Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, AL, 35294, United States of America.
Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America.
J Med Syst. 2024 Jul 23;48(1):69. doi: 10.1007/s10916-024-02085-9.
Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors.
We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk.
Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature.
We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients.
尽管在美国和发达国家，择期手术的死亡率较低，但仍有一些患者在离开麻醉后护理单元（PACU）后出现意外的医疗升级（UCE）。研究表明，患者存在 UCE 的风险因素，但尚不清楚哪些因素最重要。机器学习（ML）可以预测临床事件。我们假设 ML 可以预测 PACU 出院后的手术患者的 UCE，并确定具体的风险因素。
我们对所有接受非心脏手术（择期和紧急）的患者进行了单中心回顾性分析。我们从术前访视、术中记录、PACU 入院和 UCE 发生率中收集数据。我们使用这些数据训练了一个 ML 模型，并在一个独立的数据集中测试该模型，以确定其功效。最后，我们评估了最有可能预测 UCE 风险的个体患者和临床因素。
我们的研究表明，ML 可以预测 UCE 风险，在训练组和测试组中，UCE 风险约为 5%。我们能够确定患者的风险因素，如患者生命体征、紧急手术、ASA 状态和非手术麻醉时间，这些因素是重要的变量。我们为每位患者绘制了重要变量的 Shapley 值，以帮助确定这些变量中哪些对 UCE 风险的影响最大。值得注意的是，ML 识别的 UCE 风险因素与麻醉师的临床实践和当前文献一致。
我们使用 ML 分析了来自单中心回顾性非心脏手术患者队列的数据，其中一些患者发生了 UCE。ML 为患者发生 UCE 分配了风险预测，并确定了与风险增加相关的围手术期因素。我们提倡使用 ML 来增强麻醉师的临床决策，帮助决定从 PACU 适当的处置，并确保我们的患者得到尽可能安全的护理。
