  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 (ML) models which integrates ECG, gated SPECT
MPI (GMPS) and clinical variables to predict patients' response to CRT. This
analysis included 153 patients who met criteria for CRT from a prospective
cohort study. The variables were used to modeling 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 super-responders for
increase of LVEF>=15%. For ML, variable selection was applied, and Prediction
Analysis of Microarrays (PAM) approach was used for response modeling while
Naive Bayes (NB) was used for super-response. They were compared to models
obtained with guideline variables. PAM had AUC of 0.80 against 0.71 of logistic
regression with guideline variables (p = 0.47). The sensitivity (0.86) and
specificity (0.75) were better than for guideline alone, sensitivity (0.72) and
specificity (0.22). Neural network with guideline variables outperformed NB
(AUC = 0.87 vs 0.86; p = 0.88). Its sensitivity and specificity (1.0 and 0.75,
respectively) was better than guideline alone (0.40 and 0.06, respectively).
Compared to guideline criteria, ML methods trended towards improved CRT
response and super-response prediction. GMPS had a central role in the
acquisition of most parameters. Further studies are needed to validate the
models.
