Faculty of Data and Decision Science, Technion, Haifa Israel.
Faculty of Medicine, Technion, Haifa Israel.
PLoS Comput Biol. 2023 Sep 5;19(9):e1010835. doi: 10.1371/journal.pcbi.1010835. eCollection 2023 Sep.
Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone. Then, they use this mental model to derive the causes for instability and choose appropriate interventions. Not only this is a very hard problem due to the nature of the signals, but it also requires expertise and a clinician's ongoing presence at the bedside. Clinical decision support tools based on mechanistic dynamical models offer an appealing solution due to their inherent explainability, corollaries to the clinical mental process, and predictive power. With a translational motivation in mind, we developed iCVS: a simple, with high explanatory power, dynamical mechanistic model to infer hidden cardiovascular states. Full model estimation requires no prior assumptions on physiological parameters except age and weight, and the only inputs are arterial and venous pressure waveforms. iCVS also considers autonomic and non-autonomic modulations. To gain more information without increasing model complexity, both slow and fast timescales of the blood pressure traces are exploited, while the main inference and dynamic evolution are at the longer, clinically relevant, timescale of minutes. iCVS is designed to allow bedside deployment at pediatric and adult intensive care units and for retrospective investigation of cardiovascular mechanisms underlying instability. In this paper, we describe iCVS and inference system in detail, and using a dataset of critically-ill children, we provide initial indications to its ability to identify bleeding, distributive states, and cardiac dysfunction, in isolation and in combination.
重症监护医学复杂且资源密集。一个关键且常见的挑战在于从部分观测数据推断患者的潜在生理状态。具体来说，对于心血管系统，临床医生使用心率、动脉和静脉血压以及体格检查和辅助检查的结果等可观察变量来构建心理模型并估计隐藏变量，如心输出量、血管阻力、充盈压和容量以及自主神经张力。然后，他们使用这种心理模型来确定不稳定的原因并选择适当的干预措施。由于信号的性质，这不仅是一个非常困难的问题，而且还需要专业知识和临床医生在床边的持续存在。基于机械动力学模型的临床决策支持工具提供了一个有吸引力的解决方案，因为它们具有内在的可解释性、与临床心理过程的推论以及预测能力。考虑到转化的动机，我们开发了 iCVS：一种简单的、具有高解释力的动力学机械模型，用于推断隐藏的心血管状态。完整模型估计除了年龄和体重之外不需要对生理参数做出任何先验假设，并且仅输入动脉和静脉压力波形。iCVS 还考虑了自主和非自主调制。为了在不增加模型复杂性的情况下获得更多信息，利用了血压迹线的慢和快时间尺度，而主要推断和动态演化发生在更长的、临床相关的分钟时间尺度上。iCVS 的设计目的是允许在儿科和成人重症监护病房床边部署，并用于回顾性研究不稳定的心血管机制。在本文中，我们详细描述了 iCVS 和推理系统，并使用一组危重病儿童的数据集，提供了其单独和组合识别出血、分布状态和心功能障碍的能力的初步迹象。
