Menzenbach Jan, Kirfel Andrea, Guttenthaler Vera, Feggeler Johanna, Hilbert Tobias, Ricchiuto Arcangelo, Staerk Christian, Mayr Andreas, Coburn Mark, Wittmann Maria
Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
J Clin Anesth. 2022 Jun;78:110684. doi: 10.1016/j.jclinane.2022.110684. Epub 2022 Feb 18.
To develop and validate a pragmatic risk screening score for postoperative delirium (POD) based on routine preoperative data.
Prospective observational monocentric trial.
Preoperative data and POD assessment were collected from cardiac and non-cardiac surgical patients at a German university hospital. Data-driven modelling approaches (step-wise vs. component-wise gradient boosting on complete and restricted predictor set) were compared to predictor selection by experts (investigators vs. external Delphi survey).
Inpatients (≥60 years) scheduled for elective surgery lasting more than 60 min.
POD was assessed daily during first five postoperative or post-sedation days with confusion assessment method for intensive and standard care unit (CAM-ICU/CAM), 4 'A's test (4AT) and Delirium Observation Screening (DOS) scale.
From 1023 enrolled patients, 978 completed observations were separated in development (n = 600; POD incidence 22.2%) and validation (n = 378; POD incidence 25.7%) cohorts. Data-driven approaches generated models containing laboratory values, surgical discipline and several items on cognitive and quality of life assessment, which are time consuming to collect. Boosting on complete predictor set yielded the highest bootstrapped prediction accuracy (AUC 0.767) by selecting 12 predictors, with substantial dependence on cardiac surgery. Investigators selected via univariate comparison age, ASA and NYHA classification, surgical risk as well as ´serial subtraction´ and ´sentence repetition´ of the Montreal Cognitive Assessment (MoCA) to enable rapid collection of their risk score for preoperative screening. This investigator model provided slightly lower bootstrapped prediction accuracy (AUC 0.746) but proved to have robust results on validation cohort (AUC 0.725) irrespective of surgical discipline. Simplification of the investigator model by scaling and rounding of regression coefficients into the PROPDESC score achieved a comparable precision on the validation cohort (AUC 0.729).
The PROPDESC score showed promising performance on a separate validation cohort in predicting POD based on routine preoperative data. Suitability for universal screening needs to be shown in a large external validation.
基于术前常规数据开发并验证一种用于术后谵妄（POD）的实用风险筛查评分。
前瞻性观察性单中心试验。
在一家德国大学医院收集心脏和非心脏手术患者的术前数据及POD评估。将数据驱动建模方法（对完整和受限预测变量集进行逐步与按组件梯度提升）与专家（研究人员与外部德尔菲调查）进行的预测变量选择进行比较。
计划进行持续时间超过60分钟的择期手术的住院患者（≥60岁）。
在术后或镇静后的前五天，每天使用重症监护病房和标准护理单元的意识模糊评估方法（CAM-ICU/CAM）、4项“A”测试（4AT）和谵妄观察筛查（DOS）量表对POD进行评估。
在1023名入组患者中，978例完整观察结果被分为开发队列（n = 600；POD发生率22.2%）和验证队列（n = 378；POD发生率25.7%）。数据驱动方法生成的模型包含实验室值、手术学科以及认知和生活质量评估的几个项目，这些项目收集起来很耗时。对完整预测变量集进行梯度提升，通过选择12个预测变量产生了最高的自举预测准确性（AUC 0.767），且对心脏手术有很大依赖性。研究人员通过单变量比较选择了年龄、美国麻醉医师协会（ASA）和纽约心脏协会（NYHA）分级、手术风险以及蒙特利尔认知评估（MoCA）的“连续减法”和“句子重复”，以便快速收集术前筛查的风险评分。该研究人员模型提供的自举预测准确性略低（AUC 0.746），但在验证队列中（AUC 0.725）被证明具有稳健的结果，与手术学科无关。通过将回归系数进行缩放和四舍五入简化为PROPDESC评分的研究人员模型在验证队列中实现了相当的精度（AUC 0.729）。
PROPDESC评分在基于术前常规数据预测POD的独立验证队列中表现出良好的性能。其在通用筛查中的适用性需要在大型外部验证中得到证明。
