Wise Eric S, Hocking Kyle M, Brophy Colleen M
Department of Surgery, Vanderbilt University Medical Center, Nashville, Tenn.
Department of Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tenn.
J Vasc Surg. 2015 Jul;62(1):8-15. doi: 10.1016/j.jvs.2015.02.038. Epub 2015 May 5.
Ruptured abdominal aortic aneurysm (rAAA) carries a high mortality rate, even with prompt transfer to a medical center. An artificial neural network (ANN) is a computational model that improves predictive ability through pattern recognition while continually adapting to new input data. The goal of this study was to effectively use ANN modeling to provide vascular surgeons a discriminant adjunct to assess the likelihood of in-hospital mortality on a pending rAAA admission using easily obtainable patient information from the field.
Of 332 total patients from a single institution from 1998 to 2013 who had attempted rAAA repair, 125 were reviewed for preoperative factors associated with in-hospital mortality; 108 patients received an open operation, and 17 patients received endovascular repair. Five variables were found significant on multivariate analysis (P < .05), and four of these five (preoperative shock, loss of consciousness, cardiac arrest, and age) were modeled by multiple logistic regression and an ANN. These predictive models were compared against the Glasgow Aneurysm Score. All models were assessed by generation of receiver operating characteristic curves and actual vs predicted outcomes plots, with area under the curve and Pearson r(2) value as the primary measures of discriminant ability.
Of the 125 patients, 53 (42%) did not survive to discharge. Five preoperative factors were significant (P < .05) independent predictors of in-hospital mortality in multivariate analysis: advanced age, renal disease, loss of consciousness, cardiac arrest, and shock, although renal disease was excluded from the models. The sequential accumulation of zero to four of these risk factors progressively increased overall mortality rate, from 11% to 16% to 44% to 76% to 89% (age ≥ 70 years considered a risk factor). Algorithms derived from multiple logistic regression, ANN, and Glasgow Aneurysm Score models generated area under the curve values of 0.85 ± 0.04, 0.88 ± 0.04 (training set), and 0.77 ± 0.06 and Pearson r(2) values of .36, .52 and .17, respectively. The ANN model represented the most discriminant of the three.
An ANN-based predictive model may represent a simple, useful, and highly discriminant adjunct to the vascular surgeon in accurately identifying those patients who may carry a high mortality risk from attempted repair of rAAA, using only easily definable preoperative variables. Although still requiring external validation, our model is available for demonstration at https://redcap.vanderbilt.edu/surveys/?s=NN97NM7DTK.
腹主动脉瘤破裂(rAAA)即使能迅速转至医疗中心,死亡率仍很高。人工神经网络(ANN)是一种计算模型,通过模式识别提高预测能力,同时不断适应新的输入数据。本研究的目的是有效利用ANN建模,为血管外科医生提供一种判别辅助工具,以便在rAAA患者即将入院时,根据从现场轻松获取的患者信息评估院内死亡的可能性。
1998年至2013年来自单一机构的332例尝试进行rAAA修复的患者中,对125例患者的术前与院内死亡相关因素进行了回顾;108例患者接受了开放手术,17例患者接受了血管内修复。多变量分析发现5个变量具有显著性(P < 0.05),其中这5个变量中的4个(术前休克、意识丧失、心脏骤停和年龄)通过多元逻辑回归和ANN进行建模。将这些预测模型与格拉斯哥动脉瘤评分进行比较。所有模型均通过生成受试者工作特征曲线以及实际与预测结果图进行评估,以曲线下面积和Pearson r(2)值作为判别能力的主要指标。
125例患者中,53例(42%)未存活至出院。多变量分析中,5个术前因素是院内死亡的显著(P < 0.05)独立预测因素:高龄、肾病、意识丧失、心脏骤停和休克,不过肾病被排除在模型之外。这些风险因素从0个到4个的依次累积使总体死亡率逐步上升,从11%升至16%再到44%、76%、89%(年龄≥70岁被视为一个风险因素)。从多元逻辑回归、ANN和格拉斯哥动脉瘤评分模型得出的算法生成的曲线下面积值分别为0.85±0.04、0.88±0.04(训练集)和0.77±0.06,Pearson r(2)值分别为0.36、0.52和0.17。ANN模型是三者中判别能力最强的。
基于ANN的预测模型可能是血管外科医生的一种简单、有用且判别能力强的辅助工具,仅使用易于界定的术前变量就能准确识别那些rAAA修复尝试可能带来高死亡风险的患者。尽管仍需外部验证,但我们的模型可在https://redcap.vanderbilt.edu/surveys/?s=NN97NM7DTK上进行展示。