* Corresponding Author
Scoring system based on electrocardiogram features to predict the type of heart failure in patients with chronic heart failure
https://doi.org/10.19106/JMedSci004803201601 Hendry Purnasidha Bagaswoto(1*), Lucia Kris Dinarti(2), Erika Maharani(3)
(1) Cardiology and Vascular Department, Faculty of Medicine Gadjah Mada University-Sardjito General Hospital
(2) Cardiology and Vascular Educational Program, Faculty of Medicine / Dr. Sardjito General Hospital, Universitas Gadjah Mada, Yogyakarta
(3) Cardiology and Vascular Educational Program, Faculty of Medicine / Dr. Sardjito General Hospital, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
ABSTRACT
Heart failure is divided into heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Additional studies are required to distinguish between these two types of HF. A previous study showed that HFrEF is less likely when ECG findings are normal. This study aims to create a scoring system based on ECG findings that will predict the type of HF. We performed a cross-sectional study analyzing ECG and echocardiographic data from 110 subjects. HFrEF was defined as an ejection fraction ≤40%. Fifty people were diagnosed with HFpEF and 60 people suffered from HFrEF. Multiple logistic regression analysis revealed certain ECG variables that were independent predictors of HFrEF i.e., LAH, QRS duration >100 ms, RBBB, ST-T segment changes and prolongation of the QT interval. Based on ROC curve analysis, we obtained a score for HFpEF of -1 to +3, while HFrEF had a score of +4 to +6 with 76% sensitivity, 96% specificity, 95% positive predictive value, an 80% negative predictive value and an accuracy of 86%. The scoring system derived from this study, including the presence or absence of LAH, QRS duration >100 ms, RBBB, ST-T segment changes and prolongation of the QT interval can be used to predict the type of HF with satisfactory sensitivity and specificity
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DOI: https://doi.org/10.19106/JMedSci004803201601
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