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

doi:10.21203/rs.3.rs-251946/v1

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Research Article

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

https://doi.org/10.21203/rs.3.rs-251946/v1

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posted 10 Mar, 2021

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Pulmonary hypertension (PH) is a fatal disease even under 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.

Applied Mathematics

Computational Mathematics

Cardiac & Cardiovascular Systems

pulmonary hypertension

heart rate variability

non-linear analysis

detrended fluctuation analysis

multiscale entropy

Pulmonary hypertension (PH) is a progressive, complex, and fatal disease. It involves heterogenous etiologies and different mechanisms¹, and eventually leads to right heart failure. The mortality of PH patients is high even after contemporary treatment², however timely and intensive management can improve outcomes even in high-risk patients. In addition, the dynamic adjustment of PH medications based on disease status during follow-up also plays an important role in PH management^3–5. Therefore, a useful tool for PH risk stratification is urgently needed to guide PH treatment. Several prognostic factors of PH have been verified, including sex, exercise tolerance, right heart hemodynamics, and functional performance^6–8, and they have been applied in different prediction models.

In 2015, the European Society of Cardiology (ESC)/European Respiratory Society (ERS) PH guidelines first proposed a dynamic PH risk assessment tool, including a combination of imaging, biologic, hemodynamic, performance status and clinical conditions¹. This tool has shown good survival prediction between different risk groups^9,10, however it requires right heart hemodynamic measurements, which are invasive and difficult to apply for continuous monitoring of PH severity in clinical practice. Therefore, in this study, we propose a non-invasive and convenient tool for PH risk assessment derived from heart rate variability (HRV), namely, heart rhythm complexity analysis.

Heart rhythm complexity analyzes the complexity of changes in heart rate using non-linear methods, and it has been shown to have better predictive value for the diagnosis of PH and heart failure outcomes^11–13 than traditional HRV linear analysis¹⁴. In our previous study, we found that heart rhythm complexity was decreased in PH patients, and that it was useful to differentiate PH patients from normal populations¹³. However, whether heart rhythm complexity is useful in the risk stratification of PH patients is unknown. Therefore, we designed this study to investigate the clinical application of heart rhythm complexity in the risk stratification of PH patients.

Patient characteristics

The clinical, echocardiographic and hemodynamic variables of the enrolled patients are listed in Table 1. There were 20 patients in the high-risk group (group A) and 34 patients in the low-risk group (group B).

Table 1

Clinical Data of the patients
	High-risk group (N = 20)	Low-risk group (N = 34)	P Value
Age (Years)	43.80 ± 10.70	45.76 ± 11.34	0.533
Male, n (%)	9 (45%)	12 (35%)	0.480
BMI (kg·m^− 2)	22.09 ± 3.85	24.21 ± 4.41	0.081
CAD, n (%)	1 (5%)	1 (3%)	1.000
DM, n (%)	2 (10%)	3 (9%)	1.000
HTN, n (%)	1 (5%)	5 (15%)	0.395
Dyslipidemia, n (%)	1 (5%)	3 (9%)	1.000
PAH (WHO group 1)	17 (55%)	18 (29%)	0.017
Hemoglobin (g/dl)	13.72 ± 3.15	13.52 ± 3.76	0.835
Creatinine (mg/dl)	1.15 ± 0.67	0.76 ± 0.26	0.024
Log NT-Pro BNP	3.34 ± 0.54	2.52 ± 0.54	< 0.001
NT-Pro BNP (ng/dl)	1510 (959 ~ 6428)	292 (116 ~ 1045)	< 0.001
LVEF (%)	68.55 ± 9.46	68.62 ± 10.07	0.977
TRPG (mmHg)	93.31 ± 31.8	64.67 ± 28.10	0.001
Pericardial effusion, n (%)	7 (35%)	1 (3%)	0.003
6MWD (m)	298.31 ± 128.00	367.42 ± 120.32	0.074
mPAP (mmHg)	58.11 ± 15.46	47.44 ± 15.27	0.021
PVR (Wood Units)	13.63 ± 6.00	8.24 ± 4.23	0.002
CO (L·min^− 1)	3.71 ± 1.59	4.45 ± 1.30	0.081
CI (L·min^− 1·m²)	2.26 ± 0.97	2.75 ± 0.86	0.069
PCWP (mmHg)	14.00 ± 4.23	12.09 ± 3.69	0.097
PAH specific medication
Sildenafil, n(%)	8 (40%)	15 (44%)	0.768
Macitentan, n(%)	3 (15%)	1 (3%)	0.138
Riociguat, n(%)	0 (0%)	6 (18%)	0.074
Bosentan, n(%)	2 (10%)	2 (6%)	0.622
Iloprost, n(%)	4 (20%)	1 (3%)	0.057
Epoprostenol, n(%)	1 (5%)	1 (3%)	1.000
Abbreviation:
BMI, body mass index; CAD, coronary artery disease; DM, diabetes mellitus; HTN, hypertension; PAH, pulmonary arterial hypertension; NT-proBNP, N-terminal Pro-Brain Natriuretic Peptide; LVEF, left ventricular ejection fraction; TRPG, tricuspid regurgitation pressure gradient; 6MWD, 6-minute-walk-distance; mPAP, mean pulmonary artery pressure; PVR, pulmonary vascular resistance; CO, cardiac output; CI, cardiac index; PCWP, pulmonary capillary wedge pressure

