Evidence map›Paper›PMID 32894986›Full record

ArticleCirculation. Heart failure2020

Temporal Trend of Age at Diagnosis in Hypertrophic Cardiomyopathy: An Analysis of the International Sarcomeric Human Cardiomyopathy Registry.

Marco Canepa, Carlo Fumagalli, Giacomo Tini, Justin Vincent-Tompkins, Sharlene M Day, Euan A Ashley, Francesco Mazzarotto, James S Ware, Michelle Michels, Daniel Jacoby and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Circulation. Heart failure, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 pooled it
6.7field-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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it, 87 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

13 authors at 10 institutions in 5 countries.

Marco CanepaCardiovascular Disease Unit, IRCCS Ospedale Policlinico San Martino - IRCCS Italian Cardiovascular Network & Department of Internal Medicine, University of Genova, Italy (M.C., G.T.).
Carlo FumagalliCardiomyopathy Unit and Genetic Unit, Careggi University Hospital, Florence, Italy (C.F., F.M., I.O.).
Giacomo TiniCardiovascular Disease Unit, IRCCS Ospedale Policlinico San Martino - IRCCS Italian Cardiovascular Network & Department of Internal Medicine, University of Genova, Italy (M.C., G.T.).
Justin Vincent-TompkinsMyoKardia Inc, South San Francisco, CA (J.V.-T.).
Sharlene M DayDepartment of Internal Medicine, University of Michigan, Ann Arbor (S.M.D.).
Euan A AshleyStanford Center for Inherited Heart Disease, CA (E.A.A.).
Francesco MazzarottoCardiomyopathy Unit and Genetic Unit, Careggi University Hospital, Florence, Italy (C.F., F.M., I.O.).
James S WareNational Heart and Lung Institute and National Institute for Health Research Royal Brompton Cardiovascular Biomedical Research Unit, Imperial College London, United Kingdom (F.M., J.S.W.).
Michelle MichelsDepartment of Cardiology, Thoraxcenter, Erasmus Medical Center Rotterdam, the Netherlands (M.M.).
Daniel JacobyYale University, New Haven, CT (D.J.).
Carolyn Y HoCardiovascular Division, Brigham and Women's Hospital, Boston, MA (C.Y.H.).
Iacopo OlivottoCardiomyopathy Unit and Genetic Unit, Careggi University Hospital, Florence, Italy (C.F., F.M., I.O.).
SHaRe Investigators
Lung Institute · USOspedale Policlinico San Martino · ITAzienda Ospedaliero-Universitaria Careggi · ITBrigham and Women's Hospital · USCenter for Inherited Blood Disorders · USCTO Hospital · ITErasmus MC · NLFederal Ministry of Justice · ATUniversity of Michigan · USYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
HCMR Novel Markers of Prognosis in Hypertrophic CardiomyopathyU01HL117006 · NHLBI · UNIVERSITY OF VIRGINIA · PI KRAMER, CHRISTOPHER M., NEUBAUER, STEFAN · 2013 to 2017
$14.5M
Using Genetics For Early Phenotyping & Prevention of Hypertrophic CardiomyopathyP50HL112349 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI HO, CAROLYN Y, SEIDMAN, CHRISTINE E · 2012 to 2017
$11.2M
Medical Research Council MC_UP_1102/20NCATS NIH HHS UL1 TR001863NHLBI NIH HHS P50 HL112349NHLBI NIH HHS U01 HL117006Wellcome Trust 107469/Z/15/Z
6 · The paper itself

Abstract

backgroundOver the last 50 years, the epidemiology of hypertrophic cardiomyopathy (HCM) has changed because of increased awareness and availability of advanced diagnostic tools. We aim to describe the temporal trends in age, sex, and clinical characteristics at HCM diagnosis over >4 decades.

methodsWe retrospectively analyzed records from the ongoing multinational Sarcomeric Human Cardiomyopathy Registry. Overall, 7286 patients with HCM diagnosed at an age ≥18 years between 1961 and 2019 were included in the analysis and divided into 3 eras of diagnosis (<2000, 2000-2010, >2010).

resultsAge at diagnosis increased markedly over time (40±14 versus 47±15 versus 51±16 years,

conclusionsEvolving HCM populations include progressively greater representation of older patients with sporadic disease, mild phenotypes, and genotype-negative status. Such trend suggests a prominent role of imaging over genetic testing in promoting HCM diagnoses and urges efforts to understand genotype-negative disease eluding the classic monogenic paradigm.

Indexed as

AdultAgedAged, 80 and overAge FactorsCardiomyopathy, HypertrophicFemaleGenetic TestingGenotypeHumansInternationalityMaleMiddle AgedPhenotypeRegistriesRetrospective StudiesSex Factorscardiomyopathy, hypertrophicgenotypeheart failurephenotypeprevalence

Identifiers

PMID32894986
PMCPMC7497482
OpenAlexW3083873332

What OpenQuestion holds

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LicenceCC BY
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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.