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

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number100021
JournalResuscitation Plus
Volume3
DOIs
StatePublished - Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 The Authors

Keywords

  • CPR
  • Cardiopulmonary resuscitation
  • Haemodynamics
  • Machine learning
  • Mechanical CPR
  • OHCA
  • Personalized medicine
  • Porcine
  • Refractory VF

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