Publication: Electrocardiographic model to predict cardiac resynchronization therapy response among chronic heart failure patients
Issued Date
2020-10-01
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01252208
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2-s2.0-85092791952
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of the Medical Association of Thailand. Vol.103, No.10 (2020), 1091-1098
Suggested Citation
Pattranee Leelapatana, Porntera Sethalao, Napawan Pornnimitthum, Arisara Suwanagool Electrocardiographic model to predict cardiac resynchronization therapy response among chronic heart failure patients. Journal of the Medical Association of Thailand. Vol.103, No.10 (2020), 1091-1098. doi:10.35755/jmedassocthai.2020.10.11607 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/60054
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Title
Electrocardiographic model to predict cardiac resynchronization therapy response among chronic heart failure patients
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Abstract
© JOURNAL OF THE MEDICAL ASSOCIATION OF THAILAND | 2020. Background: Despite contemporary restrictive clinical and electrocardiographic selection criteria, up to one-third of chronic heart failure patients with implanted cardiac resynchronization therapy (CRT) are non-responders. Previous studies reported that some electrocardiographic patterns, such as the longer the intrinsicoid deflection (ID) in lead I, the higher the R wave amplitude in V6, and other patterns may be helpful for CRT response prediction. Objective: To establish a simplified model using electrocardiographic parameters as predictors of CRT response among chronic heart failure patients. Materials and Methods: Eighty chronic heart failure patients meeting the current guideline recommendation for CRT implantation were enrolled in the present retrospective cohort study. The patients’ clinical and electrocardiographic parameters at the time of CRT implantation and during follow-up were analyzed. The response to CRT was evaluated after six months of implantation, defined as a decrease in the left ventricular end systolic volume (LVESV) of 15% or more or an increase in the left ventricular ejection fraction (LVEF) of 10% or more. Results: During a median follow-up period of 34 months, there were 45 (56.3%) responders. In multivariate analysis, the independent predictors for CRT response were the greater the reduction of the QRS complex duration after implantation (QRS post – QRS pre), the higher the time to ID in the lead I/QRS ratio (ID I/QRS), and the higher the difference in the amplitude of the R and S waves in lead V1 and V6 [(S1+R6) – (S6+R1)] (QRS post – QRS pre: adjusted odds ratio [OR] 0.97, 95% CI 0.94 to 0.99, p=0.004; ID I/QRS: OR 18.65, 95% CI 1.02 to 342.64, p=0.049; (S1+R6) – (S6+R1): OR 1.1, 95% CI 1.04 to 1.17, p=0.002). The new equation for calculating the predictive CRT response model, generated from multiple logistic regression analysis, was –3.414 – 0.035(QRS post – QRS pre) + 2.926(ID I/QRS) + 0.097[(S1+R6) – (S6+R1)]. The area under the receiver operating characteristic (ROC) curve for the new model for predicting CRT response was 0.853 (95% CI 0.767 to 0.939). A model score of more than 0.3 showed a sensitivity of 85.7% and specificity of 80% for the prediction of CRT response. Conclusion: The new electrocardiographic model achieved a high sensitivity and specificity for the prediction of CRT response among chronic heart failure patients, who met the current guideline recommendation for CRT implantation.