2019
DOI: 10.1161/circep.119.007316
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy

Abstract: Background-Cardiac resynchronization therapy (CRT) has significant non-response rates. We assessed whether machine learning could predict CRT response beyond current guidelines. Methods-We analyzed CRT patients from Cleveland Clinic and Johns Hopkins. A training cohort was created from all Johns Hopkins patients and an equal number of randomly sampled Cleveland Clinic patients. All remaining patients comprised the testing cohort. Response was defined as ≥10% increase in left ventricular (LV) ejection fraction.… Show more

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Cited by 94 publications
(86 citation statements)
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References 34 publications
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“…ML can improve the care of HF patients in various ways, e.g., by augmenting the prediction of readmission after HF hospitalization or by predicting the risk of mortality (16,17,19). In HF patients undergoing CRT implantation, our research group has previously confirmed the superiority of ML over pre-existing risk scores (24), and similar results have been reported by others as well (25,26). Underpinning these findings, we were able to predict the 1-and 3-year mortality of CRT patients with good discrimination and excellent calibration, even in subsets of patients divided by sex.…”
Section: Risk Stratification Of Hf Patients Using MLsupporting
confidence: 81%
“…ML can improve the care of HF patients in various ways, e.g., by augmenting the prediction of readmission after HF hospitalization or by predicting the risk of mortality (16,17,19). In HF patients undergoing CRT implantation, our research group has previously confirmed the superiority of ML over pre-existing risk scores (24), and similar results have been reported by others as well (25,26). Underpinning these findings, we were able to predict the 1-and 3-year mortality of CRT patients with good discrimination and excellent calibration, even in subsets of patients divided by sex.…”
Section: Risk Stratification Of Hf Patients Using MLsupporting
confidence: 81%
“…The predictive value of current class 1 indications for CRT response is modest and little improved by machine learning, with area under the curve (AUC) of 0.65 and 0.70, respectively. 19 This is similar for qLV >95 ms or RVs/LVs, which correlates with qLV. 20 However, these criteria omit measures of successful delivery.…”
Section: Discussionmentioning
confidence: 67%
“…Steroid therapy, when started simultaneously with CRT, might have altered the clinical course in this patient. Recently, machine learning using the patients' baseline characteristics of the patients has been applied to the prediction of response to CRT, but the results are not necessarily very remarkable to date [43,44]. A composite evaluation of machine learning scores and simulation results would potentiate the power of these two approaches.…”
Section: Discussionmentioning
confidence: 99%