Anesthesiology Department, Catharina Hospital Eindhoven, Eindhoven, The Netherlands.
Signal Processing Department, Eindhoven University of Technology, Eindhoven, The Netherlands.
PLoS One. 2023 Aug 3;18(8):e0286818. doi: 10.1371/journal.pone.0286818. eCollection 2023.
Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions.
A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission.
An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88).
The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.
目前，在麻醉后护理单元（PACU）中没有基于证据的决策标准。这对于将术后患者分配到适当的护理水平非常有价值，并且有益于患者的预后，例如意外的重症监护病房（ICU）入院。目的是评估是否将围手术期因素纳入预测术后患者恶化和意外 ICU 入院的因素可以提高预测能力。
这是一项回顾性观察队列研究，于 2013 年 1 月至 2017 年 12 月在荷兰的一家三级医院进行。选择在此期间接受手术的所有患者。排除心胸外科手术、产科手术、导管室手术、电惊厥治疗、日间手术、静脉置管干预和 18 岁以下患者。主要结局是意外 ICU 入院。
223 名（0.9%）患者出现意外 ICU 入院，病情复杂。这些患者的医院死亡率更高（13.9%比 0.2%，p<0.001）。多变量分析得出的意外 ICU 入院预测因素包括年龄、体重指数、全身麻醉联合硬膜外麻醉、术前评分、糖尿病、血管加压药、红细胞、手术持续时间和麻醉后护理单元停留时间以及心率和氧饱和度等生命体征。该模型的受试者工作特征曲线得出的曲线下面积为 0.86（95%CI 0.83-0.88）。
当将围手术期内和早期的因素与术前患者因素相结合时，从电子病历数据预测意外 ICU 入院的准确性得到了提高。这强调了在麻醉后护理单元中需要使用临床决策支持工具来进行术后患者的分配。
