Patients recovering from cardiovascular surgeries may develop life-threatening complications such as hemodynamic decompensation, making the monitoring of patients for such complications an essential component of postoperative care. However, this need has given rise to an inexorable increase in the number and modalities of data points collected, making it challenging to effectively analyze in real time. While many algorithms exist to assist in monitoring these patients, they often lack accuracy and specificity, leading to alarm fatigue among healthcare practitioners.

In this study we propose a multimodal approach that incorporates salient physiological signals and EHR data to predict the onset of hemodynamic decompensation. A retrospective dataset of patients recovering from cardiac surgery was created and used to train predictive models. Advanced signal processing techniques were employed to extract complex features from physiological waveforms, while a novel tensor-based dimensionality reduction method was used to reduce the size of the feature space. These methods were evaluated for predicting the onset of decompensation at varying time intervals, ranging from a half-hour to 12 h prior to a decompensation event. The best performing models achieved AUCs of 0.87 and 0.80 for the half-hour and 12-h intervals respectively. These analyses evince that a multimodal approach can be used to develop clinical decision support systems that predict adverse events several hours in advance.

从心血管手术中恢复的患者可能会出现危及生命的并发症，例如血流动力学失代偿，因此对患者进行此类并发症的监测是术后护理的重要组成部分。然而，这种需求导致收集的数据点的数量和方式不可避免地增加，使得实时有效分析变得具有挑战性。虽然存在许多算法来帮助监测这些患者，但它们通常缺乏准确性和特异性，导致医疗保健从业者的警报疲劳。

在这项研究中，我们提出了一种多模式方法，该方法结合了显着的生理信号和 EHR 数据来预测血流动力学失代偿的发生。创建了一个从心脏手术中恢复的患者的回顾性数据集，并用于训练预测模型。采用先进的信号处理技术从生理波形中提取复杂特征，同时采用一种新的基于张量的降维方法来减小特征空间的大小。评估这些方法以预测失代偿发生的时间间隔不同，范围从失代偿事件发生前的半小时到 12 小时。性能最佳的模型在半小时和 12 小时间隔内分别实现了 0.87 和 0.80 的 AUC。