Evidence map›Paper›PMID 42806261›Full record

ArticleNeurocritical care2026

Automated Derivation of Cerebral Performance Category at Hospital Discharge After Cardiac Arrest Using Natural Language Processing and Machine Learning.

Sophie Furlow, Parker Houston, Kevin Bao, Bo Zhou, Gerardo Velasquez, Katherine Peterson, Adeline Hayman, Amanjot Bains, Ori Lieberman, Carolyn M Hendrickson and 7 more

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Article in Neurocritical care, 2026. 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
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0citing papers in PubMed
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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

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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

17 authors.

Sophie Furlow *College of Engineering, University of California, Berkeley, CA, USA.
Parker Houston *Department of Pediatrics, University of California, San Francisco Benioff Children's Hospital, Oakland, CA, USA.
Kevin BaoDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Bo ZhouDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Gerardo VelasquezDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Katherine PetersonDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Adeline HaymanDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Amanjot BainsUniversity of California, Berkeley, CA, USA.
Ori LiebermanDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Carolyn M HendricksonDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA.
Lucas ZierDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA.
Neel S SinghalDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
J Claude HemphillDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA.
Sahar ZafarDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
M Brandon WestoverDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Marta FernandesDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Edilberto AmorimDepartment of Neurology, Weill Institute for Neurosciences, University of California, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA. edilbertoamorim@gmail.com.ORCID http://orcid.org/0000-0001-6972-5622

Funding

American Heart Association 20CDA35310297American Heart Association 843457NIH HHS 1K23NS090900NIH HHS 1K23NS119794
6 · The paper itself

Abstract

backgroundClinical notes in the electronic health record (EHR) contain rich information that may be leveraged for automated prognostication using natural language processing (NLP) and machine learning. We evaluated the performance and potential biases of an NLP-based machine learning model for deriving cerebral performance category (CPC) for comatose patients following cardiac arrest using EHR clinical notes.

methodsWe conducted a retrospective cohort study of adult patients who were comatose after in-hospital or out-of-hospital cardiac arrest across three academic hospitals in the USA. Neurological outcomes were assessed at hospital discharge, with CPC scores of 1-3 indicating good outcomes and 4-5 indicating poor outcomes. An NLP-based logistic regression model was trained on 249 patients using all clinical notes throughout hospitalization. Model performance was evaluated on a holdout set of 108 patients. Performance was also evaluated on the holdout set using only notes documented within the first 24 h of hospitalization.

resultsUsing all clinical notes, the model achieved an overall accuracy of 81%, with an area under the receiver operating characteristic curve (AUROC) of 0.90 and an area under the precision-recall curve (AUPRC) of 0.89. When restricted to notes documented within the first 24 h of hospitalization, model accuracy decreased to 74%, with AUROC of 0.83 and AUPRC of 0.83. The model demonstrated higher precision in predicting poor neurological outcomes, particularly among patients who underwent withdrawal of life-sustaining therapy.

conclusionsAn NLP-based machine learning model demonstrated high accuracy in deriving CPC for comatose patients after cardiac arrest using EHR clinical notes. Clinical documentation within 24 h after cardiac arrest contained substantial prognostic signals about neurological outcomes, reflecting a combination of patient biology, clinician assessment, and potentially early prognostic framing. Future research should incorporate multimodal data and employ interpretable advanced language models to increase accuracy toward fully automatable CPC derivation.

Indexed as

Electronic health recordsHeart arrestNatural language processing

Identifiers

PMID42806261

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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.