Evidence map›Paper›PMID 35868590›Full record

ArticleResuscitation2022

Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019.

Anoop Mayampurath, Fereshteh Bashiri, Raffi Hagopian, Laura Venable, Kyle Carey, Dana Edelson, Matthew Churpek, American Heart Association's Get With The Guidelines®-Resuscitation Investigators

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Resuscitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
  4. Review
  5. Review
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

8 authors.

Anoop MayampurathDepartment of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI, United States; Department of Medicine, University of Wisconsin, Madison, WI, United States.
Fereshteh BashiriDepartment of Medicine, University of Wisconsin, Madison, WI, United States.
Raffi HagopianDepartment of Medicine, Weill Cornell Medicine, New York, NY, United States.
Laura VenableDepartment of Medicine, University of Chicago, Chicago, IL, United States.
Kyle CareyDepartment of Medicine, University of Chicago, Chicago, IL, United States.
Dana EdelsonDepartment of Medicine, University of Chicago, Chicago, IL, United States.
Matthew ChurpekDepartment of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI, United States; Department of Medicine, University of Wisconsin, Madison, WI, United States. Electronic address: mchurpek@medicine.wisc.edu.
American Heart Association's Get With The Guidelines®-Resuscitation Investigators

Funding

Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney InjuryR01DK126933 · NIDDK · UNIVERSITY OF CHICAGO · PI CHURPEK, MATTHEW MICHAEL, KOYNER, JAY L · 2021 to 2025
$3.4M
Using causal machine learning for personalized treatment recommendations in critically ill patientsR01HL157262 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI Matthew Michael Churpek · 2021 to 2026
$3.1M
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)R01GM123193 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI CHURPEK, MATTHEW MICHAEL · 2017 to 2021
$1.9M
Using Machine Learning to Predict Clinical Deterioration in Hospitalized ChildrenK01HL148390 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI MAYAMPURATH, ANOOP · 2019 to 2023
$738k
NHLBI NIH HHS K01 HL148390NHLBI NIH HHS R01 HL157262NIDDK NIH HHS R01 DK126933NIGMS NIH HHS R01 GM123193
6 · The paper itself

Abstract

backgroundMachine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

methodsWe conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted machine models developed in patients without COVID-19 to the COVID-19 cohort. We also updated the neural network model using transfer learning. Performance was compared between models and the Cardiac Arrest Survival Post-Resuscitation In-Hospital (CASPRI) score.

resultsAmong the 4,125 patients with COVID-19 included in the analysis, 484 (12 %) patients survived with favorable neurological outcomes. The gradient boosted machine, trained on non-COVID-19 patients was the best performing model for predicting neurological outcomes in COVID-19 patients, significantly better than the CASPRI score (c-statistic: 0.75 vs 0.67, P < 0.001). While calibration improved for the neural network with transfer learning, it did not surpass the gradient boosted machine in terms of discrimination.

conclusionOur gradient boosted machine model developed in non-COVID patients had high discrimination and adequate calibration in COVID-19 resuscitation survivors and may provide clinicians with important information for these patients.

Indexed as

Cardiopulmonary ResuscitationCOVID-19Heart ArrestHospitalsHumansRegistriesRetrospective StudiesCardiac arrestMachine learningNeurological outcomesPrediction

Identifiers

PMID35868590
PMCPMC9295318

What OpenQuestion holds

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Registered trials

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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.