Atrial Fibrillation Prediction from Critically Ill Sepsis Patients.
Atrial Fibrillation Prediction from Critically Ill Sepsis Patients.
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危重症脓毒症患者心房颤动预测
DOI：
10.3390/bios11080269
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发表时间：
2021-08-09
期刊：
影响因子：
--
通讯作者：
Chon KH
中科院分区：
文献类型：
--
作者：
Bashar SK;Ding EY;Walkey AJ;McManus DD;Chon KH
脓毒症是由感染期间危及生命的器官功能障碍定义的，是医院死亡的主要原因。在脓毒症期间，可能发生新发房颤（AF）的风险很高，这与显著的发病率和死亡率相关。因此，脓毒症期间AF的早期预测将允许在重症监护室（ICU）中测试干预措施以预防AF及其严重并发症。在本文中，我们提出了一种新的自动AF预测算法，重症脓毒症患者使用心电图（ECG）信号。从5分钟ECG采集的心率信号，使用传统的时间，频率和非线性域的方法进行特征提取。此外，变频复解调和可调Q因子小波变换的时频方法被应用到提取新的特征从心率信号。使用选定的特征子集，几个机器学习分类器，包括支持向量机（SVM）和随机森林（RF），仅使用2001年心脏病学计算机数据集进行训练。为了测试所提出的方法，本研究中使用了来自重症监护医学信息市场（MIMIC）III数据库的50名重症ICU受试者。使用来自MIMIC III的不同和独立的测试数据，SVM在AF发作前预测AF的灵敏度为80%，特异性为100%，准确性为90%，阳性预测值为100%，阴性预测值为83.33%，而RF的AF预测准确率为88%。当我们分析可以提前多少时间预测重症脓毒症患者的AF事件时，该算法提前10分钟预测AF事件的准确率达到了80%。我们的算法优于预测ICU患者AF的最先进方法，进一步证明了我们提出的方法的有效性。患者AF转换信息的注释将向其他研究者公开。我们预测AF发作的算法适用于任何ECG模态，包括贴片电极和可穿戴设备，包括霍尔特、循环记录仪和植入式器械。
Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time–frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients’ AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.
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