Predicting Cardiorespiratory Instability

Abstract

Identification of patients with overt cardiorespiratory insufficiency or at high risk of impending cardiorespiratory insufficiency is often difficult outside the venue of directly observed patients in highly staffed areas of the hospital, such as the operating room, intensive care unit (ICU) or emergency department. And even in these care locations, identification of cardiorespiratory insufficiency early or predicting its development beforehand is often challenging. The clinical literature has historically prized early recognition of cardiorespiratory insufficiency and its prompt correction as being valuable at minimizing patient morbidity and mortality while simultaneously reducing healthcare costs. Recent data support the statement that integrated monitoring systems that create derived fused parameters of stability or instability using machine learning algorithms, accurately identify cardiorespiratory insufficiency and can predict their occurrence. In this overview, we describe integrated monitoring systems based on established machine learning analysis using various established tools, including artificial neural networks, k-nearest neighbor, support vector machine, random forest classifier and others on routinely acquired non-invasive and invasive hemodynamic measures to identify cardiorespiratory insufficiency and display them in real-time with a high degree of precision.

The implications of these approaches for all healthcare monitoring across the spectrum of in-patient to chronic care is clear, even though the need may appear more pressing for the acute care setting to those of us whose daily life is centered there. The underlying assumption of these approaches is that measured changes in easily acquired physiologic variables reflect complex patient-specific interactions amongst multiple regulatory autonomic, hormonal and metabolic systems. Accordingly, simple algorithm approaches to such interactions, like the use of the existing severity scoring systems or computer-based treatment protocols, are unlikely to improve outcomes other than by standardizing therapies. Potentially, by using functional hemodynamicmonitoring principles, previously described and validated, we can predict with a high degree of accuracy volume responsiveness and central arterial tone in all patients. But one needs to identify who is unstable or going to be unstable before applying these functional hemodynamic monitoring approaches.