Evidence map›Paper›PMID 36706977›Full record

ArticleJournal of cardiac failure2023

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

Mandar A Aras, Sean Abreau, Hunter Mills, Lakshmi Radhakrishnan, Liviu Klein, Neha Mantri, Benjamin Rubin, Joshua Barrios, Christel Chehoud, Emily Kogan and 7 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiac failure, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 3 pooled it
7.9field-weighted citation impact, top 2% of its field
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

26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.

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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

17 authors at 3 institutions in 2 countries.

Mandar A ArasUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Sean AbreauUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Hunter MillsBakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Lakshmi RadhakrishnanBakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Liviu KleinUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Neha MantriUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Benjamin RubinBakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Joshua BarriosUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Christel ChehoudJanssen Pharmaceuticals, Inc, Raritan, New Jersey.
Emily KoganJanssen Pharmaceuticals, Inc, Raritan, New Jersey.
Xavier GittonActelion Pharmaceuticals Ltd., Allschwil, Switzerland.
Anderson NnewiheJanssen Pharmaceuticals, Inc, Raritan, New Jersey.
Deborah QuinnJanssen Pharmaceuticals, Inc, Raritan, New Jersey.
Charles BridgesJanssen Pharmaceuticals, Inc, Raritan, New Jersey.
Atul J ButteBakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California.
Jeffrey E OlginUCSF Department of Medicine, Division of Cardiology, San Francisco, California.
Geoffrey H TisonUCSF Department of Medicine, Division of Cardiology, San Francisco, California; Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California; Division of Cardiology, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, San Francisco, California. Electronic address: Geoff.Tison@ucsf.edu.
University of California, San Francisco · USJanssen (United States) · USActelion (Switzerland) · CH

Funding

Dynamic prediction of heart failure using real-time functional status and EHR data in the ambulatory settingK23HL135274 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TISON, GEOFFREY H · 2018 to 2022
$999k
NHLBI NIH HHS K23 HL135274
6 · The paper itself

Abstract

backgroundPulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach using electrocardiogram (ECG) data alone can detect PH and clinically important subtypes. We asked: does an automated deep learning approach to ECG interpretation detect PH and its clinically important subtypes? METHODS AND

resultsAdults with right heart catheterization or an echocardiogram within 90 days of an ECG at the University of California, San Francisco (2012-2019) were retrospectively identified as PH or non-PH. A deep convolutional neural network was trained on patients' 12-lead ECG voltage data. Patients were divided into training, development, and test sets in a ratio of 7:1:2. Overall, 5016 PH and 19,454 patients without PH were used in the study. The mean age at the time of ECG was 62.29 ± 17.58 years and 49.88% were female. The mean interval between ECG and right heart catheterization or echocardiogram was 3.66 and 2.23 days for patients with PH and patients without PH, respectively. In the test dataset, the model achieved an area under the receiver operating characteristic curve, sensitivity, and specificity, respectively of 0.89, 0.79, and 0.84 to detect PH; 0.91, 0.83, and 0.84 to detect precapillary PH; 0.88, 0.81, and 0.81 to detect pulmonary arterial hypertension, and 0.80, 0.73, and 0.76 to detect group 3 PH. We additionally applied the trained model on ECGs from participants in the test dataset that were obtained from up to 2 years before diagnosis of PH; the area under the receiver operating characteristic curve was 0.79 or greater.

conclusionsA deep learning ECG algorithm can detect PH and PH subtypes around the time of diagnosis and can detect PH using ECGs that were done up to 2 years before right heart catheterization/echocardiogram diagnosis. This approach has the potential to decrease diagnostic delays in PH.

Indexed as

Deep LearningHeart FailureHypertension, PulmonaryAdultElectrocardiographyFemaleHumansMaleRetrospective StudiesArtificial intelligencedeep learningelectrocardiogrampulmonary hypertension

Identifiers

PMID36706977
PMCPMC10363571
OpenAlexW4317936535

What OpenQuestion holds

Textmetadata
LicenceTDM
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.