Skip to main content

Advertisement

SpringerLink
Log in

Usefulness of the 2-year iodine-123 metaiodobenzylguanidine-based risk model for post-discharge risk stratification of patients with acute decompensated heart failure

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

A four-parameter risk model that included cardiac iodine-123 metaiodobenzylguanidine (MIBG) imaging and readily available clinical parameters was recently developed for prediction of 2-year cardiac mortality risk in patients with chronic heart failure. We sought to validate the ability of this risk model to predict post-discharge clinical outcomes in patients with acute decompensated heart failure (ADHF) and to compare its prognostic value with that of the Acute Decompensated Heart Failure National Registry (ADHERE) and Get With The Guidelines-Heart Failure (GWTG-HF) risk scores.

Methods

We studied 407 consecutive patients who were admitted for ADHF and survived to discharge, with definitive 2-year outcomes (death or survival). Cardiac MIBG imaging was performed just before discharge. The 2-year cardiac mortality risk was calculated using four parameters, namely age, left ventricular ejection fraction, New York Heart Association functional class, and cardiac MIBG heart-to-mediastinum ratio on delayed images. Patients were stratified into three groups based on the 2-year cardiac mortality risk: low- (< 4%), intermediate- (4–12%), and high-risk (> 12%) groups. The ADHERE and GWTG-HF risk scores were also calculated.

Results

There was a significant difference in the incidence of cardiac death among the three groups stratified using the 2-year cardiac mortality risk model (p < 0.0001). The 2-year cardiac mortality risk model had a higher C-statistic (0.732) for the prediction of cardiac mortality than the ADHERE and GWTG-HF risk scores.

Conclusion

The 2-year MIBG-based cardiac mortality risk model is useful for predicting post-discharge clinical outcomes in patients with ADHF.

Trial registration number

UMIN000015246, 25 September 2014.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this report.

References

  1. Chang PP, Wruck LM, Shahar E, Rossi JS, Loehr LR, Russell SD, et al. Trends in hospitalizations and survival of acute decompensated heart failure in four us communities (2005–2014): ARIC Study Community Surveillance. Circulation. 2018;138:12–24. https://doi.org/10.1161/CIRCULATIONAHA.117.027551.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Rocha BML, Menezes FL. Acute decompensated heart failure (ADHF): a comprehensive contemporary review on preventing early readmissions and postdischarge death. Int J Cardiol. 2016;223:1035–44. https://doi.org/10.1016/j.ijcard.2016.07.259.

    Article  PubMed  Google Scholar 

  3. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JGF, Coats AJS, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;37:2129–200. https://doi.org/10.1093/eurheartj/ehw128.

  4. Ouwerkerk W, Voors AA, Zwinderman AH. Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patients with heart failure. JACC Heart Fail. 2014;2:429–36. https://doi.org/10.1016/j.jchf.2014.04.006.

    Article  PubMed  Google Scholar 

  5. Rahimi K, Bennett D, Conrad N, Williams TM, Basu J, Dwight J, et al. Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail. 2014;2:440–6. https://doi.org/10.1016/j.jchf.2014.04.008.

    Article  PubMed  Google Scholar 

  6. Miro O, Rossello X, Platz E, Masip J, Gualandro DM, Peacock WF, et al. Risk stratification scores for patients with acute heart failure in the Emergency Department: a systematic review. Eur Heart J Acute Cardiovasc Care. 2020;9:375–98. https://doi.org/10.1177/2048872620930889.

    Article  PubMed  Google Scholar 

  7. Fonarow GC, Adams KF, Jr., Abraham WT, Yancy CW, Boscardin WJ; ADHERE Scientific Advisory Committee, Study Group, and Investigators. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis. JAMA. 2005;293:572–80. https://doi.org/10.1001/jama.293.5.572.

  8. Peterson PN, Rumsfeld JS, Liang L, Albert NM, Hernandez AF, Peterson ED, et al. A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes. 2010;3:25–32. https://doi.org/10.1161/CIRCOUTCOMES.109.854877.

