Display options
Cite
Doyle M, Pohost GM, Merz CN, et al. Improved diagnosis and prognosis using Decisions Informed by Combining Entities (DICE): results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE). Cardiovasc Diagn Ther. 2013;3(4):216-27doi: 10.3978/j.issn.2223-3652.2013.10.07.
Doyle, M., Pohost, G. M., Merz, C. N., Shaw, L. J., Sopko, G., Rogers, W. J., Sharaf, B. L., Pepine, C. J., Vido-Thompson, D. A., Rayarao, G., Tauxe, L., Kelsey, S. F., Mc Nair, D., & Biederman, R. W. (2013). Improved diagnosis and prognosis using Decisions Informed by Combining Entities (DICE): results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE). Cardiovascular diagnosis and therapy, 3(4), 216-27. https://doi.org/10.3978/j.issn.2223-3652.2013.10.07
Doyle, Mark, et al. "Improved diagnosis and prognosis using Decisions Informed by Combining Entities (DICE): results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE)." Cardiovascular diagnosis and therapy vol. 3,4 (2013): 216-27. doi: https://doi.org/10.3978/j.issn.2223-3652.2013.10.07
Doyle M, Pohost GM, Merz CN, Shaw LJ, Sopko G, Rogers WJ, Sharaf BL, Pepine CJ, Vido-Thompson DA, Rayarao G, Tauxe L, Kelsey SF, Mc Nair D, Biederman RW. Improved diagnosis and prognosis using Decisions Informed by Combining Entities (DICE): results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE). Cardiovasc Diagn Ther. 2013 Dec;3(4):216-27. doi: 10.3978/j.issn.2223-3652.2013.10.07. PMID: 24400205; PMCID: PMC3878119.
Copy Download .nbib Format: NLM
Share it on

Cardiovasc Diagn Ther. 2013 Dec;3(4):216-27. doi: 10.3978/j.issn.2223-3652.2013.10.07.

Improved diagnosis and prognosis using Decisions Informed by Combining Entities (DICE): results from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE).

Cardiovascular diagnosis and therapy

Mark Doyle, Gerald M Pohost, C Noel Bairey Merz, Leslee J Shaw, George Sopko, William J Rogers, Barry L Sharaf, Carl J Pepine, Diane A Vido-Thompson, Geetha Rayarao, Lindsey Tauxe, Sheryl F Kelsey, Douglas Mc Nair, Robert W Biederman

Affiliations

  1. Allegheny General Hospital, Pittsburgh, PA, USA;
  2. Keck Medical Center, University of Southern California, Los Angeles, CA, USA;
  3. Division of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA;
  4. National Heart Lung and Blood Institute, NIH, USA;
  5. The University of Alabama at Birmingham, AL, USA;
  6. Brown University, Providence, RI, USA;
  7. University of Florida, Gainesville, FL, USA;
  8. Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA;
  9. Cerner Math, Kansas City, MO, USA.

PMID: 24400205 PMCID: PMC3878119 DOI: 10.3978/j.issn.2223-3652.2013.10.07

Abstract

OBJECTIVES: To introduce an algorithmic approach to improve the interpretation of myocardial perfusion images in women with suspected myocardial ischemia.

BACKGROUND: Gated single photon emission computed tomography (SPECT) and magnetic resonance (MR) myocardial perfusion imaging (MPI) approaches have relatively poor diagnostic and prognostic value in women with suspected myocardial ischemia. Here we introduce an approach: Decisions Informed by Combining Entities (DICE) that forms a mathematical model utilizing MPI and cardiac dimensions generated by one modality to predict the perfusion status of another modality. The effect of the model is to systematically incorporate cardiac metrics that influence the interpretation of perfusion images, leading to greater consistency in designation of myocardial perfusion status between studies.

