Evidence map›Paper›PMID 38260374›Full record

ArticleResearch square2024

Predicting Acute Brain Injury in Venoarterial Extracorporeal Membrane Oxygenation Patients with Tree-Based Machine Learning: Analysis of the Extracorporeal Life Support Organization Registry.

Andrew Kalra, Preetham Bachina, Benjamin L Shou, Jaeho Hwang, Meylakh Barshay, Shreyas Kulkarni, Isaac Sears, Carsten Eickhoff, Christian A Bermudez, Daniel Brodie and 5 more

Abstract readPreprint
In one paragraph

Article in Research square, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Andrew KalraJohns Hopkins University School of Medicine.ORCID 0000-0001-8338-019X
Preetham BachinaJohns Hopkins University School of Medicine.
Benjamin L ShouJohns Hopkins University School of Medicine.ORCID 0000-0003-2825-3301
Jaeho HwangJohns Hopkins University School of Medicine.ORCID 0000-0002-1803-2524
Meylakh BarshayWarren Alpert Medical School of Brown University.ORCID 0000-0001-5611-024X
Shreyas KulkarniWarren Alpert Medical School of Brown University.ORCID 0000-0002-6723-515X
Isaac SearsWarren Alpert Medical School of Brown University.ORCID 0000-0002-3293-4524
Carsten EickhoffUniversity of Tübingen.ORCID 0000-0001-9895-4061
Christian A BermudezPerelman School of Medicine at the University of Pennsylvania, Philadelphia.
Daniel BrodieJohns Hopkins University School of Medicine.ORCID 0000-0002-0813-3145
Corey E VentetuoloWarren Alpert Medical School of Brown University.ORCID 0000-0002-4223-4775
Bo Soo KimJohns Hopkins University School of Medicine.
Glenn J R WhitmanJohns Hopkins University School of Medicine.ORCID 0000-0003-3225-2360
Adeel AbbasiWarren Alpert Medical School of Brown University.
Sung-Min ChoJohns Hopkins University School of Medicine.ORCID 0000-0002-5132-0958

Funding

Short-Term Training Program to Increase Diversity in Health-Related ResearchR25HL088992 · NHLBI · BROWN UNIVERSITY · PI ABID, RUHUL, DIAZ, JOSEPH A · 2007 to 2024
$1.9M
Warren Alpert Medical School Basic Translational ResearchT35HL094308 · NHLBI · BROWN UNIVERSITY · PI Thomas Benedict Bartnikas, Brittany Star Hampton · 2009 to 2026
$808k
CLots and Oxygen in Va-ExtracorpoReal membrane oxygenation (CLOVER) studyK23HL157610 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI Sung-Min Cho · 2022 to 2026
$797k
NHLBI NIH HHS K23 HL157610NHLBI NIH HHS L30 HL165486NHLBI NIH HHS R25 HL088992NHLBI NIH HHS T35 HL094308NINDS NIH HHS L30 NS119233
6 · The paper itself

Abstract

Objective: To determine if machine learning (ML) can predict acute brain injury (ABI) and identify modifiable risk factors for ABI in venoarterial extracorporeal membrane oxygenation (VA-ECMO) patients. Design: Retrospective cohort study of the Extracorporeal Life Support Organization (ELSO) Registry (2009-2021). Setting: International, multicenter registry study of 676 ECMO centers. Patients: Adults (≥18 years) supported with VA-ECMO or extracorporeal cardiopulmonary resuscitation (ECPR). Interventions: None. Measurements and Main Results: Our primary outcome was ABI: central nervous system (CNS) ischemia, intracranial hemorrhage (ICH), brain death, and seizures. We utilized Random Forest, CatBoost, LightGBM and XGBoost ML algorithms (10-fold leave-one-out cross-validation) to predict and identify features most important for ABI. We extracted 65 total features: demographics, pre-ECMO/on-ECMO laboratory values, and pre-ECMO/on-ECMO settings.Of 35,855 VA-ECMO (non-ECPR) patients (median age=57.8 years, 66% male), 7.7% (n=2,769) experienced ABI. In VA-ECMO (non-ECPR), the area under the receiver-operator characteristics curves (AUC-ROC) to predict ABI, CNS ischemia, and ICH was 0.67, 0.67, and 0.62, respectively. The true positive, true negative, false positive, false negative, positive, and negative predictive values were 33%, 88%, 12%, 67%, 18%, and 94%, respectively for ABI. Longer ECMO duration, higher 24h ECMO pump flow, and higher on-ECMO PaO Conclusions: This is the largest study predicting neurological complications on sufficiently powered international ECMO cohorts. Longer ECMO duration and higher 24h pump flow were associated with ABI in both non-ECPR and ECPR VA-ECMO.

Indexed as

acute brain injuryExtracorporeal Life Support Organizationextracorporeal membrane oxygenationmachine learningneurological complications

Identifiers

PMID38260374
PMCPMC10802703

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.