    Article  PubMed  Google Scholar 

  9. Win S, Hussain I, Hebl VB, Dunlay SM, Redfield MM. Inpatient mortality risk scores and postdischarge events in hospitalized heart failure patients: a community-based study. Circ Heart Fail. 2017;10. https://doi.org/10.1161/CIRCHEARTFAILURE.117.003926.

  10. Tamaki S, Yamada T, Okuyama Y, Morita T, Sanada S, Tsukamoto Y, et al. Cardiac iodine-123 metaiodobenzylguanidine imaging predicts sudden cardiac death independently of left ventricular ejection fraction in patients with chronic heart failure and left ventricular systolic dysfunction: results from a comparative study with signal-averaged electrocardiogram, heart rate variability, and QT dispersion. J Am Coll Cardiol. 2009;53:426–35. https://doi.org/10.1016/j.jacc.2008.10.025.

    Article  CAS  PubMed  Google Scholar 

  11. Jacobson AF, Senior R, Cerqueira MD, Wong ND, Thomas GS, Lopez VA, et al. Myocardial iodine-123 meta-iodobenzylguanidine imaging and cardiac events in heart failure. Results of the prospective ADMIRE-HF (AdreView Myocardial Imaging for Risk Evaluation in Heart Failure) study. J Am Coll Cardiol. 2010;55:2212–21. https://doi.org/10.1016/j.jacc.2010.01.014.

  12. Kasama S, Toyama T, Kaneko Y, Iwasaki T, Sumino H, Kumakura H, et al. Relationship between late ventricular potentials and myocardial 123I-metaiodobenzylguanidine scintigraphy in patients with dilated cardiomyopathy with mild to moderate heart failure: results of a prospective study of sudden death events. Eur J Nucl Med Mol Imaging. 2012;39:1056–64. https://doi.org/10.1007/s00259-012-2092-1.

    Article  PubMed  Google Scholar 

  13. Parker MW, Sood N, Ahlberg AW, Jacobson AF, Heller GV, Lundbye JB. Relationship between quantitative cardiac neuronal imaging with 123I-meta-iodobenzylguanidine and hospitalization in patients with heart failure. Eur J Nucl Med Mol Imaging. 2014;41:1666–72. https://doi.org/10.1007/s00259-014-2819-2.

    Article  CAS  PubMed  Google Scholar 

  14. Seo M, Yamada T, Tamaki S, Watanabe T, Morita T, Furukawa Y, et al. Prognostic significance of cardiac I-123-metaiodobenzylguanidine imaging in patients with reduced, mid-range, and preserved left ventricular ejection fraction admitted for acute decompensated heart failure: a prospective study in Osaka Prefectural Acute Heart Failure Registry (OPAR). Eur Heart J Cardiovasc Imaging. 2021;22:58–66. https://doi.org/10.1093/ehjci/jeaa025.

    Article  PubMed  Google Scholar 

  15. Nakajima K, Nakata T, Matsuo S, Jacobson AF. Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models. Eur Heart J Cardiovasc Imaging. 2016;17:1138–45. https://doi.org/10.1093/ehjci/jev322.

    Article  PubMed  Google Scholar 

  16. Nakajima K, Nakata T, Doi T, Kadokami T, Matsuo S, Konno T, et al. Validation of 2-year 123I-meta-iodobenzylguanidine-based cardiac mortality risk model in chronic heart failure. Eur Heart J Cardiovasc Imaging. 2018;19:749–56. https://doi.org/10.1093/ehjci/jey016.

  17. McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of congestive heart failure: the Framingham study. N Engl J Med. 1971;285:1441–6. https://doi.org/10.1056/NEJM197112232852601.

    Article  CAS  PubMed  Google Scholar 

  18. Kondo T, Yamada T, Tamaki S, Morita T, Furukawa Y, Iwasaki Y, et al. Serial change in serum chloride during hospitalization could predict heart failure death in acute decompensated heart failure patients. Circ J. 2018;82:1041–50. https://doi.org/10.1253/circj.CJ-17-0938.

