Key result
Autoencoder-derived LV dyssynchrony on SPECT MPI predicts CRT response better than conventional parameters.
Why the study?
Conventional left ventricular mechanical dyssynchrony parameters have statistical limitations, prompting the extraction of new parameters using deep learning to assist CRT patient selection.
Do autoencoder-based LVMD parameters extracted from gated SPECT MPI improve the prediction of CRT response compared to conventional parameters in patients undergoing CRT?
Cohort (n=103)
Yes
Do autoencoder-based LVMD parameters extracted from gated SPECT MPI improve the prediction of CRT response compared to conventional parameters in patients undergoing CRT?
Effect estimate: OR 1.03 (95% CI 1.01-1.06)
p-value: p=0.006
Deep learning-derived LVMD parameters from baseline gated SPECT MPI provide incremental value over conventional parameters for predicting CRT response.
May aid CRT response prediction via SPECT; leaves open prospective validation before practice change.
Background. Studies have shown that the conventional left ventricular mechanical dyssynchrony (LVMD) parameters have their own statistical limitations. The purpose of this study is to extract new LVMD parameters from the phase analysis of gated SPECT MPI by deep learning to help CRT patient selection. Methods. One hundred and three patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as a decrease in left ventricular end-systolic volume (LVESV) >= 15% at 6 +- 1 month follow up. Autoencoder (AE), an unsupervised deep learning method, was trained by the raw LV systolic phase polar maps to extract new LVMD parameters, called AE-based LVMD parameters. Correlation analysis was used to explain the relationships between new parameters with conventional LVMD parameters. Univariate and multivariate analyses were used to establish a multivariate model for predicting CRT response. Results. Complete data were obtained in 102 patients, 44.1% of them were classified as CRT responders. AE-based LVMD parameter was significant in the univariate (OR 1.24, 95% CI 1.07 - 1.44, P = 0.006) and multivariate analyses (OR 1.03, 95% CI 1.01 - 1.06, P = 0.006). Moreover, it had incremental value over PSD (AUC 0.72 vs. 0.63, LH 8.06, P = 0.005) and PBW (AUC 0.72 vs. 0.64, LH 7.87, P = 0.005), combined with significant clinic characteristics, including LVEF and gender. Conclusions. The new LVMD parameters extracted by autoencoder from the baseline gated SPECT MPI has the potential to improve the prediction of CRT response.
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He et al. (2021) conducted a cohort in Heart failure requiring cardiac resynchronization therapy (n=103). Autoencoder-based LVMD parameters vs. Conventional LVMD parameters (PSD and PBW) was evaluated on CRT response (decrease in LVESV ≥ 15%) (OR 1.03, 95% CI 1.01-1.06, p=0.006). An autoencoder-based left ventricular mechanical dyssynchrony parameter from baseline gated SPECT MPI significantly predicted CRT response (OR 1.03) with incremental value over conventional parameters.
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