The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients

Abstract

Pulmonary hypertension (PH) is a fatal disease—even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6–20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability

“…The MSE complexity measure analysis method was proposed by Costa et al (2002) , which quantifies the non-linear dynamics of complex systems on multiple scales based on SampEn and measures signal complexity more comprehensively. Several studies have been conducted to apply the complexity indicators quantified by this method for disease prediction, classification and prognostic assessment ( Lin et al, 2016 ; Frassineti et al, 2021 ; Tang et al, 2021 ; Yang et al, 2021 ; Liao et al, 2022 ). For example, Frassineti et al (2021) found that MSE analysis had prescreening value in neonatal seizures.…”

“…For example, Frassineti et al (2021) found that MSE analysis had prescreening value in neonatal seizures. Tang et al (2021) showed that MSE analysis was helpful for the identification of high-risk pulmonary hypertension patients. In contrast to other single time scale analyses, MSE can reflect an understanding of a range of time scales ( Busa and van Emmerik, 2016 ).…”

Modifications of long-term heart rate variability produced in an experimental model of diet-induced metabolic syndrome

“…The MSE method employs an entropy measure to quantify the degree of unpredictability of RR time series, taking into account the complex temporal fluctuations conveyed in different scales. Even though studies that have analysed the nonlinear dynamics and the complexity of HRV using long-term RR time series in MetS are scarce, the decrease in maximum MSE indicates higher predictability of cardiac fluctuations and lower randomness and complexity in cardiac activity [71], which has been described in other populations such as heart failure, high-risk pulmonary hypertension and peritoneal dialysis patients [62,63,72].…”

“…Finally, we studied the nonlinear dynamics of the RR time series. Although the use of nonlinear parameters for HRV analysis is not widely implemented, these measures have been shown to be useful for the assessment of cardiovascular risk and sudden cardiac death [28,[59][60][61][62] and are a wellrecognized way to distinguish between physiological and pathological conditions [58]. Furthermore, the importance and pathophysiological significance of the DFA scaling exponents and the multiscale properties, as well as their advantages over traditional HRV measurements, have been highlighted by some [63,64], even though the precise mechanisms underlying their modifications are not completely understood.…”

“…Furthermore, the importance and pathophysiological significance of the DFA scaling exponents and the multiscale properties, as well as their advantages over traditional HRV measurements, have been highlighted by some [63,64], even though the precise mechanisms underlying their modifications are not completely understood. We used the multiscale entropy (MSE) and detrended fluctuation analysis (DFA) to quantify the fractal properties of the HRV series, such as long-term memory effect and information richness over different time-scales [62]. DFA quantifies the correlation property of inter-beat interval dynamics in the time series, while eliminating the external nonstationarity by removing the linear-fitted trends in a different time-scale (box-size), as previously described [65].…”