Evidence map›Paper›PMID 34223304›Full record

ArticleResuscitation plus2020

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

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Resuscitation plus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Pierre S SebastianCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Marinos N KosmopoulosCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Manan GandhiGeorgia Institute of Technology, School of Aerospace Engineering, Institute of Robotics and Intelligent Machines, Center for Machine Learning, United States.
Alex OshinGeorgia Institute of Technology, School of Aerospace Engineering, Institute of Robotics and Intelligent Machines, Center for Machine Learning, United States.
Matthew D OlsonCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Adrian RipeckyjCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Logan BahmerCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Jason A BartosCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
Evangelos A TheodorouGeorgia Institute of Technology, School of Aerospace Engineering, Institute of Robotics and Intelligent Machines, Center for Machine Learning, United States.
Demetris YannopoulosCenter for Resuscitation Medicine, University of Minnesota Medical School, Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States.
University of Minnesota Medical Center · USGeorgia Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe 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.

backgroundAmerican 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.

methodsCPR 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.

resultsMC-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.

conclusionMachine 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.

Indexed as

Cardiopulmonary resuscitationCPRHaemodynamicsMachine learningMechanical CPROHCAPersonalized medicinePorcineRefractory VF

Identifiers

PMID34223304
PMCPMC8244522
OpenAlexW3048753436

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.