    Article  CAS  PubMed  Google Scholar 

  19. Tamaki S, Mano T, Sakata Y, Ohtani T, Takeda Y, Kamimura D, et al. Interleukin-16 promotes cardiac fibrosis and myocardial stiffening in heart failure with preserved ejection fraction. PLoS ONE. 2013;8: e68893. https://doi.org/10.1371/journal.pone.0068893.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53:982–92. https://doi.org/10.1053/j.ajkd.2008.12.034.

    Article  CAS  PubMed  Google Scholar 

  21. Ogita H, Shimonagata T, Fukunami M, Kumagai K, Yamada T, Asano Y, et al. Prognostic significance of cardiac 123I metaiodobenzylguanidine imaging for mortality and morbidity in patients with chronic heart failure: a prospective study. Heart. 2001;86:656–60. https://doi.org/10.1136/heart.86.6.656.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nakajima K, Okuda K, Yoshimura M, Matsuo S, Wakabayashi H, Imanishi Y, et al. Multicenter cross-calibration of I-123 metaiodobenzylguanidine heart-to-mediastinum ratios to overcome camera-collimator variations. J Nucl Cardiol. 2014;21:970–8. https://doi.org/10.1007/s12350-014-9916-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Verschure DO, Poel E, Nakajima K, Okuda K, van Eck-Smit BLF, Somsen GA, et al. A European myocardial 123I-mIBG cross-calibration phantom study. J Nucl Cardiol. 2018;25:1191–7. https://doi.org/10.1007/s12350-017-0782-6.

    Article  PubMed  Google Scholar 

  24. van der Veen BJ, Al Younis I, de Roos A, Stokkel MP. Assessment of global cardiac I-123 MIBG uptake and washout using volumetric quantification of SPECT acquisitions. J Nucl Cardiol. 2012;19:752–62. https://doi.org/10.1007/s12350-012-9539-4.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Albert NM, Barnason S, Deswal A, Hernandez A, Kociol R, Lee E, et al. Transitions of care in heart failure: a scientific statement from the American Heart Association. Circ Heart Fail. 2015;8:384–409. https://doi.org/10.1161/HHF.0000000000000006.

    Article  PubMed  Google Scholar 

  26. Kamiya K, Sato Y, Takahashi T, Tsuchihashi-Makaya M, Kotooka N, Ikegame T, et al. Multidisciplinary cardiac rehabilitation and long-term prognosis in patients with heart failure. Circ Heart Fail. 2020;13: e006798. https://doi.org/10.1161/CIRCHEARTFAILURE.119.006798.

    Article  PubMed  Google Scholar 

  27. Diop MS, Rudolph JL, Zimmerman KM, Richter MA, Skarf LM. Palliative care interventions for patients with heart failure: a systematic review and meta-analysis. J Palliat Med. 2017;20:84–92. https://doi.org/10.1089/jpm.2016.0330.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pocock SJ, Wang D, Pfeffer MA, Yusuf S, McMurray JJ, Swedberg KB, et al. Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J. 2006;27:65–75. https://doi.org/10.1093/eurheartj/ehi555.

    Article  PubMed  Google Scholar 

  29. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation. 2006;113:1424–33. https://doi.org/10.1161/CIRCULATIONAHA.105.584102.

    Article  PubMed  Google Scholar 

  30. Giamouzis G, Kalogeropoulos A, Georgiopoulou V, Laskar S, Smith AL, Dunbar S, et al. Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail. 2011;17:54–75. https://doi.org/10.1016/j.cardfail.2010.08.010.

    Article  PubMed  Google Scholar 

  31. Kurata C, Uehara A, Sugi T, Ishikawa A, Fujita K, Yonemura K, et al. Cardiac autonomic neuropathy in patients with chronic renal failure on hemodialysis. Nephron. 2000;84:312–9. https://doi.org/10.1159/000045605.

    Article  CAS  PubMed  Google Scholar 

  32. Rengo G, Pagano G, Vitale DF, Formisano R, Komici K, Petraglia L, et al. Impact of aging on cardiac sympathetic innervation measured by 123I-mIBG imaging in patients with systolic heart failure. Eur J Nucl Med Mol Imaging. 2016;43:2392–400. https://doi.org/10.1007/s00259-016-3432-3.

