Skip to main content

Advertisement

SpringerLink
Log in

External validation of the CRAX2MACE model in an Italian cohort of patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging

  • Original Article
  • Published:
Journal of Nuclear Cardiology Aims and scope

Abstract

Background

Prevention and development of diagnostic and therapeutic techniques reduced morbidity and mortality for coronary artery disease (CAD). In this context, the cardiovascular risk assessment for major adverse cardiac events (MACE) at 2-year (CRAX2MACE) model for prediction of 2-year major adverse cardiac events was developed. We performed an external validation of this model.

Methods

We included 1003 patients with suspected CAD undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging at our academic center between March 2015 and April 2019.

Results

Considering the occurrence of MACE (death from any cause, acute myocardial infarction, or late coronary revascularization), for the CRAX2MACE model the area under the receiver operating characteristic curve was 0.612 and the Brier score was 0.061. The Hosmer–Lemeshow test estimated a non-optimal fit (χ2 28, P < .001). Considering only hard events (cardiac death, acute myocardial infarction), the external validation of the CRAX2MACE model revealed a Brier score of 0.053 and an area under the receiver operating characteristic curve of 0.621. Hosmer–Lemeshow test was calculated by deciles and showed a poor fit (χ2 31, P < .001).

Conclusion

CRAX2MACE model had a limited value for predicting 2-year major adverse cardiovascular events in an external validation cohort of patients with suspected CAD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2

Similar content being viewed by others

Abbreviations

MPS:

Single-photon emission computed tomography myocardial perfusion imaging

CAD:

Coronary artery disease

MACE:

Major adverse cardiac events

LV:

Left ventricular

TPD:

Total perfusion defect

TID:

Transient ischemic dilatation

CRAX2MACE:

Cardiovascular risk assessment for MACE at 2 years

IQR:

Interquartile range

CI:

Confidence interval

AUROC:

Area under receiver operating characteristic

References

  1. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 2016;37:2315‐81.

    Article  Google Scholar 

  2. Mensah GA, Wei GS, Sorlie PD, Fine LJ, Rosenberg Y, Kaufmann PG, et al Decline in cardiovascular mortality: Possible causes and implications. Circ Res 2017;120:366‐80.

    Article  CAS  Google Scholar 

  3. Rozanski A, Gransar H, Hayes SW, Min J, Friedman JD, Thomson LE, et al Temporal trends in the frequency of inducible myocardial ischemia during cardiac stress testing: 1991 to 2009. J Am Coll Cardiol 2013;61:1054‐65.

    Article  Google Scholar 

  4. Duvall WL, Rai M, Ahlberg AW, O’Sullivan DM, Henzlova MJ. A multi-center assessment of the temporal trends in myocardial perfusion imaging. J Nucl Cardiol 2015;22:539‐51.

    Article  Google Scholar 

  5. Thompson RC, Allam AH. More risk factors, less ischemia, and the relevance of MPI testing. J Nucl Cardiol 2015;22:552‐4.

    Article  Google Scholar 

  6. Jouni H, Askew JW, Crusan DJ, Miller TD, Gibbons RJ. Temporal trends of single-photon emission computed tomography myocardial perfusion imaging in patients without prior coronary artery disease: A 22-year experience at a tertiary academic medical center. Am Heart J 2016;176:127‐33.

    Article  Google Scholar 

  7. Jouni H, Askew JW, Crusan DJ, Miller TD, Gibbons RJ. Temporal trends of single-photon emission computed tomography myocardial perfusion imaging in patients with coronary artery disease: A 22-year experience from a tertiary academic medical center. Circ Cardiovasc Imaging 2017;10:e005628.

    Article  Google Scholar 

  8. Megna R, Zampella E, Assante R, Nappi C, Gaudieri V, Mannarino T, et al Temporal trends of abnormal myocardial perfusion imaging in a cohort of Italian subjects: Relation with cardiovascular risk factors. J Nucl Cardiol 2020;27:2167‐77.

    Article  Google Scholar 

  9. Leslie WD, Bryanton M, Goertzen A, Slomka P. Prediction of 2-year major adverse cardiac events from myocardial perfusion scintigraphy and clinical risk factors. J Nucl Cardiol 2021. https://doi.org/10.1007/s12350-021-02617-7.

    Article  Google Scholar 

  10. Megna R, Petretta M, Alfano B, Cantoni V, Green R, Daniele S, et al A new relational database including clinical data and myocardial perfusion imaging findings in coronary artery disease. Curr Med Imaging Rev. 2019;15:661‐71.

    Article  Google Scholar 

  11. Verberne HJ, Acampa W, Anagnostopoulos C, Ballinger J, Bengel F, De Bondt P, et al EANM procedural guidelines for radionuclide myocardial perfusion imaging with SPECT and SPECT/CT: 2015 revision. Eur J Nucl Med Mol Imaging 2015;2015:1929‐40.

    Article  Google Scholar 

  12. Berman DS, Abidov A, Kang X, Hayes SW, Friedman JD, Sciammarella MG, et al Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion SPECT interpretation. J Nucl Cardiol 2004;11:414‐23.

