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Myocardial flow reserve estimation with contemporary CZT-SPECT and 99mTc-tracers lacks precision for routine clinical application

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Journal of Nuclear Cardiology Aims and scope

Abstract

Background

PET myocardial flow reserve (MFR) has established diagnostic and prognostic value. Technological advances have now enabled SPECT MFR quantification. We investigated whether SPECT MFR precision is sufficient for clinical categorization of patients.

Methods

Validation studies vs invasive flow measurements and PET MFR were reviewed to determine global SPECT MFR thresholds. Studies vs PET and a SPECT MFR repeatability study were used to establish imprecision in SPECT MFR measurements as the standard deviation of the difference between SPECT and PET MFR, or test-retest SPECT MFR. Simulations were used to evaluate the impact of SPECT MFR imprecision on confidence of clinically relevant categorization.

Results

Based on validation studies, the typical PET MFR categories were used for SPECT MFR classification (< 1.5, 1.5-2.0, > 2.0). Imprecision vs PET MFR ranged from 0.556 to 0.829, and test-retest imprecision was 0.781-0.878. Simulations showed correct classification of up to only 34% of patients when 1.5 ≤ true MFR ≤ 2.0. Categorization with high confidence (> 80%) was only achieved for extreme MFR values (< 1.0 or > 2.5), with correct classification in only 15% of patients in a typical lab with MFR of 1.8 ± 0.5.

Conclusions

Current SPECT-derived estimates of MFR lack precision and require further optimization for clinical risk stratification.

Chinese Abstract

背景

PET 心肌血流储备 (Myocardial Flow Reserve, MFR)诊断和预后价值已经明确。SPECT MFR 的量化随着技术的进步也已实现。我们探讨 SPECT MFR 精度能否满足对患者进行临床分类。

方法

回顾有创血流测量和PET-MFR对比的研究来确定SPECT的整体MFR阈值。PET和SPECT-MFR重复性研究用于确定SPECT-MFR测量的不精确性, 并以此作为SPECT和PET-MFR之间或SPECT-MFR的重测差异的标准差。模拟用来评估SPECT MFR的不精确性对临床相关分类可信度的影响。

结果

在验证研究中, SPECT-MFR分类采用典型PET-MFR分类 (< 1.5, 1.5-2.0, > 2.0)。对比PET-MFR, 不精确度的比值范围为0.556∼0.829, 重测不精确度为0.781∼0.878。模拟显示, 当 1.5≤真实 MFR≤2.0时, 正确分类的患者只有 34%。 在MFR值为1.8±0.5的典型实验模拟中, 只有15%的患者进行了正确的分类, 只有极端的MFR值 (< 1.0或 >2.5) 才能实现高置信度分类 (>80%)。

结论

目前SPECT估计得出的MFR缺乏精确性, 用于临床风险分层尚需要进一步优化。

French Abstract

Mise en contexte

L’évaluation de la réserve du flot myocardique (RFM) en TEP possède une valeur diagnostique et pronostique reconnue. Des avancées technologiques permettent maintenant une quantification de la RFM à l’aide de la SPECT.

Méthodologie

Des études de validation versus des mesures de flot invasives et de la RFM en TEP ont été analysées afin de déterminer les seuils globaux de la RFM en SPECT. Les études TEP et de répétabilité de la RFM en SPECT ont été utilisées afin d’établir le degré d’imprécision sur les mesures de la RFM en SPECT comme déviation standard de la différence entre la RFM évaluée en SPECT et en TEP, ou évaluation – réévaluation de la RFM en SPECT. Des simulations ont été utilisées afin d’évaluer l’impact de l’imprécision de la RFM en SPECT sur la confiance de catégorisation clinique.

Résultats

Basées sur les études de validation, les catégories de RFM en TEP typiques ont été utilisées pour la classification de la RFM en SPECT (< 1.5, 1.5–2.0, > 2.0). L’imprécision versus RFM en TEP variait de 0.556 à 0.829 et l’imprécision de l’évaluation-réévaluation était de 0.781–0.878. Les simulations ont démontré une classification correcte jusqu’à seulement 34% des patients lorsque la RFM vraie se situait entre 1.5 et 2.0. Une catégorisation avec un haut degré de confiance (> 80%) a été effectuée seulement pour les valeurs extrêmes de RFM (< 1.0 ou > 2.5) avec une classification correcte chez uniquement 15% des patients vus dans un laboratoire typique avec une RFM de 1.8 ± 0.5.

Conclusion

Les estimations de la RFM à partir des SPECT actuels manquent de précision et nécessitent davantage d’optimisation pour la stratification du risque clinique.

