Xie Guang-Hong, Shen Jun, Li Fan, Yan Huan-Huan, Qian Ying
Department of Operating Room, The First People's Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People's Republic of China.
Department of Breast Surgery, The First People's Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People's Republic of China.
J Multidiscip Healthc. 2024 May 22;17:2535-2550. doi: 10.2147/JMDH.S458784. eCollection 2024.
We aimed to analyze the factors related to delay in transfer of patients in the post-anesthesia care unit (PACU) and to develop and validate a prediction model for understanding these factors to guide precise clinical intervention.
We collected data from two cohorts of 1153 and 297 patients who underwent surgery and were treated in the PACU at two time points. We examined their clinical features and anesthesia care data using analytical methods such as logistic regression, Random Forest, and eXtreme Gradient Boosting (Xgboost) to screen out variables and establish a prediction model. We then validated and simplified the model and plotted a nomogram. Using LASSO regression, we reduced the dimensionality of the data. We developed multiple models and plotted receiver operating characteristic (ROC) and calibration curves. We then constructed a simplified model by pooling the identified variables, which included hemoglobin (HB), alanine transaminase (ALT), glucose levels, duration of anesthesia, and the minimum bispectral index value (BIS_min).
The model had good prediction performance parameters in the training and validation sets, with an AUC of 0.909 (0.887-0.932) in the training set and 0.939 (0.919-0.959) in the validation set. When we compared model 6 with other models, the net reclassification index (NRI) and the integrated discriminant improvement (IDI) index indicated that it did not differ significantly from the other models. We developed a scoring system, and it showed good prediction performance when verified with the training and validation sets as well as external data. Additionally, both the decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the potential clinical efficacy of the model in guiding patient interventions.
Predicting transfer delays in the post-anesthesia care unit using predictive models is feasible; however, this merits further exploration.
我们旨在分析麻醉后护理单元（PACU）患者转运延迟的相关因素，并开发和验证一个预测模型，以了解这些因素，从而指导精准的临床干预。
我们收集了两个队列的数据，分别为1153例和297例在两个时间点接受手术并在PACU接受治疗的患者。我们使用逻辑回归、随机森林和极端梯度提升（Xgboost）等分析方法检查他们的临床特征和麻醉护理数据，以筛选出变量并建立预测模型。然后我们对模型进行验证和简化，并绘制列线图。使用套索回归，我们对数据进行降维。我们开发了多个模型，并绘制了受试者工作特征（ROC）曲线和校准曲线。然后，我们通过汇总识别出的变量构建了一个简化模型，这些变量包括血红蛋白（HB）、丙氨酸转氨酶（ALT）、血糖水平、麻醉持续时间和最小双谱指数值（BIS_min）。
该模型在训练集和验证集中具有良好的预测性能参数，训练集的AUC为0.909（0.887 - 0.932），验证集的AUC为0.939（0.919 - 0.959）。当我们将模型6与其他模型进行比较时，净重新分类指数（NRI）和综合判别改善（IDI）指数表明它与其他模型没有显著差异。我们开发了一个评分系统，在用训练集、验证集以及外部数据进行验证时，它显示出良好的预测性能。此外，决策曲线分析（DCA）和临床影响曲线（CIC）均证明了该模型在指导患者干预方面的潜在临床疗效。
使用预测模型预测麻醉后护理单元的转运延迟是可行的；然而，这值得进一步探索。
