Evidence map›Paper›PMID 37162979›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Quantitative prediction of right ventricular and size and function from the electrocardiogram.

Son Q Duong, Akhil Vaid, Ha My Thi Vy, Liam R Butler, Joshua Lampert, Robert H Pass, Alexander W Charney, Jagat Narula, Rohan Khera, Hayit Greenspan and 3 more

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

Article in medRxiv : the preprint server for health sciences, 2023. 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, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Son Q DuongDivision of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY.
Akhil VaidThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Ha My Thi VyThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Liam R ButlerDivision of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY.
Joshua LampertHelmsley Center for Electrophysiology at The Mount Sinai Hospital, New York, NY.
Robert H PassDivision of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY.
Alexander W CharneyThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Jagat NarulaMount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY.
Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.
Hayit GreenspanBiomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY.
Bruce D GelbDivision of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY.
Ron DoThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Girish NadkarniThe Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
Icahn School of Medicine at Mount Sinai · USChild Health and Development Institute · USMount Sinai Hospital · USYale New Haven Hospital · US

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
Elucidating hereditary transthyretin-mediated heart failure risk using machine learning, polygenic risk and recall by genotype approaches in African ancestry individualsR01HL155915 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DO, RON, NADKARNI, GIRISH NITIN · 2021 to 2024
$3.4M
NCATS NIH HHS UL1 TR004419NHLBI NIH HHS R01 HL155915
6 · The paper itself

Abstract

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored. Methods: We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m Results: Prevalence of RV dysfunction for UKBB/MSH Conclusions: DL-ECG analysis can accurately identify significant RV dysfunction and dilation both overall and in key subgroups. Predicted RVEF is independently associated with clinical outcome.

Indexed as

cardiac MRIdeep learningelectrocardiogramright ventricle

Identifiers

PMID37162979
PMCPMC10168487
OpenAlexW4367059720

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.