Selective improvement in Seattle Heart Failure Model risk stratification using iodine-123 meta-iodobenzylguanidine imaging View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-10

AUTHORS

Eric S. Ketchum, Arnold F. Jacobson, James H. Caldwell, Roxy Senior, Manuel D. Cerqueira, Gregory S. Thomas, Denis Agostini, Jagat Narula, Wayne C. Levy

ABSTRACT

BACKGROUND: The Seattle Heart Failure Model (SHFM) is a multivariable model that uses demographic and clinical markers to predict survival in patients with heart failure. Inappropriate activation of the sympathetic nervous system, which contributes to the progression of heart failure and increased mortality, can be assessed using iodine-123 meta-iodobenzylguanidine (MIBG) cardiac imaging. This study investigated the incremental value of MIBG cardiac imaging when added to the SHFM for prediction of all-cause mortality. METHODS: Survival data from 961 NYHA II-III subjects in the ADMIRE-HFX trial were included in this analysis. The predictive value of the SHFM alone and in combination with MIBG heart-to-mediastinum ratio (H/M) was compared for all-cause mortality (101 deaths during a median follow-up of 2 years). RESULTS: The addition of H/M to the SHFM in a Cox model significantly improved risk prediction (P < .0001), with a greater utility in higher risk SHFM patients. The observed 2-year mortality in the highest-risk SHFM subjects (rounded SHFM score of 1) was 24%, but varied from 46% with H/M <1.2 to 0% with H/M >1.8. Net reclassification improvement was 22.7% (P < .001), with 14.9% of subjects who died reclassified into a higher risk category than suggested by SHFM score alone (P = .01) and 7.9% of subjects who survived reclassified into a lower risk category (P < .0001). The 2-year integrated discrimination improvement (+4.14%, P < .0001) and the 1-year area under the receiver-operator characteristic curve (+0.04, P = .026) both showed significant improvement for the combined model with H/M compared to the SHFM alone. CONCLUSION: The addition of MIBG imaging to the SHFM improves risk stratification, especially in higher risk patients. MIBG may have clinical utility in higher risk patients who are being considered for devices such as ICD, CRT-D, LVAD, and cardiac transplantation. More... »

PAGES

1007-1016

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12350-012-9603-0

DOI

http://dx.doi.org/10.1007/s12350-012-9603-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008416505

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/22949270


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1102", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Cardiorespiratory Medicine and Haematology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "3-Iodobenzylguanidine", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Area Under Curve", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Female", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Heart Failure", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Kaplan-Meier Estimate", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Male", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Multivariate Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Proportional Hazards Models", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radionuclide Imaging", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiopharmaceuticals", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Risk", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Washington", 
          "id": "https://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Division of Cardiology, University of Washington, 1959 NE Pacific Street, P.O. Box 356422, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ketchum", 
        "givenName": "Eric S.", 
        "id": "sg:person.0667655076.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0667655076.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "GE Healthcare (United States)", 
          "id": "https://www.grid.ac/institutes/grid.474545.3", 
          "name": [
            "GE Healthcare, Princeton, NJ, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jacobson", 
        "givenName": "Arnold F.", 
        "id": "sg:person.01014520400.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014520400.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Washington", 
          "id": "https://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Division of Cardiology, University of Washington, 1959 NE Pacific Street, P.O. Box 356422, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Caldwell", 
        "givenName": "James H.", 
        "id": "sg:person.01172021053.68", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01172021053.68"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imperial College London", 
          "id": "https://www.grid.ac/institutes/grid.7445.2", 
          "name": [
            "BRU, Royal Brompton Hospital, Imperial College, London and Northwick Park Hospital, Harrow, United Kingdom"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Senior", 
        "givenName": "Roxy", 
        "id": "sg:person.0726632134.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726632134.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Cleveland Clinic", 
          "id": "https://www.grid.ac/institutes/grid.239578.2", 
          "name": [
            "Imaging and Heart and Vascular Institutes, Cleveland Clinic, Cleveland, OH, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cerqueira", 
        "givenName": "Manuel D.", 
