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. 2023 Aug;50(10):3022-3033.
doi: 10.1007/s00259-023-06259-4. Epub 2023 May 17.

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

Fernando de A Fernandes #  1 , Kristoffer Larsen #  2 , Zhuo He  3 , Erivelton Nascimento  4 , Amalia Peix  5 , Qiuying Sha  2 , Diana Paez  6 , Ernest V Garcia  7 , Weihua Zhou  8   9 , Claudio T Mesquita  10
Affiliations

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

Fernando de A Fernandes et al. Eur J Nucl Med Mol Imaging. 2023 Aug.

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.

Keywords: CRT; Heart failure; Machine learning; SPECT.

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Conflict of interest statement

Conflict of interest The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Modeling pipeline
Fig. 2
Fig. 2
Study flow chart
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Fig. 3
Importance of variables for prediction of CRT response
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Fig. 4
Importance of variables for prediction of CRT super-response
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Fig. 5
Receiver-operating characteristic curves for response to CRT
Fig. 6
Fig. 6
Receiver-operating characteristic curves for super-response to CRT
See this image and copyright information in PMC

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