老年患者术后谵妄的预测

Shun-Chin Jim Wu, Nitin Sharma, Anne Bauch, Hao-Chun Yang, Jasmine L. Hect, Christine Thomas, Soeren Wagner, Bernd R. Foerstner, Christine A.F. von Arnim, Tobias Kaufmann, Gerhard W. Eschweiler, Thomas Wolfers
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摘要

背景:在全球范围内,老年人选择性手术的数量呈上升趋势。机器学习可改善风险评估,对手术规划和术后护理产生影响。术前认知评估有助于早期识别术后谵妄(POD)。本研究旨在利用术前和/或围手术期特征评估机器学习模型对 POD 的预测能力,特别关注术前神经心理学评估:这项回顾性队列研究分析了多中心 PAWEL 研究及其 PAWEL-R 子研究的数据,涵盖了 2017 年 7 月至 2019 年 4 月期间在五个医疗中心接受择期手术的老年患者(≥70 岁)。共纳入1624名患者,出院前进行了POD诊断。数据包括人口统计学、临床、手术和神经心理学特征,收集时间为术前和围手术期。使用接受者操作特征曲线下面积(AUC)评估机器学习模型的性能,并使用置换检验进行显著性检验和SHapley Additive exPlanations(SHAP)进行有效的神经心理学评估识别:在这组1624名患者中,52.3%(N=850)为男性,平均[SD]年龄为77.9[4.9]岁。利用人口统计学、临床、手术和神经心理学特征预测术前 POD 的 AUC 为 0.79。将所有术前和围手术期特征纳入模型后,AUC 略高于 0.82,但无显著差异(P= 0.19)。值得注意的是,认知因素本身并不是强有力的预测因素(AUC=0.61)。然而,根据SHAP分析,神经心理评估中的特定测试,如蒙特利尔认知评估记忆子域和寻迹测试B部分,对预测至关重要:术前 POD 风险预测可提高术前评估的风险意识,改善谵妄高危患者的术后管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Postoperative Delirium in Older Patients
Background: The number of elective surgeries for older individuals is on the rise globally. Machine learning may improve risk assessment with impact on surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early identification of postoperative delirium (POD). This study aim to estimate the predictive ability of machine learning models for POD using pre- and/or perioperative features, with a specific focus on adding neuropsychological assessments prior to surgery. Materials and Methods: This retrospective cohort study analyzed data from the multicenter PAWEL study and its PAWEL-R substudy, encompassing older patients (≥70 years) undergoing elective surgeries across five medical centers from July 2017 to April 2019. A total of 1624 patients were included, with POD diagnosis made before discharge. Data included demographics, clinical, surgical, and neuropsychological features collected pre- and perioperatively. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations (SHAP) for effective neuropsychological assessments identification. Results: In this cohort of 1624 patients, 52.3% (N=850) were male, with a mean [SD] age of 77.9 [4.9] years. Predicting POD before surgery using demographic, clinical, surgical, and neuropsychological features achieved an AUC of 0.79. Incorporating all pre- and perioperative features into the model yielded a slightly higher AUC of 0.82, with no significant difference observed (P= .19). Notably, cognitive factors alone were not strong predictors (AUC=0.61). However, specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B, were found to be crucial for prediction according to SHAP analysis. Conclusion and Relevance: Preoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve postoperative management in patients with a high risk for delirium.
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