Compared to group B, significantly more patients in group A had World Health Organization (WHO) group 1 pulmonary artery hypertension (PAH) and pericardial effusion. In addition, group A had higher levels of serum creatinine and plasma N-terminal pro-brain natriuretic peptide (NT-proBNP), and higher tricuspid regurgitation peak gradient (TRPG) than group B. In pulmonary hemodynamic studies, pulmonary vascular resistance (PVR), and mean pulmonary artery pressure (mPAP) were significantly higher in group A. The PAH specific medication was listed in Table 1.

Predictors of interest: HRV analysis

In linear HRV analysis, group A had significantly lower the ratio of low frequency (LF) and high frequency (HF) compared to group B. Other linear parameters were comparable between the two groups (Table 2). In non-linear HRV analysis, group A had significantly lower DFAα1, lower the slope of MSE curve between scale 1–5 (slope 1–5), the entropy values of scale 5 (scale 5) and lower area under the MSE curve for scale 6–20 (area 6–20) compared to group B (Table 2). The entropies over different time scales in group A and group B are shown in Fig. 1.

Table 2

Holter parameters of the patients
	High-risk group ( N = 20)	Low-risk group (N = 34)	P Value
Time Domain Analysis
Mean RR (ms)	684.03 (605.77 ~ 795.63)	748.63 (678.30 ~ 805.53)	0.203
SDRR (ms)	57.14 (43.84 ~ 65.88)	64.42 (54.37 ~ 87.43)	0.162
pNN20 (%)	19.17 (9.20 ~ 26.67)	20.86 (13.94 ~ 36.88)	0.463
pNN50 (%)	3.47 (0.32 ~ 12.32)	2.21 (0.77 ~ 6.64)	0.667
Frequency Domain Analysis
VLF (ms^− 2)	172.56 (46.43 ~ 543.01)	384.16 (169.56 ~ 604.98)	0.062
LF (ms^− 2)	64.99 (19.52 ~ 140.02)	98.00 (38.11 ~ 174.58)	0.333
HF (ms^− 2)	42.28 (12.81 ~ 227.52)	36.46 (15.94 ~ 125.03)	0.629
LF/HF ratio	1.06 (0.56 ~ 2.17)	2.14 (1.03 ~ 3.61)	0.026
Detrended fluctuation analysis
DFAα1	0.92 (0.56 ~ 1.05)	1.04 (0.89 ~ 1.23)	0.028
DFAα2	1.12 (1.01 ~ 1.19)	1.11 (1.03 ~ 1.17)	0.900
Multiscale entropy
Slope 1–5	-0.008 (-0.075 ~ 0.039)	0.04 (-0.03 ~ 0.07)	0.038
Scale 5	1.01 (0.73 ~ 1.14)	1.22 (1.06 ~ 1.36)	0.002
Area 1–5	3.30 (2.94 ~ 4.44)	4.18 (3.26 ~ 4.89)	0.135
Area 6–20	15.94 (12.48 ~ 18.40)	18.89 (15.16 ~ 20.91)	0.004

Comparisons of linear and non-linear HRV parameters to differentiate the high-risk PH patients (Fig. 2)

In area under the receiving operating characteristic curve (AUC) analysis, MSE scale 5 had predictive power to predict the high-risk PH patients. The AUCs of MSE scale 5 was 0.758. The AUCs of other linear and non-linear HRV parameters were 0.604 (mean RR), 0.616 (standard deviation of RR interval [SDRR]), 0.560 (percentage of absolute differences in normal RR intervals greater than 20 ms [pNN20]), 0.465 (percentage of absolute differences in normal RR intervals greater than 50 ms [pNN50]), 0.653 (VLF), 0.579 (low frequency [LF]), 0.460 (high frequency [HF]), 0.682 (LH/HF ratio), 0.681 (DFAα1), 0.510 (DFAα2), 0.671 (slope 1–5), 0.623 (area 1–5) and 0.737 (area 6–20).

