Closed-loop machine-controlled CPR system optimises haemodynamics during prolonged CPR

Pierre S Sebastian; Marinos N Kosmopoulos; Manan Gandhi; Alex Oshin; Matthew D Olson; Adrian Ripeckyj; Logan Bahmer; Jason A Bartos; Evangelos A Theodorou; Demetris Yannopoulos

doi:10.1016/j.resplu.2020.100021

Closed-loop machine-controlled CPR system optimises haemodynamics during prolonged CPR

Resusc Plus. 2020 Aug 12:3:100021. doi: 10.1016/j.resplu.2020.100021. eCollection 2020 Sep.

Affiliations

¹ Center for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
² Georgia Institute of Technology, School of Aerospace Engineering, Institute of Robotics and Intelligent Machines, Center for Machine Learning, United States.

Abstract

Objectives: We evaluated the feasibility of optimising coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) with a closed-loop, machine-controlled CPR system (MC-CPR) that sends real-time haemodynamic feedback to a set of machine learning and control algorithms which determine compression/decompression characteristics over time.

Background: American Heart Association CPR guidelines (AHA-CPR) and standard mechanical devices employ a "one-size-fits-all" approach to CPR that fails to adjust compressions over time or individualise therapy, thus leading to deterioration of CPR effectiveness as duration exceeds 15-20 min.

Methods: CPR was administered for 30 min in a validated porcine model of cardiac arrest. Intubated anaesthetised pigs were randomly assigned to receive MC-CPR (6), mechanical CPR conducted according to AHA-CPR (6), or human-controlled CPR (HC-CPR) (10). MC-CPR directly controlled the CPR piston's amplitude of compression and decompression to maximise CPP over time. In HC-CPR a physician controlled the piston amplitudes to maximise CPP without any algorithmic feedback, while AHA-CPR had one compression depth without adaptation.

Results: MC-CPR significantly improved CPP throughout the 30-min resuscitation period compared to both AHA-CPR and HC-CPR. CPP and carotid blood flow (CBF) remained stable or improved with MC-CPR but deteriorated with AHA-CPR. HC-CPR showed initial but transient improvement that dissipated over time.

Conclusion: Machine learning implemented in a closed-loop system successfully controlled CPR for 30 min in our preclinical model. MC-CPR significantly improved CPP and CBF compared to AHA-CPR and ameliorated the temporal haemodynamic deterioration that occurs with standard approaches.

Keywords: CPR; Cardiopulmonary resuscitation; Haemodynamics; Machine learning; Mechanical CPR; OHCA; Personalized medicine; Porcine; Refractory VF.