Angelotti G, Morandini P, Lehman L H, Mark R G, Barbieri R
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2784-2787. doi: 10.1109/EMBC.2018.8512859.
A life threatening condition in Intensive Care Unit (ICU) is the Acute Hypotensive Episode (AHE). Patients experiencing an AHE may suffer from irreversible organ damage associated with increased mortality. Predicting the onset of AHE could be of pivotal importance to establish appropriate and timely interventions. We propose a method that, using waveforms widely acquired in ICU, like Arterial Blood Pressure (ABP) and Electrocardiogram (ECG), will extract features relative to the cardiac system to predict whether or not a patient will experience a hypotensive episode. Specifically, we want to assess if there are hidden patterns in the dynamics of baroreflex able to improve the prediction of AHEs. We will investigate the predictive power of features related to the baroreflex by performing classifications with and without them. Results are obtained using 17 classifiers belonging to different model families: classification trees, Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs) replicated with different set of hyper-parameters and logistic regression. On average, the use of baroreflex features in the AHE prediction process increases the Area Under the Curve (AUC) by 10%.
重症监护病房（ICU）中的一种危及生命的状况是急性低血压发作（AHE）。经历AHE的患者可能会遭受与死亡率增加相关的不可逆器官损伤。预测AHE的发作对于建立适当且及时的干预措施可能至关重要。我们提出了一种方法，该方法利用在ICU中广泛采集的波形，如动脉血压（ABP）和心电图（ECG），提取与心脏系统相关的特征，以预测患者是否会经历低血压发作。具体而言，我们想评估压力感受器反射动态中是否存在能够改善AHE预测的隐藏模式。我们将通过在有和没有这些特征的情况下进行分类，来研究与压力感受器反射相关的特征的预测能力。使用属于不同模型家族的17个分类器获得结果：分类树、支持向量机（SVM）、使用不同超参数集复制的K近邻（KNN）以及逻辑回归。平均而言，在AHE预测过程中使用压力感受器反射特征会使曲线下面积（AUC）增加10%。