        "id": "sg:person.01101651666.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101651666.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Long Beach Memorial Medical Center", 
          "id": "https://www.grid.ac/institutes/grid.415304.7", 
          "name": [
            "Long Beach Memorial Medical Center, Long Beach, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Thomas", 
        "givenName": "Gregory S.", 
        "id": "sg:person.01341543437.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341543437.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centre Hospitalier Universitaire de Caen", 
          "id": "https://www.grid.ac/institutes/grid.411149.8", 
          "name": [
            "Centre Hospitalier Universitaire Cote de Nacre, Caen, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Agostini", 
        "givenName": "Denis", 
        "id": "sg:person.01254275406.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254275406.08"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Icahn School of Medicine at Mount Sinai", 
          "id": "https://www.grid.ac/institutes/grid.59734.3c", 
          "name": [
            "Division of Cardiology, Mount Sinai School of Medicine, New York, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Narula", 
        "givenName": "Jagat", 
        "id": "sg:person.01052643704.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052643704.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Washington", 
          "id": "https://www.grid.ac/institutes/grid.34477.33", 
          "name": [
            "Division of Cardiology, University of Washington, 1959 NE Pacific Street, P.O. Box 356422, Seattle, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Levy", 
        "givenName": "Wayne C.", 
        "id": "sg:person.0745731013.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745731013.51"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1056/nejm198207223070401", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000155380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2010.10.040", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002537813"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amjcard.2007.03.083", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005532182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.106.672402", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006648149"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jcmg.2010.10.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006921344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.amjcard.2010.12.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007837026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.105.584102", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008451871"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2007.08.058", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009138600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1136/hrt.2010.204149", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009802228"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2009.12.066", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010295678"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.108.816884", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012327179"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/mat.0b013e31824450f9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015644420"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/mat.0b013e31824450f9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015644420"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.hfc.2010.12.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021056003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejmoa055373", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021227277"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/ede.0b013e3181c30fb2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023301544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/ede.0b013e3181c30fb2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023301544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/ede.0b013e3181c30fb2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023301544"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/1097-0258(20001230)19:24<3401::aid-sim554>3.0.co;2-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023725669"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2006.10.079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028613085"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.106.687103", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028760692"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejcts.2010.07.049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030023578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0002-9149(85)90791-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033024518"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12149-011-0479-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033974061", 
          "https://doi.org/10.1007/s12149-011-0479-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circulationaha.109.858076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035091529"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.healun.2012.04.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035333909"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/01.cir.73.4.615", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036132319"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2010.01.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038380674"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jacc.2008.10.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043991259"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00392-008-0656-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044508918", 
