Risk Stratification in Idiopathic Dilated Cardiomyopathy Patients Using Cardiovascular Coupling Analysis

Abstract

Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDC HR ) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDC LR ) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDC HR patients presented lowered HRV and increased BPV compared to both IDC LR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDC LR vs. IDC HR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.

Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability

“…Despite the growing use of machine learning-based prediction models in medicine [5][6][7][8][9], clinicians still struggle to rely on these models in clinical practice [10]. Machine learning methods were also applied to produce heart disease detection and prediction models [11] based on clinical history and ECG features [12], magnetocardiography [13], photoplethysmography signal parameters [14], and HRV and blood pressure variability features [15].…”

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies

“…Ultimately, a total of 46 studies were included in this review. 15 , 16 , 17 , 18 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 Out of these 46 studies, 36 used one or more ad-hoc dataset(s) and were pooled in separate meta-analysis. 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 57 , …”

“…The characteristics of studies are summarised in Table 1 , details on the electrophysiological signals used are displayed in Supplementary Material Table S6 . 15 , 16 , 17 , 18 , 38 , 45 , 53 , 56 , 62 , 64 Two studies used intracardiac EGMs, 15 , 56 seven used body surface ECG recordings 16 , 17 , 38 , 45 , 53 , 62 , 64 and one study used ventricular monophasic action potentials (MAP) as model input. 18 ECGs ranged from 10 s till 24 h in duration and differed in number of leads (1-, 3-, 7- and 12-leads) and sampling rate (125 Hz–1600 Hz).…”

“… 18 ECGs ranged from 10 s till 24 h in duration and differed in number of leads (1-, 3-, 7- and 12-leads) and sampling rate (125 Hz–1600 Hz). Support vector machine classifiers were implemented as prediction model in six studies, 15 , 17 , 18 , 56 , 62 , 64 ensemble learning methods (random forests, decision tree) in three studies 15 , 38 , 45 and artificial neural network in one study. 53 Kwon et al.…”

“…long-term prediction). K-fold cross-validation and leave-one-out-cross validation were used for model validation in four studies validation, 17 , 56 , 62 , 64 whereas a hold-out test set was used in six studies. 15 , 16 , 18 , 38 , 45 , 53 External validation of the model was performed in two studies.…”