Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada; Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, ON, Canada.
Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, ON, Canada; Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, ON, Canada.
Br J Anaesth. 2022 Oct;129(4):506-514. doi: 10.1016/j.bja.2022.07.019. Epub 2022 Aug 26.
Preoperative frailty is associated with increased risk of postoperative mortality and complications. Routine preoperative frailty assessment is underperformed. Automation of preoperative frailty assessment using electronic health data could improve adherence to guideline-based care if an accurate instrument is identified.
We conducted a retrospective cohort study of adults >65 yr undergoing elective noncardiac surgery between 2012 and 2018. Four frailty instruments were compared: Frailty Index, Hospital Frailty Risk Score, Risk Analysis Index-Administrative, and Adjusted Clinical Groups frailty-defining diagnoses indicator. We compared the predictive performance of each instrument added to a baseline model (age, sex, ASA physical status, and procedural risk) using discrimination, calibration, explained variance, net reclassification, and Brier score (binary outcomes); and explained variance, root mean squared error, and mean absolute prediction error (continuous outcomes). Primary outcome was 30-day mortality. Secondary outcomes included 365-day mortality, length of stay, non-home discharge, days alive at home, and 365-day costs.
For this study, 171 576 patients met the inclusion criteria; 1370 (0.8%) died within 30 days. Compared with the baseline model predicting 30-day mortality (area under the curve [AUC] 0.85; R 0.08), the addition of Hospital Frailty Risk Score led to the greatest improvement in discrimination (AUC 0.87), explained variance (R 0.09), and net reclassification (Net Reclassification Index 0.65). Brier and calibration scores were comparable.
All four frailty instruments significantly improved discrimination and risk reclassification when added to typically assessed preoperative risk factors. Accurate identification of the presence or absence of preoperative frailty using electronic frailty instruments may improve perioperative risk stratification. Future research should evaluate the impact of automated frailty assessment in guiding surgical planning and patient-centred optimisation amongst older surgical patients.
术前虚弱与术后死亡率和并发症风险增加相关。常规的术前虚弱评估执行不佳。如果能识别出准确的工具，那么使用电子健康数据对术前虚弱进行自动化评估可能会提高基于指南的护理的依从性。
我们对 2012 年至 2018 年间接受择期非心脏手术的>65 岁成年人进行了回顾性队列研究。比较了 4 种虚弱工具：衰弱指数、医院衰弱风险评分、风险分析指数-行政和调整后的临床分组虚弱定义诊断指标。我们比较了每个工具添加到基线模型（年龄、性别、ASA 身体状况和手术风险）后的预测性能，使用区分度、校准、解释方差、净再分类和 Brier 评分（二分类结局）；以及解释方差、均方根误差和平均绝对预测误差（连续结局）。主要结局为 30 天死亡率。次要结局包括 365 天死亡率、住院时间、非家庭出院、在家存活天数和 365 天费用。
在这项研究中，171576 名患者符合纳入标准；30 天内死亡 1370 例（0.8%）。与预测 30 天死亡率的基线模型（曲线下面积[AUC]0.85；R0.08）相比，添加医院衰弱风险评分可显著提高区分度（AUC0.87）、解释方差（R0.09）和净再分类（净再分类指数 0.65）。Brier 和校准评分相当。
当将所有四种虚弱工具添加到通常评估的术前危险因素中时，均显著提高了区分度和风险再分类。使用电子虚弱工具准确识别术前虚弱的存在或不存在可能会改善围手术期风险分层。未来的研究应评估自动化虚弱评估在指导老年手术患者的手术计划和以患者为中心的优化方面的影响。