          "https://doi.org/10.1007/s00392-008-0656-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00392-008-0656-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044508918", 
          "https://doi.org/10.1007/s00392-008-0656-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00259-010-1617-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044673240", 
          "https://doi.org/10.1007/s00259-010-1617-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/sim.2929", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044960408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/eurheartj/ehn113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045470525"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1056/nejm198409273111303", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048410078"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.healun.2010.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050720551"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/cir.0b013e31823ac046", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051424218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s12350-008-9008-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052487595", 
          "https://doi.org/10.1007/s12350-008-9008-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.108.782433", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052558953"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circimaging.108.782433", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052558953"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circheartfailure.110.958223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053476118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1161/circheartfailure.110.958223", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053476118"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ehj.2003.10.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054732345"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1055/s-0029-1240687", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057206965"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/oxfordjournals.aje.a113284", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1082126726"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082362182", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1082369100", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-10", 
    "datePublishedReg": "2012-10-01", 
    "description": "BACKGROUND: The Seattle Heart Failure Model (SHFM) is a multivariable model that uses demographic and clinical markers to predict survival in patients with heart failure. Inappropriate activation of the sympathetic nervous system, which contributes to the progression of heart failure and increased mortality, can be assessed using iodine-123 meta-iodobenzylguanidine (MIBG) cardiac imaging. This study investigated the incremental value of MIBG cardiac imaging when added to the SHFM for prediction of all-cause mortality.\nMETHODS: Survival data from 961 NYHA II-III subjects in the ADMIRE-HFX trial were included in this analysis. The predictive value of the SHFM alone and in combination with MIBG heart-to-mediastinum ratio (H/M) was compared for all-cause mortality (101 deaths during a median follow-up of 2 years).\nRESULTS: The addition of H/M to the SHFM in a Cox model significantly improved risk prediction (P < .0001), with a greater utility in higher risk SHFM patients. The observed 2-year mortality in the highest-risk SHFM subjects (rounded SHFM score of 1) was 24%, but varied from 46% with H/M <1.2 to 0% with H/M >1.8. Net reclassification improvement was 22.7% (P < .001), with 14.9% of subjects who died reclassified into a higher risk category than suggested by SHFM score alone (P = .01) and 7.9% of subjects who survived reclassified into a lower risk category (P < .0001). The 2-year integrated discrimination improvement (+4.14%, P < .0001) and the 1-year area under the receiver-operator characteristic curve (+0.04, P = .026) both showed significant improvement for the combined model with H/M compared to the SHFM alone.\nCONCLUSION: The addition of MIBG imaging to the SHFM improves risk stratification, especially in higher risk patients. MIBG may have clinical utility in higher risk patients who are being considered for devices such as ICD, CRT-D, LVAD, and cardiac transplantation.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s12350-012-9603-0", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1106202", 
        "issn": [
          "1071-3581", 
          "1532-6551"
        ], 
        "name": "Journal of Nuclear Cardiology", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Selective improvement in Seattle Heart Failure Model risk stratification using iodine-123 meta-iodobenzylguanidine imaging", 
    "pagination": "1007-1016", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "28189b247336d5750612768f9c7bed8aff1956a2e909c424201fa2f9b79236e3"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "22949270"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "9423534"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s12350-012-9603-0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008416505"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s12350-012-9603-0", 
