Issue 5, 2020

Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

Abstract

This work describes a fully-integrated portable microfluidic analysis system for real-time monitoring of dynamic changes in glucose and lactate occurring in the brain as a result of cardiac arrest and resuscitation. Brain metabolites are sampled using FDA-approved microdialysis probes and coupled to a high-temporal resolution 3D printed microfluidic chip housing glucose and lactate biosensors. The microfluidic biosensors are integrated with a wireless 2-channel potentiostat forming a compact analysis system that is ideal for use in a crowded operating theatre. Data are transmitted to a custom-written app running on a tablet for real-time visualisation of metabolic trends. In a proof-of-concept porcine model of cardiac arrest, the integrated analysis system proved reliable in a challenging environment resembling a clinical setting; noise levels were found to be comparable with those seen in the lab and were not affected by major clinical interventions such as defibrillation of the heart. Using this system, we were able, for the first time, to measure changes in brain glucose and lactate levels caused by cardiac arrest and resuscitation; the system was sensitive to clinical interventions such as infusion of adrenaline. Trends suggest that cardiopulmonary resuscitation alone does not meet the high energy demands of the brain as metabolite levels only return to their values preceding cardiac arrest upon return of spontaneous circulation.

Graphical abstract: Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

Supplementary files

Article information

Article type
Paper
Submitted
30 Sept. 2019
Accepted
28 Nov. 2019
First published
24 Janv. 2020

Analyst, 2020,145, 1894-1902

Real-time neurochemical measurement of dynamic metabolic events during cardiac arrest and resuscitation in a porcine model

S. A. N. Gowers, I. C. Samper, D. R. K. Murray, G. K. Smith, S. Jeyaprakash, M. L. Rogers, M. Karlsson, M. H. Olsen, K. Møller and M. G. Boutelle, Analyst, 2020, 145, 1894 DOI: 10.1039/C9AN01950B

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