Choe Sooho, Park Eunjeong, Shin Wooseok, Koo Bonah, Shin Dongjin, Jung Chulwoo, Lee Hyungchul, Kim Jeongmin
School of Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Cerebro-Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
JMIR Med Inform. 2021 Sep 30;9(9):e31311. doi: 10.2196/31311.
Intraoperative hypotension has an adverse impact on postoperative outcomes. However, it is difficult to predict and treat intraoperative hypotension in advance according to individual clinical parameters.
The aim of this study was to develop a prediction model to forecast 5-minute intraoperative hypotension based on the weighted average ensemble of individual neural networks, utilizing the biosignals recorded during noncardiac surgery.
In this retrospective observational study, arterial waveforms were recorded during noncardiac operations performed between August 2016 and December 2019, at Seoul National University Hospital, Seoul, South Korea. We analyzed the arterial waveforms from the big data in the VitalDB repository of electronic health records. We defined 2s hypotension as the moving average of arterial pressure under 65 mmHg for 2 seconds, and intraoperative hypotensive events were defined when the 2s hypotension lasted for at least 60 seconds. We developed an artificial intelligence-enabled process, named short-term event prediction in the operating room (STEP-OP), for predicting short-term intraoperative hypotension.
The study was performed on 18,813 subjects undergoing noncardiac surgeries. Deep-learning algorithms (convolutional neural network [CNN] and recurrent neural network [RNN]) using raw waveforms as input showed greater area under the precision-recall curve (AUPRC) scores (0.698, 95% CI 0.690-0.705 and 0.706, 95% CI 0.698-0.715, respectively) than that of the logistic regression algorithm (0.673, 95% CI 0.665-0.682). STEP-OP performed better and had greater AUPRC values than those of the RNN and CNN algorithms (0.716, 95% CI 0.708-0.723).
We developed STEP-OP as a weighted average of deep-learning models. STEP-OP predicts intraoperative hypotension more accurately than the CNN, RNN, and logistic regression models.
ClinicalTrials.gov NCT02914444; https://clinicaltrials.gov/ct2/show/NCT02914444.
术中低血压对术后结局有不利影响。然而，根据个体临床参数提前预测和治疗术中低血压很困难。
本研究的目的是基于个体神经网络的加权平均集成，利用非心脏手术期间记录的生物信号，开发一种预测模型来预测5分钟术中低血压。
在这项回顾性观察研究中，于2016年8月至2019年12月在韩国首尔国立大学医院进行的非心脏手术期间记录动脉波形。我们分析了电子健康记录的VitalDB存储库中的大数据中的动脉波形。我们将2秒低血压定义为动脉压在65 mmHg以下持续2秒的移动平均值，当2秒低血压持续至少60秒时定义为术中低血压事件。我们开发了一种名为手术室短期事件预测（STEP-OP）的人工智能程序，用于预测短期术中低血压。
该研究对18813例接受非心脏手术的受试者进行。使用原始波形作为输入的深度学习算法（卷积神经网络[CNN]和循环神经网络[RNN]）在精确召回曲线下面积（AUPRC）得分方面（分别为0.698，95%CI 0.690-0.705和0.706，95%CI 0.698-0.715）高于逻辑回归算法（0.673，95%CI 0.665-0.682）。STEP-OP的表现优于RNN和CNN算法，且AUPRC值更高（0.716，95%CI 0.708-0.723）。
我们开发了STEP-OP作为深度学习模型的加权平均值。STEP-OP比CNN、RNN和逻辑回归模型更准确地预测术中低血压。
ClinicalTrials.gov NCT02914444；https://clinicaltrials.gov/ct2/show/NCT02914444 。