      "https://app.dimensions.ai/details/publication/pub.1008416505"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T14:15", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000536.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2Fs12350-012-9603-0"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s12350-012-9603-0'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s12350-012-9603-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12350-012-9603-0'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12350-012-9603-0'


 

This table displays all metadata directly associated to this object as RDF triples.

328 TRIPLES      21 PREDICATES      85 URIs      36 LITERALS      24 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s12350-012-9603-0 schema:about N0132713bdfc44370b8ac631a2d9d1aea
2 N1a4cba964a554d0aa2f0069763e8b48d
3 N24a72972f8c04c6bb767cfbaff7b6a06
4 N48254a1827d44f0bafd4c6c4483260ec
5 N5b49f721d8e04c328f5ceae5fcbc5b23
6 N71af4689c1454e689b8305d34380aa51
7 N747575cf8ab74a22b57a4d595d6e03af
8 N7d9c2fdbf310405cb94df2ce4d5c6b4d
9 N850d16e8f57d469cb8eb9467ef1aecc8
10 N88ff05e495aa40ba95b6314ead0f13e6
11 N90ba9e5ec6374e829ad99a80ffd9e2e6
12 Nb647945380d741f8b25d33603e94688a
13 Ncbf1bd3661c247249d0e3b26c252b427
14 Nd24eeeec8b3949568c7f861e759ba46c
15 Ndc0faeb4e03c467895bf26c7531498d9
16 anzsrc-for:11
17 anzsrc-for:1102
18 schema:author N03c91ff6d392431db56f5f836c484229
19 schema:citation sg:pub.10.1007/s00259-010-1617-8
20 sg:pub.10.1007/s00392-008-0656-7
21 sg:pub.10.1007/s12149-011-0479-x
22 sg:pub.10.1007/s12350-008-9008-2
23 https://app.dimensions.ai/details/publication/pub.1082362182
24 https://app.dimensions.ai/details/publication/pub.1082369100
25 https://doi.org/10.1002/1097-0258(20001230)19:24<3401::aid-sim554>3.0.co;2-2
26 https://doi.org/10.1002/sim.2929
27 https://doi.org/10.1016/0002-9149(85)90791-x
28 https://doi.org/10.1016/j.amjcard.2007.03.083
29 https://doi.org/10.1016/j.amjcard.2010.12.019
30 https://doi.org/10.1016/j.ehj.2003.10.030
31 https://doi.org/10.1016/j.ejcts.2010.07.049
32 https://doi.org/10.1016/j.healun.2010.05.002
33 https://doi.org/10.1016/j.healun.2012.04.006
34 https://doi.org/10.1016/j.hfc.2010.12.001
35 https://doi.org/10.1016/j.jacc.2006.10.079
36 https://doi.org/10.1016/j.jacc.2007.08.058
37 https://doi.org/10.1016/j.jacc.2008.10.023
38 https://doi.org/10.1016/j.jacc.2009.12.066
39 https://doi.org/10.1016/j.jacc.2010.01.014
40 https://doi.org/10.1016/j.jacc.2010.10.040
41 https://doi.org/10.1016/j.jcmg.2010.10.005
42 https://doi.org/10.1055/s-0029-1240687
43 https://doi.org/10.1056/nejm198207223070401
44 https://doi.org/10.1056/nejm198409273111303
45 https://doi.org/10.1056/nejmoa055373
46 https://doi.org/10.1093/eurheartj/ehn113
47 https://doi.org/10.1093/oxfordjournals.aje.a113284
48 https://doi.org/10.1097/ede.0b013e3181c30fb2
49 https://doi.org/10.1097/mat.0b013e31824450f9
50 https://doi.org/10.1136/hrt.2010.204149
51 https://doi.org/10.1161/01.cir.73.4.615
52 https://doi.org/10.1161/cir.0b013e31823ac046
53 https://doi.org/10.1161/circheartfailure.110.958223
54 https://doi.org/10.1161/circimaging.108.782433
55 https://doi.org/10.1161/circulationaha.105.584102
56 https://doi.org/10.1161/circulationaha.106.672402
57 https://doi.org/10.1161/circulationaha.106.687103
58 https://doi.org/10.1161/circulationaha.108.816884
59 https://doi.org/10.1161/circulationaha.109.858076
60 schema:datePublished 2012-10
61 schema:datePublishedReg 2012-10-01
62 schema:description BACKGROUND: The Seattle Heart Failure Model (SHFM) is a multivariable model that uses demographic and clinical markers to predict survival in patients with heart failure. Inappropriate activation of the sympathetic nervous system, which contributes to the progression of heart failure and increased mortality, can be assessed using iodine-123 meta-iodobenzylguanidine (MIBG) cardiac imaging. This study investigated the incremental value of MIBG cardiac imaging when added to the SHFM for prediction of all-cause mortality. METHODS: Survival data from 961 NYHA II-III subjects in the ADMIRE-HFX trial were included in this analysis. The predictive value of the SHFM alone and in combination with MIBG heart-to-mediastinum ratio (H/M) was compared for all-cause mortality (101 deaths during a median follow-up of 2 years). RESULTS: The addition of H/M to the SHFM in a Cox model significantly improved risk prediction (P < .0001), with a greater utility in higher risk SHFM patients. The observed 2-year mortality in the highest-risk SHFM subjects (rounded SHFM score of 1) was 24%, but varied from 46% with H/M <1.2 to 0% with H/M >1.8. Net reclassification improvement was 22.7% (P < .001), with 14.9% of subjects who died reclassified into a higher risk category than suggested by SHFM score alone (P = .01) and 7.9% of subjects who survived reclassified into a lower risk category (P < .0001). The 2-year integrated discrimination improvement (+4.14%, P < .0001) and the 1-year area under the receiver-operator characteristic curve (+0.04, P = .026) both showed significant improvement for the combined model with H/M compared to the SHFM alone. CONCLUSION: The addition of MIBG imaging to the SHFM improves risk stratification, especially in higher risk patients. MIBG may have clinical utility in higher risk patients who are being considered for devices such as ICD, CRT-D, LVAD, and cardiac transplantation.