Logistic regression analysis to predict the presence of high-risk PH (Table 3)

Table 3

Univariable and multivariable logistic regression model to predict the high-risk group in pulmonary hypertension
Univariable logistic regression			Multivariable logistic regression
	OR (95% CI)	P	OR (95% CI)	P
Age	0.984 (0.935 ~ 1.035)	0.525
Sex	1.500 (0.486 ~ 4.631)	0.481
BMI	0.884 (0.768 ~ 1.017)	0.086
PAH group 1	5.037 (1.242 ~ 20.43)	0.024
Creatinine	8.301 (1.358 ~ 50.75)	0.022
NT-Pro BNP	1.001 (1.000 ~ 1.002)	0.019	1.001 (1.000 ~ 1.002)	0.009
6MWD	0.995 (0.990 ~ 1.001)	0.080
mPAP	1.046 (1.005 ~ 1.089)	0.029
CI	0.525 (0.258 ~ 1.067)	0.075
PVR	1.232 (1.070 ~ 1.418)	0.004
Mean RR	0.997 (0.992 ~ 1.002)	0.198
SDRR	0.992 (0.973 ~ 1.010)	0.373
pNN20	0.993 (0.961 ~ 1.025)	0.647
pNN50	1.016 (0.971 ~ 1.063)	0.503
VLF	0.998 (0.996 ~ 1.000)	0.081
LF	0.999 (0.997 ~ 1.002)	0.543
HF	1.000 (0.999 ~ 1.001)	0.858
LF/HF ratio	0.622 (0.391 ~ 0.990)	0.045
DFAα1	0.072 (0.008 ~ 0.626)	0.017
DFAα2	0.457 (0.006 ~ 33.761)	0.721
Slope 1–5	0.000 (0.000 ~ 0.560)	0.036
Scale 5	0.012 (0.001 ~ 0.222)	0.003	0.009 (< 0.001 ~ 0.324)	0.010
Area 1–5	0.705 (0.418 ~ 1.189)	0.190
Area 6–20	0.835 (0.714 ~ 0.977)	0.024

In univariable logistic regression analysis, serum creatinine level, PAH group 1, plasma NT-pro BNP level, mPAP, PVR, LF/HF ratio, DFAα1, MSE slope 1–5, scale 5, and area 6–20 were significantly associated with the presence of high-risk PH. These parameters were further investigated in multivariable logistic regression analysis, which showed that plasma NT-pro BNP levels (odds ratio [OR]: 1.001, 95% confidence interval [CI]: 1.000 ~ 1.002, p = 0.009), and MSE scale 5 (OR: 0.009, 95% CI: <0.001 ~ 0.324, p = 0.010) were remained in the model and both NT-pro BNP level and MSE scale 5 were significantly associated with the presence of high-risk PH.

The effect of adding heart rhythm complexity to the linear HRV parameters to identify high-risk PH patients (Table 4)

Table 4

AUC, NRI, and IDI models of linear parameters before and after adding DFAα1 and MSE parameters for risk stratification in pulmonary hypertension
Parameters		AUC	R square	NRI	NRI p-value	IDI	IDI p-value
Mean RR		0.604	0.032
	+ Scale5	0.775	0.051	0.694	0.008	0.194	0.001
	+Area 6–20	0.749	0.12	0.535	0.048	0.092	0.026
	+ DFAα1	0.701	0.126	0.494	0.071	0.095	0.028
SDRR		0.615	0.015
	+ Scale5	0.781	0.12	0.771	0.003	0.211	0.001
	+Area 6–20	0.731	0.121	0.494	0.071	0.107	0.014
	+ DFAα1	0.681	0.123	0.535	0.048	0.108	0.017
VLF		0.653	0.061
	+ Scale5	0.782	0.117	0.535	0.048	0.171	0.002
	+Area 6–20	0.725	0.147	0.653	0.014	0.082	0.035
	+ DFAα1	0.699	0.145	0.694	0.008	0.084	0.037
LF		0.579	0.008
	+ Scale5	0.768	0.086	0.771	0.003	0.209	0.001
	+Area 6–20	0.731	0.118	0.494	0.071	0.112	0.012
	+ DFAα1	0.694	0.134	0.553	0.042	0.129	0.01
HF		0.54	0.001
	+ Scale5	0.76	0.029	0.871	0.001	0.221	< 0.001
	+Area 6–20	0.734	0.116	0.553	0.042	0.118	0.01
	+ DFAα1	0.694	0.129	0.612	0.023	0.132	0.009
LF/HF ratio		0.682	0.075
	+ Scale5	0.806	0.077	0.771	0.003	0.184	0.001
	+Area 6–20	0.76	0.156	0.394	0.154	0.068	0.039
	+ DFAα1	0.718	0.114	0.335	0.228	0.027	0.19

