Method and system for assessing fluid responsiveness using multimodal data

Abstract

A system (100) for assessing fluid responsiveness includes an infusion pump (24) in communication with at least one processor (32), and a plurality of physiological monitors (40,42,44,46) operable to receive physiological signals from an associated patient. Physiological signals (48,50) acquired from the associated patient (10) during a fluid challenge are synchronized with a timing signal (54) of the infusion pump (24) administering the fluid challenge. One or more dynamic indices and / or features (58) is calculated from the synchronized physiological signals (50), and one or more dynamic indices and / or features (50) is calculated from baseline physiological signals (48) acquired from the associated patient (10) prior to the fluid challenge. A fluid responsiveness probability value (64) of the patient (10) is determined based on dynamic indices and / or features (58) from the synchronized physiological signals (50) and dynamic indices and / or features (50) from the baseline physiological signals (48).

Description

technical field [0001] This application relates generally to the field of medicine. It is specifically applied in conjunction with medical monitoring, patient treatment, etc., in hospitals, urgent care centers, emergency and / or trauma treatment, fluid resuscitation, etc. However, it should be understood that the present application is also applicable to other usage scenarios and is not necessarily limited to the above-mentioned applications. Background technique [0002] Fluid resuscitation is the first step in the management of a critically ill patient who has been deemed hemodynamically unstable. Unfortunately, only 50% of critically ill patients in the operating room (OR), intensive care unit (ICU) or emergency department (ED) respond positively to fluid resuscitation. Furthermore, patients who do not respond aggressively may be harmed by the treatment itself, and thus develop other clinical problems such as pulmonary edema or tissue edema. A predictive algorithm for f...

AI Technical Summary

Problems solved by technology

Unfortunately, these dynamic indicators are inaccurate in predicting fluid responsiveness in spontaneously breathing patients, mainly because ABP, ECG, or PPG do not vary very dramatically with respiratory cycles (ie, very low signal-to-noise ratios)

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