150. doi: 10.1016/j.jvs.2017.05.087. Epub 2017 Aug 12.

Abstract

OBJECTIVE

The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample.

METHODS

The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles.

RESULTS

Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0.639). Across the 5 risk quintiles, the VSGNE model predicted observed mortality significantly with great accuracy.

CONCLUSIONS

This simple VSGNE AAA risk predictive model showed very high discriminative ability in predicting mortality after elective AAA repair among a large external independent sample of AAA cases performed by a diverse array of physicians nationwide. The risk score based on this simple VSGNE model can reliably stratify patients according to their risk of mortality after elective AAA repair better than other established models.

摘要

目的

本研究旨在对新英格兰血管研究组(VSGNE)报告的择期腹主动脉瘤(AAA)修复术后死亡率的术后风险预测模型进行外部验证,并比较其在不同患者风险类别中的预测能力,以及与使用血管质量倡议(VQI)AAA 样本的既定风险预测模型相比的预测能力。

方法

在 VQI AAA 数据库(2010-2015 年)中查询接受择期 AAA 修复的患者。VSGNE 病例被排除在 VQI 样本之外。使用 VQI 择期 AAA 修复样本对最近发表的 VSGNE AAA 风险预测模型(仅包括术前变量[年龄、性别、冠状动脉疾病史、慢性阻塞性肺疾病、脑血管疾病、肌酐水平和动脉瘤大小]和计划的修复类型)进行了外部验证。通过 C 统计量评估模型的预测价值。Hosmer-Lemeshow 法用于评估校准和拟合优度。然后,将该模型与 Medicare、Vascular Governance Northwest 模型和格拉斯哥动脉瘤评分进行比较,以预测 VQI 样本中的死亡率。Vuong 检验用于比较模型之间的拟合优度。在不同风险组的 VQI 五分位数中评估模型的区分能力。

结果

从 VSGNE 样本中获得 4431 例数据,总死亡率为 1.4%,用于建立模型。在 VSGNE 择期 AAA 修复样本中,内部验证的 VSGNE 模型在预测死亡率方面具有非常高的区分能力(C=0.822),并且模型拟合良好(Hosmer-Lemeshow P=0.309)。在总体死亡率为 0.9%的 16989 例 VQI 病例的外部验证中,死亡率的预测能力非常稳健(C=0.802)。Vuong 检验表明,VSGNE 优于 Medicare 模型(C=0.780)、Vascular Governance Northwest(0.774)和格拉斯哥动脉瘤评分(0.639)的拟合优度存在显著差异。在 5 个风险五分位数中,VSGNE 模型能够准确预测观察到的死亡率。

结论

在全国范围内由各种医生进行的大量 AAA 病例的大型外部独立样本中,这种简单的 VSGNE AAA 风险预测模型在预测择期 AAA 修复术后死亡率方面具有非常高的区分能力。基于该简单 VSGNE 模型的风险评分可以根据患者择期 AAA 修复后的死亡率风险对患者进行更可靠的分层,优于其他既定模型。

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