    Article  CAS  PubMed  Google Scholar 

  33. Fernandez-Gasso L, Hernando-Arizaleta L, Palomar-Rodriguez JA, Abellan-Perez MV, Pascual-Figal DA. Trends, causes and timing of 30-day readmissions after hospitalization for heart failure: 11-year population-based analysis with linked data. Int J Cardiol. 2017;248:246–51. https://doi.org/10.1016/j.ijcard.2017.07.094.

    Article  PubMed  Google Scholar 

  34. Kitakata H, Kohno T, Kohsaka S, Shiraishi Y, Parizo JT, Niimi N, et al. Prognostic implications of early and midrange readmissions after acute heart failure hospitalizations: a report from a Japanese multicenter registry. J Am Heart Assoc. 2020;9: e014949. https://doi.org/10.1161/JAHA.119.014949.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kasama S, Toyama T, Sumino H, Nakazawa M, Matsumoto N, Sato Y, et al. Prognostic value of serial cardiac 123I-MIBG imaging in patients with stabilized chronic heart failure and reduced left ventricular ejection fraction. J Nucl Med. 2008;49:907–14. https://doi.org/10.2967/jnumed.107.047548.

    Article  PubMed  Google Scholar 

  36. Cintron G, Johnson G, Francis G, Cobb F, Cohn JN. Prognostic significance of serial changes in left ventricular ejection fraction in patients with congestive heart failure. The V-HeFT VA Cooperative Studies Group. Circulation. 1993;87:VI17–23.

  37. Kasama S, Toyama T, Funada R, Takama N, Koitabashi N, Ichikawa S, et al. Effects of adding intravenous nicorandil to standard therapy on cardiac sympathetic nerve activity and myocyte dysfunction in patients with acute decompensated heart failure. Eur J Nucl Med Mol Imaging. 2015;42:761–70. https://doi.org/10.1007/s00259-015-2990-0.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Cardiology, Osaka General Medical Centre, 3-1-56, Mandai-Higashi, Sumiyoshi-ku, Osaka, 558-8558, Japan

    Shunsuke Tamaki, Takahisa Yamada, Tetsuya Watanabe, Takashi Morita, Masato Kawasaki, Atsushi Kikuchi, Tsutomu Kawai, Masahiro Seo, Jun Nakamura, Kiyomi Kayama, Daisuke Sakamoto, Kumpei Ueda, Takehiro Kogame, Yuto Tamura, Takeshi Fujita, Keisuke Nishigaki, Yuto Fukuda, Yuki Kokubu & Masatake Fukunami

Authors
  1. Shunsuke Tamaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takahisa Yamada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tetsuya Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takashi Morita
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masato Kawasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Atsushi Kikuchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tsutomu Kawai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Masahiro Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jun Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kiyomi Kayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Daisuke Sakamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Kumpei Ueda
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Takehiro Kogame
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yuto Tamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Takeshi Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Keisuke Nishigaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yuto Fukuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yuki Kokubu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Masatake Fukunami
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Shunsuke Tamaki. The first draft of the manuscript was written by Shunsuke Tamaki, Takahisa Yamada, Tetsuya Watanabe, and Masatake Fukunami, and all the authors commented on earlier versions of the manuscript. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Shunsuke Tamaki.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. The Institutional Ethics Committee approved the study protocol.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

Patients signed informed consent for publication of their data.

Additional information

Publisher's note

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

This work was presented in part as a poster presentation at the ESC congress 2019 in Paris, France.

This article is part of the Topical Collection on Cardiology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamaki, S., Yamada, T., Watanabe, T. et al. Usefulness of the 2-year iodine-123 metaiodobenzylguanidine-based risk model for post-discharge risk stratification of patients with acute decompensated heart failure. Eur J Nucl Med Mol Imaging 49, 1906–1917 (2022). https://doi.org/10.1007/s00259-021-05663-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-021-05663-y

Keywords

Search

Navigation