Ma Guoting, Yan Wenjun, Zhao Zunqiang, Li Yanjia, Wang Lingkai
Department of Anesthesiology, Gansu Provincial Hospital, No.204 Donggang West Road, Lanzhou, 730000, China.
The First School of Clinical Medicine, Lanzhou University, No.1 Donggang West Road, Lanzhou, 730000, China.
BMC Anesthesiol. 2025 May 31;25(1):278. doi: 10.1186/s12871-025-03145-4.
There are potential associations between post-anesthesia care unit (PACU) complications that significantly impact enhanced recovery after surgery. Timely identification of these signs is essential for implementing comprehensive, systematic management strategies and delivering personalized anesthetic care. However, relevant studies are currently limited. This study aimed to develop and validate an interpretable multi-label classification model to predict PACU complications concurrently.
This prospective cohort study enrolled adult patients who underwent general anesthesia and elective surgery and were transferred to the PACU after surgery. The patients were dynamically monitored and evaluated for the occurrence of six common PACU complications: respiratory adverse events, hypothermia, hemodynamic instability, nausea/vomiting, agitation/delirium, and pain. A multi-label classification model was developed on the basis of 16 key features, and a Markov network was embedded to quantify and analyze the association network among these complications. The SHapley Additive exPlanations (SHAP) method was applied to conduct interpretability analysis of the model.
Of the 16,838 total patients, 6,830 (40.6%) experienced at least one complication. In the training cohort, 2,125 (57.0%) patients experienced two or more complications at the same time. The AUCs for the six complications in the three cohorts ranged from 0.735 to 0.914, 0.720 to 0.920, and 0.693 to 0.928, respectively. Respiratory adverse events performed best. Age, gender, BMI, duration of anesthesia, and postoperative analgesia emerged as the five most important features. The relative importance of respiratory adverse events to hemodynamic instability was the highest.
The integration of a multi-label classification model with interpretable methods has significant advantages in simultaneously predicting PACU complications, identifying the risk factors for specific complications, optimizing postoperative resource allocation, and improving patient outcomes.
麻醉后护理单元（PACU）并发症之间存在潜在关联，这些并发症会对术后加速康复产生重大影响。及时识别这些体征对于实施全面、系统的管理策略以及提供个性化麻醉护理至关重要。然而，目前相关研究有限。本研究旨在开发并验证一种可解释的多标签分类模型，以同时预测PACU并发症。
这项前瞻性队列研究纳入了接受全身麻醉和择期手术并于术后转入PACU的成年患者。对患者进行动态监测和评估，以确定六种常见的PACU并发症的发生情况：呼吸不良事件、体温过低、血流动力学不稳定、恶心/呕吐、躁动/谵妄和疼痛。基于16个关键特征开发了一个多标签分类模型，并嵌入马尔可夫网络以量化和分析这些并发症之间的关联网络。应用SHapley加性解释（SHAP）方法对模型进行可解释性分析。
在总共16,838例患者中，6,830例（40.6%）至少经历了一种并发症。在训练队列中，2,125例（57.0%）患者同时经历了两种或更多种并发症。三个队列中六种并发症的AUC分别为0.735至0.914、0.720至0.920和0.693至0.928。呼吸不良事件表现最佳。年龄、性别、BMI、麻醉持续时间和术后镇痛成为五个最重要的特征。呼吸不良事件对血流动力学不稳定的相对重要性最高。
将多标签分类模型与可解释方法相结合在同时预测PACU并发症、识别特定并发症的危险因素、优化术后资源分配以及改善患者结局方面具有显著优势。