METHODS: Women (n=213), with suspected myocardial ischemia, underwent MPI assessment for regional perfusion defects using two modalities: gated SPECT (n=207) and MR imaging (n=203). To determine perfusion status, MR data were evaluated qualitatively and semi-quantitatively while SPECT data were evaluated using conventional clinical criteria. These perfusion status readings were designated "Original". Four regression models were generated to model perfusion status obtained with one modality [e.g., semi-quantitative magnetic resonance imaging (MRI)] against another modality (e.g., SPECT) and a threshold applied (DICE modeling) to designate perfusion status as normal or low. The DICE models included perfusion status, left ventricular (LV) chamber volumes and myocardial wall thickness. Women were followed for 40±16 months for the development of first major adverse cardiovascular event (MACE: CV death, nonfatal myocardial infarction (MI) or hospitalization for congestive heart failure). Original and DICE perfusion status were compared in their ability to detect high-grade coronary artery disease (CAD) and for prediction of MACE.

RESULTS: Adverse events occurred in 25 (12%) women and CAD was present in 34 (16%). In receiver-operator characteristic (ROC) analysis for CAD detection, the average area under the curve (AUC) for DICE vs. Original status was 0.77±0.03 vs. 0.70±0.03, P<0.01. Similarly, in Kaplan-Meier survival analysis the average log-rank statistic was higher for DICE vs. the Original readings (10.6±5.2 vs. 3.0±0.6, P<0.05).

CONCLUSIONS: While two data sets are required to generate the DICE models no knowledge of follow-up results is needed. DICE modeling improved diagnostic and prognostic value vs. the Original interpretation of the myocardial perfusion status.

Keywords: Modeling; diagnosis; imaging; perfusion; prognosis; women

References

  1. J Cardiovasc Magn Reson. 2005;7(2):481-5 - PubMed
  2. Eur Heart J. 2010 Apr;31(7):794-805 - PubMed
  3. Lancet. 2003 Feb 1;361(9355):374-9 - PubMed
  4. J Thorac Dis. 2013 Jun;5(3):357-9 - PubMed
  5. Cardiovasc Diagn Ther. 2013 Sep;3(3):153-60 - PubMed
  6. Cardiovasc Diagn Ther. 2012 Dec;2(4):268-77 - PubMed
  7. Curr Cardiol Rep. 2011 Feb;13(1):77-85 - PubMed
  8. J Cardiovasc Magn Reson. 2003 Jul;5(3):475-85 - PubMed
  9. Circulation. 2003 Aug 12;108(6):647-53 - PubMed
  10. Eur Heart J. 2006 Apr;27(7):846-53 - PubMed
  11. J Am Coll Cardiol. 2009 Oct 6;54(15):1457-66 - PubMed
  12. J Am Coll Cardiol. 2003 Oct 15;42(8):1475-83 - PubMed
  13. Curr Cardiol Rep. 2003 Jan;5(1):83-90 - PubMed
  14. J Cardiovasc Magn Reson. 2006;8(6):773-9 - PubMed
  15. J Nucl Cardiol. 1999 May-Jun;6(3):278-85 - PubMed
  16. Heart. 2007 Nov;93(11):1363-8 - PubMed
  17. Trials. 2009 Jul 29;10:62 - PubMed
  18. Circulation. 1999 Jul 13;100(2):185-92 - PubMed
  19. J Nucl Med. 2006 Jan;47(1):74-82 - PubMed
  20. J Am Coll Cardiol. 1999 Aug;34(2):441-7 - PubMed
  21. JACC Cardiovasc Imaging. 2010 Oct;3(10):1030-6 - PubMed
  22. Int J Cardiol. 2006 Jun 7;110(1):116-8 - PubMed
  23. J Am Coll Cardiol. 2004 Jun 16;43(12):2253-9 - PubMed
  24. Circ Cardiovasc Imaging. 2010 Jan;3(1):32-40 - PubMed
  25. Circulation. 2002 Jan 15;105(2):162-7 - PubMed
  26. Circulation. 2003 Oct 21;108(16):1945-53 - PubMed

Publication Types

Grant support