63 schema:genre research_article
64 schema:inLanguage en
65 schema:isAccessibleForFree false
66 schema:isPartOf N3953d5e1a0b04b3998c8612e88ec6e28
67 Nb10a971bf2ef45ff820a2970cca322eb
68 sg:journal.1106202
69 schema:name Selective improvement in Seattle Heart Failure Model risk stratification using iodine-123 meta-iodobenzylguanidine imaging
70 schema:pagination 1007-1016
71 schema:productId N0dc7c9606e4049d28f00554c2c165638
72 N30bf94ce2cf247709b6c49acbda3b104
73 N3475e9cfe80d4f9e9bb1996531223a10
74 N87c82d54ab0b4b39bff74d21ba5c1593
75 N8abff0efefd44649afb8c55b34cd7d1d
76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008416505
77 https://doi.org/10.1007/s12350-012-9603-0
78 schema:sdDatePublished 2019-04-10T14:15
79 schema:sdLicense https://scigraph.springernature.com/explorer/license/
80 schema:sdPublisher N7ca45bee04314b5fa19449a58931a2f1
81 schema:url http://link.springer.com/10.1007%2Fs12350-012-9603-0
82 sgo:license sg:explorer/license/
83 sgo:sdDataset articles
84 rdf:type schema:ScholarlyArticle
85 N0132713bdfc44370b8ac631a2d9d1aea schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
86 schema:name Heart Failure
87 rdf:type schema:DefinedTerm
88 N03c91ff6d392431db56f5f836c484229 rdf:first sg:person.0667655076.53
89 rdf:rest N5d5f126f858943bfaca290842bd55129
90 N0dc7c9606e4049d28f00554c2c165638 schema:name doi
91 schema:value 10.1007/s12350-012-9603-0
92 rdf:type schema:PropertyValue
93 N1a4cba964a554d0aa2f0069763e8b48d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
94 schema:name Radiopharmaceuticals
95 rdf:type schema:DefinedTerm
96 N1bba4ac6cd5742ae81cb3c0598316668 rdf:first sg:person.01341543437.45
97 rdf:rest Nc487e59e6d964b6d9f4286dfabb776c2
98 N234c29f86b1140ee9e802018b5307c37 rdf:first sg:person.01052643704.75
99 rdf:rest N40bf3876861947fb96fe71b3daf59a21
100 N24a72972f8c04c6bb767cfbaff7b6a06 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
101 schema:name Aged
102 rdf:type schema:DefinedTerm
103 N30bf94ce2cf247709b6c49acbda3b104 schema:name dimensions_id
104 schema:value pub.1008416505
105 rdf:type schema:PropertyValue
106 N3475e9cfe80d4f9e9bb1996531223a10 schema:name readcube_id
107 schema:value 28189b247336d5750612768f9c7bed8aff1956a2e909c424201fa2f9b79236e3
108 rdf:type schema:PropertyValue
109 N3953d5e1a0b04b3998c8612e88ec6e28 schema:issueNumber 5
110 rdf:type schema:PublicationIssue
111 N40bf3876861947fb96fe71b3daf59a21 rdf:first sg:person.0745731013.51
112 rdf:rest rdf:nil
113 N48254a1827d44f0bafd4c6c4483260ec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
114 schema:name Humans
115 rdf:type schema:DefinedTerm
116 N4afc9d3d025143c0b4e01aa468ca2197 rdf:first sg:person.0726632134.33
117 rdf:rest N7bb6f3cc980f4187971db5b8b865eb50
118 N5b49f721d8e04c328f5ceae5fcbc5b23 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
119 schema:name Male
120 rdf:type schema:DefinedTerm
121 N5d5f126f858943bfaca290842bd55129 rdf:first sg:person.01014520400.86
122 rdf:rest Nfd708125b7f646c2bbfabc91dde7b41b
123 N71af4689c1454e689b8305d34380aa51 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
124 schema:name Female
125 rdf:type schema:DefinedTerm
126 N747575cf8ab74a22b57a4d595d6e03af schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
127 schema:name Risk
128 rdf:type schema:DefinedTerm
129 N7bb6f3cc980f4187971db5b8b865eb50 rdf:first sg:person.01101651666.29