In both net reclassification improvement (NRI) and integrated discrimination improvement (IDI) models, the MSE scale 5 significantly improved the discrimination power of all linear HRV parameters, including mean RR, SDRR, VLF, LF, HF, and LF/HF ratio. Area 6–20 significantly improved the discrimination power of mean RR, VLF and HF in both NRI and IDI models, and SDRR, LF and LF/HF ratio in IDI model. DFAα1 significantly improved the discrimination power of SDRR, VLF, LF and HF in both the NRI and IDI models, and mean RR in the IDI model.

The main finding of this study was that heart rhythm complexity was significantly depressed in high-risk PH patients. In addition, adding heart rhythm complexity predictors to traditional linear HRV parameters improved the power to predict high-risk PH patients. This is the first study to demonstrate an association between heart rhythm complexity and severity of PH, and the better performance of heart rhythm complexity in identifying high-risk PH patients than traditional HRV parameters.

PH is a critical disease which needs an early diagnosis and timely management. Patients classified as being at high risk according to the 2015 ESC/ERS PH guidelines have a worse prognosis compared to patients at low risk. Sitbon et al. demonstrated that poor functional status was associated with poor outcomes. In their study, PH patients in WHO functional class IV and those in class III with severely compromised hemodynamics had the worst outcomes¹⁵. Previous studies have demonstrated that early interventions including both pharmacological and multidisciplinary team care can improve the outcomes of PH patients, even those with severe disease and poor functional status⁵. Therefore, identifying high-risk patients is essential for the management of PH. Several survival prediction models have been proposed for PH patients, however they are complex and difficult to use¹⁶. Currently, the 2015 ESC/ERS PH guidelines advocate assessing the risk of PH by using a combination of several different tools, and this method is widely used in daily practice¹. However, risk assessment requires invasive right heart catheterization, which is difficult to apply in frequent monitoring during follow-up. Therefore, there is still a strong unmet need for an easy to use tool to allow for both timely and continuous monitoring of disease status to improve the clinical care of PH patients.

HRV is a useful non-invasive tool which has been studied in many diseases including coronary artery disease, heart failure and even pulmonary hypertension^17–19. It has been correlated with autonomic dysfunction and used as an outcome predictor. Autonomic dysfunction has also been correlated with the severity of PH^20,21. Therefore, measuring autonomic system regulation resulting from PH using HRV could potentially be a predictor of disease severity and long-term outcomes^22–25. Bienias et al. demonstrated that patients with arterial or chronic thromboembolic PH had significantly impaired heart rate turbulence, a linear HRV parameter²⁶. Recently, Peng et al. proposed the use of heart rhythm complexity derived from two non-linear parameters of HRV, DFA and MSE, which are based on fractal and chaos therapy, respectively^27–29. Heart rhythm complexity has been shown to have better efficacy and predictive power for various diseases than traditional HRV^14,30.

Heart rhythm complexity measures the complexity of changes in the R-R interval which contains detailed information derived from heart rate dynamics. Once a biological system has become diseased, the complexity breaks down, and non-linear HRV analysis can detect subtle changes at an early stage³¹. In a retrospective study, abnormal DFAα1 in asymptomatic heart failure patients was associated with the onset of heart failure years in advance of the first clinical event^32,33. Tsai et al. recently demonstrated that heart rhythm complexity had a better prognostic value for cardiovascular events in patients undergoing peritoneal dialysis compared with linear HRV analysis³⁰. In recent years, heart rhythm complexity has been extensively studied in many fields, including left heart failure³⁴, post-infarction myocardial function³⁵, patients undergoing dialysis^12,30,36, severity of abdominal aorta calcification³⁷, primary aldosteronism³⁸, stroke³⁹, and PH⁴⁰. These studies support the importance of heart rhythm complexity in clinical practice and its potential role in disease risk stratification. In the present study, we demonstrated that heart rhythm complexity parameters, especially MSE scale 5, were significantly associated with PH disease severity and could be used in PH risk stratification. To the best of our knowledge, this is the first study to apply heart rhythm complexity to the prediction of PH disease severity.

