Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis
Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis
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DOI：
10.1097/aln.0000000000002300
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发表时间：
2018-10-01
期刊：
影响因子：
8.8
通讯作者：
Cannesson, Maxime
中科院分区：
文献类型：
--
作者：
Hatib, Feras;Jian, Zhongping;Cannesson, Maxime
背景：通过适当的算法，计算机可以学会在大数据集中检测模式和关联。作者的目标是将机器学习应用于动脉压力波形，并创建一种预测低血压的算法。该算法检测波形的早期变化，这些变化预示着影响前负荷、后负荷和收缩性的心血管代偿机制的减弱。方法：该算法采用两种不同的数据来源：(1)用于训练的回顾性队列，包括1334例患者的记录，545,959分钟的动脉波形记录和25,461次低血压；(2)一项用于外部验证的前瞻性当地医院队列研究，包括204例患者的记录，33,236分钟动脉波形记录和1,923次低血压发作。该算法将从高保真动脉压波形中计算出的大量特征与即将到来的低血压事件（平均动脉压< 65 mmHg）的预测联系起来。接受者工作特征曲线分析评估了该算法在预测低血压（定义为平均动脉压低于65 mmHg）方面的成功。结果：利用每个心动周期3,022个个体特征，该算法在低血压事件发生前15分钟预测动脉低血压的灵敏度和特异性分别为88%（85 ~ 90%）和87%(85 ~ 90%)（曲线下面积，0.95 [0.94 ~ 0.95]）；10 min前89%（87 ~ 91%）和90%(87 ~ 92%)（曲线下面积0.95 [0.95 ~ 0.96]）；92% (90 ~ 94%), 92% (90 ~ 94%)， 5min前（曲线下面积，0.97[0.97 ~ 0.98]）。结论：研究结果表明，机器学习算法可以通过大量高保真动脉波形数据集进行训练，以预测手术患者的低血压记录。
Background: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility.Methods: The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients' records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients' records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm's success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg.Results: Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]).Conclusions: The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients' records.