Evidence map›Paper›PMID 42001174›Full record

ArticleCritical care (London, England)2026

ECMO PAL VV: using deep neural networks for survival prognostication in venovenous extracorporeal membrane oxygenation.

Andrew F Stephens, Michael Šeman, Riley Hackwill, Arne Diehl, David Pilcher, Ryan P Barbaro, Daniel Brodie, Vincent Pellegrino, David M Kaye, Shaun D Gregory and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

12 authors.

Andrew F Stephens *Advanced Cardiorespiratory Engineering Laboratory, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia. research.andrew.stephens@gmail.com.ORCID http://orcid.org/0000-0002-2271-750X
Michael Šeman *Advanced Cardiorespiratory Engineering Laboratory, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia.
Riley HackwillAdvanced Cardiorespiratory Engineering Laboratory, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia.
Arne DiehlSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
David PilcherSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Ryan P BarbaroPediatric Critical Care Medicine, Susan B. Meister Child Health Evaluation and Research Center, University of Michigan, Ann Arbor, MI, USA.
Daniel BrodieDivision of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Vincent PellegrinoDepartment of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Australia.
David M KayeDepartment of Cardiology, The Alfred Hospital, Melbourne, Australia.
Shaun D Gregory *Advanced Cardiorespiratory Engineering Laboratory, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia.
Carol L Hodgson *School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Extracorporeal Life Support Organization Member Centres

Funding

Common Good RF2024-O-03Medical Research Future Fund MRFFRDII000056National Health and Medical Research Council 2016995National Health and Medical Research Council GNT2033103National Heart Foundation of Australia 106675
6 · The paper itself

Abstract

backgroundPrognostication for venovenous extracorporeal membrane oxygenation (ECMO) outcomes is crucial for risk-adjusting centre performance. This study aimed to leverage a large, multicentre, international database to develop and evaluate AI-driven models for predicting survival to hospital discharge of adult patients receiving venovenous ECMO. The model was called ECMO PAL VV (ECMO – Predictive Algorithm for VV).

methodsTraining and temporal validation data were sourced from the Extracorporeal Life Support Organization Registry (ELSO), 39,501 patients across 660 hospitals. Deep neural networks were trained on all adult patients receiving VV ECMO between 2017 and 2023 (N = 35,182) to predict survival to hospital discharge. Temporal validation was performed on registry data cases from 2024 (N = 4,318). Model predictions were compared against published venovenous ECMO outcomes scores using the validation cohort.

resultsInternal training yielded an accuracy of 79% and an area under the receiver operating characteristic curve (AUC) of 0.87. Temporal validation revealed a drop in accuracy to 73% with an AUC of 0.78, primarily due to a reduction in sensitivity to mortality prediction (71% to 57%). ECMO PAL VV outperformed published venovenous ECMO scores, which had accuracies of 65% (RESP) and 60% (Lazzeri score) for predictions on the validation data.

conclusionsECMO PAL VV demonstrated strong accuracy on contemporary international registry data (73%) with strong sensitivity (81%) and precision (77%) to predict survival to hospital discharge, outperforming existing published scores. ECMO PAL VV has the potential to improve risk adjustment and enable data-driven healthcare.

Indexed as

Extracorporeal Membrane OxygenationNeural Networks, ComputerArea Under CurveFemaleHumansPrediction AlgorithmsPredictive Learning ModelsPrognosisRegistriesROC CurveARDSArtificial intelligenceECMOPrognosticationRisk adjustmentSurvival score

Identifiers

PMID42001174
PMCPMC13224564

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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.