Spanish Abstract

Antecedentes

la reserva de flujo miocárdico (RFM) por PET tiene un valor diagnóstico y pronóstico bien establecido. Los avances tecnológicos ahora han permitido la cuantificación RFM por SPECT. Investigamos si la precisión de la RFM por SPECT es suficiente para la categorización clínica de pacientes.

Métodos

se revisaron los estudios de validación versus mediciones invasivas de flujo y RFM por PET para determinar los puntos de cohorte globales de la RFM por SPECT. Estudios versus PET y un estudio de repetibilidad de la RFM por SPECT fueron usados para establecer la imprecisión de las mediciones de RFM por SPECT como la desviación estándar de la diferencia entre la RFM por SPECT y PET, o estudio-repetir estudio de la RFM por SPECT. Se utilizaron simulaciones para evaluar el impacto de la imprecisión en la confianza de la categorización clínicamente relevante por la RFM por SPECT.

Resultados

basado en estudios de validación, las típicas categorías de la RFM por PET se utilizaron para la clasificación de la RFM por SPECT (< 1.5, 1.5–2.0, > 2.0). La imprecisión frente a la RFM por PET osciló entre 0.556 y 0.829, y la imprecisión de estudio-repetir estudio fue de 0.781–0.878. Las simulaciones mostraron una correcta clasificación de hasta solo el 34% de los pacientes cuando 1.5 ≤ RFM real ≤ 2.0. La categorización con alta confianza (> 80%) solo se logró para valores extremos de RFM (< 1.0 o > 2.5), con clasificación correcta en solo el 15% de los pacientes en un laboratorio típico con RFM de 1.8 ± 0.5.

Conclusiones

las estimaciones actuales de MFR derivadas de SPECT carecen de precisión y requieren una mayor optimización para la estratificación del riesgo clínico.

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Abbreviations

CAD:

Coronary artery disease

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

LVEF:

Left ventricular ejection fraction

MBF:

Myocardial blood flow

MFR:

Myocardial flow reserve

MPI:

Myocardial perfusion imaging

NAC:

Non-attenuation corrected

SD:

Standard deviation

SSS:

Summed stress score

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Acknowledgements

JNC thanks Weihua Zhou, PhD for providing the Chinese abstract, Raymond Taillefer, MD for providing the French abstract, and Erick Alexanderson, MD for providing the Spanish abstract.

Disclosures

J.M. Renaud, A. Poitrasson-Riviere, T. Hagio and J.B. Moody are employees of INVIA Medical Imaging Solutions. J.M. Renaud is a consultant for Jubilant DraxImage and receives royalties from sales and licensing of FlowQuant software. E.P. Ficaro is an owner and stockholder of INVIA Medical Imaging Solutions, which produces Corridor4DM, a clinical software package for nuclear cardiology analysis. V.L. Murthy is supported by R01AG059729 from the National Institute on Aging, U01DK123013 from the National Institute of Diabetes and Digestive and Kidney Disease, and R01HL136685 from the National Heart, Lung, and Blood Institute as well as the Melvyn Rubenfire Professorship in Preventive Cardiology. Dr. Murthy has received research grants and speaking honoraria from Siemens Medical Imaging. He serves as a scientific advisor for Ionetix and owns stock options in the same. Dr. Murthy also owns stock in General Electric and Cardinal Health. He has received expert witness payments on behalf of Jubilant DraxImage and a speaking honorarium from 2Quart Medical. Dr. Murthy receives non-financial research support from INVIA Medical Imaging Solutions.

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Author notes

  1. Edward P. Ficaro and Venkatesh L. Murthy have equal contribution as senior co-authors.

Authors and Affiliations

  1. INVIA Medical Imaging Solutions, 3025 Boardwalk Dr., Suite 200, Ann Arbor, MI, 48108, USA

    Jennifer M. Renaud MSc, Alexis Poitrasson-Rivière PhD, Tomoe Hagio PhD, Jonathan B. Moody PhD & Edward P. Ficaro PhD

  2. Frankel Cardiovascular Center, Division of Cardiovascular Medicine (Department of Internal Medicine) and Division of Nuclear Medicine (Department of Radiology), University of Michigan, Ann Arbor, MI, USA

    Liliana Arida-Moody, Edward P. Ficaro PhD & Venkatesh L. Murthy MD, PhD

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  1. Jennifer M. Renaud MSc
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Correspondence to Jennifer M. Renaud MSc.

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Renaud, J.M., Poitrasson-Rivière, A., Hagio, T. et al. Myocardial flow reserve estimation with contemporary CZT-SPECT and 99mTc-tracers lacks precision for routine clinical application. J. Nucl. Cardiol. 29, 2078–2089 (2022). https://doi.org/10.1007/s12350-021-02761-0

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