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Cardiac resynchronization therapy (CRT) has been established as an importanttherapy for heart failure. Mechanical dyssynchrony has the potential to predictresponders to CRT. The aim of this study was to report the development and thevalidation of machine learning (ML) models which integrates ECG, gated SPECTMPI (GMPS) and clinical variables to predict patients' response to CRT. Thisanalysis included 153 patients who met criteria for CRT from a prospectivecohort study. The variables were used to modeling predictive methods for CRT.Patients were classified as responders for an increase of LVEF>=5% atfollow-up. In a second analysis, patients were classified super-responders forincrease of LVEF>=15%. For ML, variable selection was applied, and PredictionAnalysis of Microarrays (PAM) approach was used for response modeling whileNaive Bayes (NB) was used for super-response. They were compared to modelsobtained with guideline variables. PAM had AUC of 0.80 against 0.71 of logisticregression with guideline variables (p = 0.47). The sensitivity (0.86) andspecificity (0.75) were better than for guideline alone, sensitivity (0.72) andspecificity (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 CRTresponse and super-response prediction. GMPS had a central role in theacquisition of most parameters. Further studies are needed to validate themodels.

european journal of nuclear medicine and molecular imaging

A new method using machine learning to integrate ECG and gated SPECT MPI  for Cardiac Resynchronization Therapy Decision Support on behalf of the  VISION-CRT