130 rdf:rest N1bba4ac6cd5742ae81cb3c0598316668
131 N7ca45bee04314b5fa19449a58931a2f1 schema:name Springer Nature - SN SciGraph project
132 rdf:type schema:Organization
133 N7d9c2fdbf310405cb94df2ce4d5c6b4d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
134 schema:name Radionuclide Imaging
135 rdf:type schema:DefinedTerm
136 N850d16e8f57d469cb8eb9467ef1aecc8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
137 schema:name Middle Aged
138 rdf:type schema:DefinedTerm
139 N87c82d54ab0b4b39bff74d21ba5c1593 schema:name nlm_unique_id
140 schema:value 9423534
141 rdf:type schema:PropertyValue
142 N88ff05e495aa40ba95b6314ead0f13e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
143 schema:name Aged, 80 and over
144 rdf:type schema:DefinedTerm
145 N8abff0efefd44649afb8c55b34cd7d1d schema:name pubmed_id
146 schema:value 22949270
147 rdf:type schema:PropertyValue
148 N90ba9e5ec6374e829ad99a80ffd9e2e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
149 schema:name 3-Iodobenzylguanidine
150 rdf:type schema:DefinedTerm
151 Nb10a971bf2ef45ff820a2970cca322eb schema:volumeNumber 19
152 rdf:type schema:PublicationVolume
153 Nb647945380d741f8b25d33603e94688a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Area Under Curve
155 rdf:type schema:DefinedTerm
156 Nc487e59e6d964b6d9f4286dfabb776c2 rdf:first sg:person.01254275406.08
157 rdf:rest N234c29f86b1140ee9e802018b5307c37
158 Ncbf1bd3661c247249d0e3b26c252b427 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
159 schema:name Kaplan-Meier Estimate
160 rdf:type schema:DefinedTerm
161 Nd24eeeec8b3949568c7f861e759ba46c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Multivariate Analysis
163 rdf:type schema:DefinedTerm
164 Ndc0faeb4e03c467895bf26c7531498d9 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name Proportional Hazards Models
166 rdf:type schema:DefinedTerm
167 Nfd708125b7f646c2bbfabc91dde7b41b rdf:first sg:person.01172021053.68
168 rdf:rest N4afc9d3d025143c0b4e01aa468ca2197
169 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
170 schema:name Medical and Health Sciences
171 rdf:type schema:DefinedTerm
172 anzsrc-for:1102 schema:inDefinedTermSet anzsrc-for:
173 schema:name Cardiorespiratory Medicine and Haematology
174 rdf:type schema:DefinedTerm
175 sg:journal.1106202 schema:issn 1071-3581
176 1532-6551
177 schema:name Journal of Nuclear Cardiology
178 rdf:type schema:Periodical
179 sg:person.01014520400.86 schema:affiliation https://www.grid.ac/institutes/grid.474545.3
180 schema:familyName Jacobson
181 schema:givenName Arnold F.
182 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014520400.86
183 rdf:type schema:Person
184 sg:person.01052643704.75 schema:affiliation https://www.grid.ac/institutes/grid.59734.3c
185 schema:familyName Narula
186 schema:givenName Jagat
187 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01052643704.75
188 rdf:type schema:Person
189 sg:person.01101651666.29 schema:affiliation https://www.grid.ac/institutes/grid.239578.2
190 schema:familyName Cerqueira
191 schema:givenName Manuel D.
192 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01101651666.29
193 rdf:type schema:Person
194 sg:person.01172021053.68 schema:affiliation https://www.grid.ac/institutes/grid.34477.33
195 schema:familyName Caldwell
196 schema:givenName James H.
