Department of Anaesthesia and Perioperative Medicine, Auckland City Hospital, Auckland, New Zealand.
Orion Health, Auckland, New Zealand; Department of Statistics, University of Auckland, Auckland, New Zealand.
Eur J Vasc Endovasc Surg. 2021 Apr;61(4):657-663. doi: 10.1016/j.ejvs.2020.12.008. Epub 2021 Jan 8.
Risk calculators and prediction models are available to assist clinicians and patients with peri-operative decision making to optimise outcomes. In a vascular surgical setting, the majority of these models is based on open AAA repair outcomes, and in general their clinical use is limited. The objective of this study was to develop and validate a simple and accurate vascular surgical risk prediction model.
A national administrative database was accessed to collect information on all adult patients undergoing vascular surgery between 1 July 2011 and 30 June 2016 in New Zealand. The primary outcomes were mortality at 30 days, one year, and two years. Previously established covariables including American Society of Anaesthesiologists (ASA) physical status score, sex, surgical urgency, cancer status and ethnicity were tested, and other covariables such as smoking status, presence of renal failure, diabetes, anatomical site of operation, structure operated, and type of procedures (open or endovascular) were explored. LASSO regression was used to select variables for inclusion in the model.
A total of 21 597 cases formed the final risk prediction models, with covariables including ASA score, gender, surgical urgency, cancer status, presence of renal failure, diabetes, anatomical site, structure operated, and endovascular procedure. The area under the receiver operating curve (AUROC) for 30 day, one year, and two year mortality using L-min model was 0.869, 0.833, and 0.824, respectively, demonstrating very good discrimination. Calibration with the validation dataset was also excellent, with slopes of 0.971, 1.129, and 1.011, respectively, and McFadden's pseudo-R statistics of 0.250, 0.227, and 0.227, respectively.
A simple and accurate multivariable risk calculator for vascular surgical patients was developed and validated using the New Zealand national dataset, with excellent discrimination and calibration for 30 day, one year, and two year mortality.
风险计算器和预测模型可用于协助临床医生和患者进行围手术期决策，以优化结果。在血管外科环境中，这些模型中的大多数都是基于开放式 AAA 修复结果的，一般来说，其临床应用受到限制。本研究的目的是开发和验证一种简单而准确的血管外科风险预测模型。
访问国家行政数据库，收集 2011 年 7 月 1 日至 2016 年 6 月 30 日期间在新西兰接受血管手术的所有成年患者的信息。主要结局是 30 天、1 年和 2 年的死亡率。测试了先前确定的协变量，包括美国麻醉医师协会（ASA）身体状况评分、性别、手术紧急程度、癌症状态和种族，并探索了其他协变量，如吸烟状况、肾衰竭、糖尿病、手术部位、手术结构和手术类型（开放或血管内）。LASSO 回归用于选择纳入模型的变量。
共有 21597 例病例构成最终的风险预测模型，协变量包括 ASA 评分、性别、手术紧急程度、癌症状态、肾衰竭、糖尿病、手术部位、手术结构和血管内手术。使用 L-min 模型的 30 天、1 年和 2 年死亡率的接收器工作特征曲线（AUROC）面积分别为 0.869、0.833 和 0.824，表明具有很好的区分度。与验证数据集的校准也非常出色，斜率分别为 0.971、1.129 和 1.011，McFadden 的伪 R 统计量分别为 0.250、0.227 和 0.227。
使用新西兰国家数据集开发和验证了一种简单而准确的血管外科患者多变量风险计算器，对 30 天、1 年和 2 年死亡率具有良好的区分度和校准度。
