Evidence map›Paper›PMID 28961271›Full record

ArticlePloS one2017

Multiplanar strain quantification for assessment of right ventricular dysfunction and non-ischemic fibrosis among patients with ischemic mitral regurgitation.

Antonino Di Franco, Jiwon Kim, Sara Rodriguez-Diego, Omar Khalique, Jonathan Y Siden, Samantha R Goldburg, Neil K Mehta, Aparna Srinivasan, Mark B Ratcliffe, Robert A Levine and 3 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

8 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
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  7. Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2019
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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

13 authors at 5 institutions in 2 countries.

Antonino Di FrancoDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Jiwon KimDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Sara Rodriguez-DiegoDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Omar KhaliqueDepartment of Medicine, Columbia University, New York, New York, United States of America.
Jonathan Y SidenDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Samantha R GoldburgDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Neil K MehtaDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Aparna SrinivasanDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Mark B RatcliffeDepartment of Surgery, University of California San Francisco, San Francisco, California, United States of America.
Robert A LevineDepartment of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
Filippo CreaDepartment of Cardiology, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli, Rome, Italy.
Richard B DevereuxDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.
Jonathan W WeinsaftDepartment of Medicine, Weill Cornell Medical College, New York City, New York, United States of America.ORCID http://orcid.org/0000-0002-8386-0297
Cornell University · USColumbia University · USMassachusetts General Hospital · USUniversità Cattolica del Sacro Cuore · ITUniversity of California, San Francisco · US

Funding

CMR Myocardial Tissue Based Prediction of Ischemic MR Revascularization ResponseR01HL128278 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI WEINSAFT, JONATHAN W. · 2015 to 2019
$3.8M
Treating Ventricle and Valve: New Synergies for Ischemic LV Remodeling with MRR01HL128099 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI AKAR, FADI GABRIEL, LEVINE, ROBERT A · 2016 to 2019
$2.8M
NHLBI NIH HHS R01 HL128099NHLBI NIH HHS R01 HL128278
6 · The paper itself

Abstract

backgroundIschemic mitral regurgitation (iMR) predisposes to right ventricular (RV) pressure and volume overload, providing a nidus for RV dysfunction (RVDYS) and non-ischemic fibrosis (NIF). Echocardiography (echo) is widely used to assess iMR, but performance of different indices as markers of RVDYS and NIF is unknown.

methodsiMR patients prospectively underwent echo and cardiac magnetic resonance (CMR) within 72 hours. Echo quantified iMR, assessed conventional RV indices (TAPSE, RV-S', fractional area change [FAC]), and strain via speckle tracking in apical 4-chamber (global longitudinal strain [RV-GLS]) and parasternal long axis orientation (transverse strain). CMR volumetrically quantified RVEF, and assessed ischemic pattern myocardial infarction (MI) and septal NIF.

results73 iMR patients were studied; 36% had RVDYS (EF<50%) on CMR among whom LVEF was lower, PA systolic pressure higher, and MI size larger (all p<0.05). CMR RVEF was paralleled by echo results; correlations were highest for RV-GLS (r = 0.73) and lowest for RV-S' (r = 0.43; all p<0.001). RVDYS patients more often had CMR-evidenced NIF (54% vs. 7%; p<0.001). Whereas all RV indices were lower among NIF-affected patients (all p≤0.006), percent change was largest for transverse strain (48.3%). CMR RVEF was independently associated with RV-GLS (partial r = 0.57, p<0.001) and transverse strain (r = 0.38, p = 0.002) (R = 0.78, p<0.001). Overall diagnostic performance of RV-GLS and transverse strain were similar (AUC = 0.93[0.87-0.99]|0.91[0.84-0.99], both p<0.001), and yielded near equivalent sensitivity and specificity (85%|83% and 80%|79% respectively).

conclusionCompared to conventional echo indices, RV strain parameters yield stronger correlation with CMR-defined RVEF and potentially constitute better markers of CMR-evidenced NIF in iMR.

Indexed as

FibrosisVentricular Function, RightAgedEchocardiographyFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMitral Valve InsufficiencyProspective Studies

Identifiers

PMID28961271
PMCPMC5621708
OpenAlexW2759505716

What OpenQuestion holds

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Read underepoch 390

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.