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01172021053.68
198 rdf:type schema:Person
199 sg:person.01254275406.08 schema:affiliation https://www.grid.ac/institutes/grid.411149.8
200 schema:familyName Agostini
201 schema:givenName Denis
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01254275406.08
203 rdf:type schema:Person
204 sg:person.01341543437.45 schema:affiliation https://www.grid.ac/institutes/grid.415304.7
205 schema:familyName Thomas
206 schema:givenName Gregory S.
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01341543437.45
208 rdf:type schema:Person
209 sg:person.0667655076.53 schema:affiliation https://www.grid.ac/institutes/grid.34477.33
210 schema:familyName Ketchum
211 schema:givenName Eric S.
212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0667655076.53
213 rdf:type schema:Person
214 sg:person.0726632134.33 schema:affiliation https://www.grid.ac/institutes/grid.7445.2
215 schema:familyName Senior
216 schema:givenName Roxy
217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0726632134.33
218 rdf:type schema:Person
219 sg:person.0745731013.51 schema:affiliation https://www.grid.ac/institutes/grid.34477.33
220 schema:familyName Levy
221 schema:givenName Wayne C.
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0745731013.51
223 rdf:type schema:Person
224 sg:pub.10.1007/s00259-010-1617-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044673240
225 https://doi.org/10.1007/s00259-010-1617-8
226 rdf:type schema:CreativeWork
227 sg:pub.10.1007/s00392-008-0656-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044508918
228 https://doi.org/10.1007/s00392-008-0656-7
229 rdf:type schema:CreativeWork
230 sg:pub.10.1007/s12149-011-0479-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033974061
231 https://doi.org/10.1007/s12149-011-0479-x
232 rdf:type schema:CreativeWork
233 sg:pub.10.1007/s12350-008-9008-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052487595
234 https://doi.org/10.1007/s12350-008-9008-2
235 rdf:type schema:CreativeWork
236 https://app.dimensions.ai/details/publication/pub.1082362182 schema:CreativeWork
237 https://app.dimensions.ai/details/publication/pub.1082369100 schema:CreativeWork
238 https://doi.org/10.1002/1097-0258(20001230)19:24<3401::aid-sim554>3.0.co;2-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023725669
239 rdf:type schema:CreativeWork
240 https://doi.org/10.1002/sim.2929 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044960408
241 rdf:type schema:CreativeWork
242 https://doi.org/10.1016/0002-9149(85)90791-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1033024518
243 rdf:type schema:CreativeWork
244 https://doi.org/10.1016/j.amjcard.2007.03.083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005532182
245 rdf:type schema:CreativeWork
246 https://doi.org/10.1016/j.amjcard.2010.12.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007837026
247 rdf:type schema:CreativeWork
248 https://doi.org/10.1016/j.ehj.2003.10.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054732345
249 rdf:type schema:CreativeWork
250 https://doi.org/10.1016/j.ejcts.2010.07.049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030023578
251 rdf:type schema:CreativeWork
252 https://doi.org/10.1016/j.healun.2010.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050720551
253 rdf:type schema:CreativeWork
254 https://doi.org/10.1016/j.healun.2012.04.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035333909
255 rdf:type schema:CreativeWork
256 https://doi.org/10.1016/j.hfc.2010.12.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021056003
257 rdf:type schema:CreativeWork
258 https://doi.org/10.1016/j.jacc.2006.10.079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028613085
259 rdf:type schema:CreativeWork
260 https://doi.org/10.1016/j.jacc.2007.08.058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009138600
261 rdf:type schema:CreativeWork
262 https://doi.org/10.1016/j.jacc.2008.10.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043991259
263 rdf:type schema:CreativeWork
264 https://doi.org/10.1016/j.jacc.2009.12.066 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010295678
265 rdf:type schema:CreativeWork
266 https://doi.org/10.1016/j.jacc.2010.01.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038380674
267 rdf:type schema:CreativeWork
268 https://doi.org/10.1016/j.jacc.2010.10.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002537813
