Evidence map›Paper›PMID 31679580›Full record

ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2019

20th Annual Feigenbaum Lecture: Echocardiography for Precision Medicine-Digital Biopsy to Deconstruct Biology.

Sanjiv J Shah

Open access · greenAbstract readLecture
In one paragraph

Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.3field-weighted citation impact, top 10% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Review
  3. Observational
  4. Article
  5. Review
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  8. Review
  9. Heart Failure: Is There an Ideal Biomarker?Reviews in cardiovascular medicine · 2023
    Review
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  11. Review
  12. Article
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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

1 author at 1 institution in 1 country.

Sanjiv J ShahDivision of Cardiology, Department of Medicine, University Feinberg School of Medicine, Chicago, Illinois. Electronic address: sanjiv.shah@northwestern.edu.
Northwestern University · US

Funding

Transition from Risk Factors to Early HF: Prevalence, Pathogenesis, and PhenomicsR01HL127028 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI BERTONI, ALAIN GERALD, SHAH, SANJIV J · 2015 to 2018
$6.5M
Study of Cardiac Mechanics in Systemic HypertensionR01HL107577 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SHAH, SANJIV J · 2011 to 2019
$4.1M
Machine learning for the automated identification and tracking of rare myocardial diseasesR01HL140731 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MACRAE, CALUM A., SHAH, SANJIV J · 2018 to 2021
$2.8M
Precision Medicine for Dilated Cardiomyopathy—Novel Assessment of Cardiac Mechanics via Speckle Tracking Echocardiography to Identify Early PhenotypesR01HL149423 · NHLBI · OHIO STATE UNIVERSITY · PI HERSHBERGER, RAY E., KINNAMON, DANIEL DAVIS · 2019 to 2022
$1.6M
NHLBI NIH HHS R01 HL107577NHLBI NIH HHS R01 HL127028NHLBI NIH HHS R01 HL140731NHLBI NIH HHS R01 HL149423
6 · The paper itself

Abstract

Heart failure with preserved ejection fraction (HFpEF) is a complex, heterogeneous syndrome in need of improved classification given its high morbidity and mortality and few effective treatment options. HFpEF represents an ideal setting to examine the utility and feasibility of a precision medicine approach. This article (based on the 20th annual Feigenbaum Lecture, presented at the 2019 American Society of Echocardiography Scientific Sessions) describes the utility of echocardiography as a "digital biopsy" and how deep quantitative echocardiographic phenotyping, coupled with machine learning, can be used to identify novel HFpEF phenotypes. The cellular and ultrastructural basis of abnormal speckle-tracking echocardiography- (STE-) based measurements of cardiac mechanics can provide a window into cardiomyocyte calcium homeostasis. STE-based measurements of longitudinal strain can thus inform the extent of myocardial involvement in patients with HFpEF, which may help to determine responsiveness to cardiac-specific HF medications. However, classifying the complex, systemic, multiorgan nature of HFpEF appropriately likely requires more advanced methods. Using unsupervised machine learning, HFpEF can be classified into three distinct phenogroups with differing clinical and echocardiographic characteristics and outcomes: (1) natriuretic peptide deficiency syndrome; (2) extreme cardiometabolic syndrome; and (3) right ventricle-cardio-abdomino-renal syndrome. Each can be probed to determine their biological basis. The goal of improved classification of HFpEF is to match the right patient with the right treatment, with the hope of improving the track record of HFpEF clinical trials. This article emphasizes the central role of echocardiography in advancing precision medicine and illustrates the integration of basic, translational, clinical, and population research in echocardiography with the goal of better understanding the pathobiology of a complex cardiovascular syndrome.

Indexed as

Machine LearningEchocardiographyHeart FailureHumansImage-Guided BiopsyPrecision MedicineBasic mechanismsCardiac mechanicsEchocardiographyHeart failure with preserved ejection fractionPrecision medicine

Identifiers

PMID31679580
PMCPMC6830525
OpenAlexW2983514274

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.