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A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support

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Abstract

Purpose

Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients’ response to CRT.

Methods

This analysis included 153 patients who met criteria for CRT from a prospective cohort study. The variables were used to model predictive methods for CRT. Patients were classified as “responders” for an increase of LVEF ≥ 5% at follow-up. In a second analysis, patients were classified as “super-responders” for an increase of LVEF ≥ 15%. For ML, variable selection was applied, and Prediction Analysis of Microarrays (PAM) approach was used to model response while Naïve Bayes (NB) was used to model super-response. These ML models were compared to models obtained with guideline variables.

Results

PAM had AUC of 0.80 against 0.72 of partial least squares-discriminant analysis with guideline variables (p = 0.52). The sensitivity (0.86) and specificity (0.75) were better than for guideline alone, sensitivity (0.75) and specificity (0.24). Neural network with guideline variables was better than NB (AUC = 0.93 vs. 0.87) however without statistical significance (p = 0.48). Its sensitivity and specificity (1.0 and 0.75, respectively) were better than guideline alone (0.78 and 0.25, respectively).

Conclusions

Compared to guideline criteria, ML methods trended toward improved CRT response and super-response prediction. GMPS was central in the acquisition of most parameters. Further studies are needed to validate the models.

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Abbreviations

CABG:

Coronary artery bypass graft

CAD:

Coronary artery disease

CRT:

Cardiac resynchronization therapy

ECTb4:

Emory Cardiac Toolbox Version 4.0

ECG:

Electrocardiogram

ESV:

Left ventricular end systolic volume

GMPS:

Gated myocardial perfusion SPECT

HF:

Heart failure

IAEA:

International Atomic Energy Agency

LBBB:

Left bundle branch block

LV:

Left ventricle

LVEF:

Left ventricular ejection fraction

MI:

Myocardial infarction

ML:

Machine learning

NYHA class:

New York Heart Association Class

OSEM:

Ordered subset expectation maximization

PCI:

Percutaneous coronary intervention

PSD:

Left ventricular phase histogram standard deviation

SPECT:

Single photon emission tomography

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Acknowledgements

The authors thank the Vision CRT researchers for sharing the data and collaborating: Amelia Jimenez-Heffernan, Sadaf Butt, Claudio T. Mesquita, Teresa Massardo, Amalia Peix, Alka Kumar, Chetan Patel, Erick Alexanderson, Luz M. Pabon, Ganesan Karthikeyan, Claudia Gutierrez, Ernest Garcia, and Diana Paez.

Funding

This study presents the results derived from the International Atomic Energy Agency (IAEA) multicenter trial: “Value of intraventricular synchronism assessment by gated-SPECT myocardial perfusion imaging in the management of heart failure patients submitted to cardiac resynchronization therapy” (IAEA VISION-CRT), Coordinated Research Protocol E1.30.34, and received funds from IAEA. CTM receives grants from CNPq and FAPERJ. It was supported in part by a grant from The American Heart Association (Project Number: 17AIREA33700016, PI: Weihua Zhou) and by Michigan Technological University Undergraduate Research Internship Program (PI: Kristoffer Larsen).

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Author notes

  1. Fernando de Amorim Fernandes and Kristoffer Larsen contributed equally to this work.

Authors and Affiliations

  1. Nuclear Medicine Department, Hospital Universitario Antonio Pedro-EBSERH-UFF, 303 Marquês de Parana Street, Niteroi, Rio de Janeiro, 24033-900, Brazil

    Fernando de A. Fernandes & Claudio T. Mesquita

  2. Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA

    Kristoffer Larsen & Qiuying Sha

  3. Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA

    Zhuo He & Weihua Zhou

  4. Cardiology Department, Hospital Universitario Antonio Pedro-EBSERH-UFF, Niteroi, Brazil

    Erivelton Nascimento

  5. Nuclear Medicine Department, Institute of Cardiology, La Habana, Cuba

    Amalia Peix

  6. Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria

    Diana Paez

  7. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA

    Ernest V. Garcia

  8. Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA

    Weihua Zhou

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  1. Fernando de A. Fernandes
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Corresponding authors

Correspondence to Fernando de A. Fernandes or Weihua Zhou.

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Ethical approval

The study was approved by the participant countries’ scientific councils and complies with the Declaration of Helsinki. Written informed consent was obtained from all participants and patient anonymity was maintained during data analysis. In addition, we would like to state that (1) the paper is not under consideration elsewhere, (2) none of the paper’s contents have been previously published, (3) all authors have read and approved the manuscript, and that (4) the paper has been published as a preprint in arXiv and is available at http://arxiv.org/abs/2211.07472.

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de A. Fernandes, F., Larsen, K., He, Z. et al. A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support. Eur J Nucl Med Mol Imaging 50, 3022–3033 (2023). https://doi.org/10.1007/s00259-023-06259-4

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