269 rdf:type schema:CreativeWork
270 https://doi.org/10.1016/j.jcmg.2010.10.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006921344
271 rdf:type schema:CreativeWork
272 https://doi.org/10.1055/s-0029-1240687 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057206965
273 rdf:type schema:CreativeWork
274 https://doi.org/10.1056/nejm198207223070401 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000155380
275 rdf:type schema:CreativeWork
276 https://doi.org/10.1056/nejm198409273111303 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048410078
277 rdf:type schema:CreativeWork
278 https://doi.org/10.1056/nejmoa055373 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021227277
279 rdf:type schema:CreativeWork
280 https://doi.org/10.1093/eurheartj/ehn113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045470525
281 rdf:type schema:CreativeWork
282 https://doi.org/10.1093/oxfordjournals.aje.a113284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1082126726
283 rdf:type schema:CreativeWork
284 https://doi.org/10.1097/ede.0b013e3181c30fb2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023301544
285 rdf:type schema:CreativeWork
286 https://doi.org/10.1097/mat.0b013e31824450f9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015644420
287 rdf:type schema:CreativeWork
288 https://doi.org/10.1136/hrt.2010.204149 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009802228
289 rdf:type schema:CreativeWork
290 https://doi.org/10.1161/01.cir.73.4.615 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036132319
291 rdf:type schema:CreativeWork
292 https://doi.org/10.1161/cir.0b013e31823ac046 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051424218
293 rdf:type schema:CreativeWork
294 https://doi.org/10.1161/circheartfailure.110.958223 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053476118
295 rdf:type schema:CreativeWork
296 https://doi.org/10.1161/circimaging.108.782433 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052558953
297 rdf:type schema:CreativeWork
298 https://doi.org/10.1161/circulationaha.105.584102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008451871
299 rdf:type schema:CreativeWork
300 https://doi.org/10.1161/circulationaha.106.672402 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006648149
301 rdf:type schema:CreativeWork
302 https://doi.org/10.1161/circulationaha.106.687103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028760692
303 rdf:type schema:CreativeWork
304 https://doi.org/10.1161/circulationaha.108.816884 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012327179
305 rdf:type schema:CreativeWork
306 https://doi.org/10.1161/circulationaha.109.858076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035091529
307 rdf:type schema:CreativeWork
308 https://www.grid.ac/institutes/grid.239578.2 schema:alternateName Cleveland Clinic
309 schema:name Imaging and Heart and Vascular Institutes, Cleveland Clinic, Cleveland, OH, USA
310 rdf:type schema:Organization
311 https://www.grid.ac/institutes/grid.34477.33 schema:alternateName University of Washington
312 schema:name Division of Cardiology, University of Washington, 1959 NE Pacific Street, P.O. Box 356422, Seattle, WA, USA
313 rdf:type schema:Organization
314 https://www.grid.ac/institutes/grid.411149.8 schema:alternateName Centre Hospitalier Universitaire de Caen
315 schema:name Centre Hospitalier Universitaire Cote de Nacre, Caen, France
316 rdf:type schema:Organization
317 https://www.grid.ac/institutes/grid.415304.7 schema:alternateName Long Beach Memorial Medical Center
318 schema:name Long Beach Memorial Medical Center, Long Beach, CA, USA
319 rdf:type schema:Organization
320 https://www.grid.ac/institutes/grid.474545.3 schema:alternateName GE Healthcare (United States)
321 schema:name GE Healthcare, Princeton, NJ, USA
322 rdf:type schema:Organization
323 https://www.grid.ac/institutes/grid.59734.3c schema:alternateName Icahn School of Medicine at Mount Sinai
324 schema:name Division of Cardiology, Mount Sinai School of Medicine, New York, NY, USA
325 rdf:type schema:Organization
326 https://www.grid.ac/institutes/grid.7445.2 schema:alternateName Imperial College London
327 schema:name BRU, Royal Brompton Hospital, Imperial College, London and Northwick Park Hospital, Harrow, United Kingdom
328 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...