    Article  Google Scholar 

  13. Martineau P, Slomka P, Goertzen A, Leslie WD. CRAX: A simple cardiovascular risk assessment tool to predict risk of acute myocardial infarction or death. J Nucl Cardiol 2018;27:2365‐74.

    Article  Google Scholar 

  14. Hosmer DW, Lemesbow S. Goodness of fit tests for the multiple logistic regression model. Commun Stat Theory Methods 1980;9:1043‐69.

    Article  Google Scholar 

  15. Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med 1979;300:1350‐8.

    Article  CAS  Google Scholar 

  16. Gibbons RJ, Miller TD. Declining accuracy of the traditional Diamond-Forrester estimates of pretest probability of coronary artery disease: Time for new methods. JAMA Intern Med 2021;181:579‐80.

    Article  Google Scholar 

  17. Genders TS, Steyerberg EW, Alkadhi H, Leschka S, Desbiolles L, Nieman K, et al A clinical prediction rule for the diagnosis of coronary artery disease: Validation, updating, and extension. Eur Heart J 2011;32:1316‐30.

    Article  Google Scholar 

  18. Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, et al Prediction model to estimate presence of coronary artery disease: Retrospective pooled analysis of existing cohorts. BMJ 2012;344:e3485.

    Article  Google Scholar 

  19. Megna R, Assante R, Zampella E, Gaudieri V, Nappi C, Cuocolo R, et al Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging. J Nucl Cardiol 2019. https://doi.org/10.1007/s12350-019-01941-3.

    Article  Google Scholar 

  20. Reeh J, Therming CB, Heitmann M, Højberg S, Sørum C, Bech J, et al Prediction of obstructive coronary artery disease and prognosis in patients with suspected stable angina. Eur Heart J 2019;40:1426‐35.

    Article  Google Scholar 

  21. Boden WE, O’Rourke RA, Teo KK, Hartigan PM, Maron DJ, Kostuk WJ, et al Optimal medical therapy with or without PCI for stable coronary disease. N Engl J Med 2007;356:1503‐16.

    Article  CAS  Google Scholar 

  22. Stone NJ, Grundy SM. The 2018 AHA/ACC/Multi-Society Cholesterol guidelines: Looking at past, present and future. Prog Cardiovasc Dis 2019;62:375‐83.

    Article  Google Scholar 

  23. Petretta M, Cuocolo A. Comparison of ESC and ACC/AHA guidelines for myocardial revascularization: Are the differences clinically relevant? The European perspective. J Nucl Cardiol 2017;24:1057‐61.

    Article  Google Scholar 

  24. Genders TSS, Coles A, Hoffmann U, Patel MR, Mark DB, Lee KL, et al The external validity of prediction models for the diagnosis of obstructive coronary artery disease in patients with stable chest pain: Insights from the promise trial. JACC Cardiovasc Imaging 2018;11:437‐46.

    Article  Google Scholar 

  25. Ramspek CL, Jager KJ, Dekker FW, Zoccali C, van Diepen M. External validation of prognostic models: What, why, how, when and where? Clin Kidney J 2020;14:49‐58.

    Article  Google Scholar 

Download references

Disclosures

R. Megna, M. Petretta, R. Assante, E. Zampella, C. Nappi, V. Gaudieri, T. Mannarino, R. Green, V. Cantoni R. Green, A. D’Antonio, P. Buongiorno, W. Acampa, and A. Cuocolo declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy

    Rosario Megna PhD, Valeria Gaudieri MD, PhD & Wanda Acampa MD, PhD

  2. IRCCS-SDN, Naples, Italy

    Mario Petretta MD

  3. Department of Advanced Biomedical Sciences, University Federico II, Via Pansini 5, 80131, Naples, Italy

    Roberta Assante MD, PhD, Emilia Zampella MD, PhD, Carmela Nappi MD, PhD, Valeria Gaudieri MD, PhD, Teresa Mannarino MD, Roberta Green PhD, Valeria Cantoni PhD, Pietro Buongiorno MS, Adriana D’Antonio MD, Wanda Acampa MD, PhD & Alberto Cuocolo MD

Authors
  1. Rosario Megna PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario Petretta MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roberta Assante MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emilia Zampella MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Carmela Nappi MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Valeria Gaudieri MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Teresa Mannarino MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Roberta Green PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Valeria Cantoni PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pietro Buongiorno MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Adriana D’Antonio MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Wanda Acampa MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Alberto Cuocolo MD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Cuocolo MD.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors of this article have provided a PowerPoint file, available for download at SpringerLink, which summarizes the contents of the paper and is free for re-use at meetings and presentations. Search for the article DOI on SpringerLink.com.

The authors have also provided an audio summary of the article, which is available to download as ESM, or to listen to via the JNC/ASNC Podcast.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PPTX 246 KB)

Supplementary file2 (MP3 2544 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Megna, R., Petretta, M., Assante, R. et al. External validation of the CRAX2MACE model in an Italian cohort of patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging. J. Nucl. Cardiol. 29, 2967–2973 (2022). https://doi.org/10.1007/s12350-021-02855-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12350-021-02855-9

Keywords

Search

Navigation