Evidence map›Paper›PMID 37237055›Full record

ArticleCommunications medicine2023

Deep learning-based electrocardiographic screening for chronic kidney disease.

Lauri Holmstrom, Matthew Christensen, Neal Yuan, J Weston Hughes, John Theurer, Melvin Jujjavarapu, Pedram Fatehi, Alan Kwan, Roopinder K Sandhu, Joseph Ebinger and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Observational
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  4. Review
  5. Observational
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  7. Review
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  18. Reliable Multi-Class Mental Health Prediction Using a WiSARD Discriminator Model on Imbalanced Data.Inquiry : a journal of medical care organization, provision and financing
    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

14 authors.

Lauri Holmstrom *Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Matthew Christensen *Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Neal YuanDepartment of Medicine, Division of Cardiology, San Francisco VA, UCSF, San Francisco, CA, USA.
J Weston HughesDepartment of Computer Science, Stanford University, Palo Alto, CA, USA.
John TheurerDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Melvin JujjavarapuEnterprise Information Service, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Pedram FatehiDivision of Nephrology, Department of Medicine, Stanford University, Palo Alto, CA, USA.
Alan KwanDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-4393-1011
Roopinder K SandhuDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Joseph EbingerDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Susan ChengDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
James ZouDepartment of Computer Science, Stanford University, Palo Alto, CA, USA.ORCID http://orcid.org/0000-0001-8880-4764
Sumeet S ChughDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
David OuyangDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. David.Ouyang@cshs.org.ORCID http://orcid.org/0000-0002-3813-7518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUndiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs.

methodsWe collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018.

resultsUsing 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]).

conclusionsOur deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.

Identifiers

PMID37237055
PMCPMC10220039

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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.