Compared with heart rhythm complexity, linear HRV parameters, including SDRR, SDRR index, VLF, LF/HF ratio and heart rate turbulence have been widely studied to assess PH^41,42. Recent studies have also demonstrated an association between impaired linear HRV parameter, SDRR and PH disease severity markers, including impaired WHO functional status, decreased 6MWD, impaired tricuspid annular plane systolic excursion (TAPSE), right ventricular systolic function, higher TRPG and NT-pro BNP level^43–45. In this study, we first demonstrated a better association between heart rhythm complexity and PH disease severity compared to traditional HRV analysis. Second, the discrimination power of linear HRV for PH disease severity improved significantly after combining heart rhythm complexity parameters. The combination of linear and non-linear HRV parameters to form a new predictive model may have further improved its risk stratification ability and outcome prediction.

There are several limitations to this study. First, this is a pilot study. The number of cases was small, and further studies are needed to validate the results. Second, we only enrolled PH patients in WHO group 1 and group 4, and future studies should enroll different groups of PH patients to investigate the potential application of HRV in these patients. Third, this pilot study is a cross-sectional design and lacks clinical long-term follow-up data. A prospective cohort study with clinical end-point follow-up is needed to confirm the utility of heart rhythm complexity on clinical outcome predictions.

This study demonstrated that high-risk PH patients had worse heart rhythm complexity. MSE scale 5 had the best discrimination power to predict high-risk PH patients. Moreover, adding MSE scale 5, area 6–20 or DFAα1 to linear HRV parameters significantly improved the predictive power for high-risk PH patients. Heart rhythm complexity can potentially be used as an indicator of PH disease severity and to stratify the risk of PH.

Patients

We prospectively enrolled 54 Taiwanese patients with PH from a single center, including 34 with PAH (WHO group 1) and 20 with chronic thromboembolic pulmonary hypertension (CTEPH, WHO group 4) from May 2012 to April 2018. PAH and CTEPH have similar pathophysiological mechanisms as vascular arteriopathy⁴⁶, presenting as elevated pre-capillary vessel pressure and pulmonary vascular resistance⁴⁷. Other types of PH may involve complex disease mechanisms such as lung disease or heart failure, which may result in patient heterogeneity and were excluded from this study. Therefore, we only enrolled these two PH subgroups in the present study to avoid the confounding influence of other pathophysiologies.

All patients underwent echocardiography, right heart catheterization and 24-hour ambulatory electrocardiogram Holter recording. The diagnosis of PH was confirmed by right heart catheterization, based on the ESC guidelines¹. Holter recordings were performed 2 months before or after right heart catheterization.

The patients were divided into two groups based on PH severity⁴⁸. There were 20 patients in the high-risk group (group A), which was defined as WHO functional class IV or class III with severely compromised hemodynamics (right atrial pressure: P_ra >15 mmHg or cardiac index < 2.0 L·min^− 1·m²)^15,49, and 34 patients in the low-risk group (group B).

Echocardiogram

All patients underwent typical transthoracic echocardiography (iE33 x MATRIX Echocardiography System, Philips, Amsterdam, Netherlands). According to the recommendations of the American Society of Echocardiography, TRPG was measured as the peak flow velocity of tricuspid regurgitation using a simplified Bernoulli equation: TRPG = 4 x TRV². Left ventricular ejection fraction in M-mode was measured in the parasternal long axis view⁵⁰.

24-hour Holter recording and data processing

All patients received 24-hour ambulatory electrocardiogram Holter recording (Zymed DigiTrak Plus 24-Hour Holter Monitor Recorder and Digitrak XT Holter Recorder 24-Hour, Philips, Amsterdam, Netherlands) and maintained their original daily activity during the examination without specific limitations. A selected stable 4-hour duration of daytime RR intervals was obtained between 9 AM and 5 PM. The data were automatically processed using an algorithm and then checked by two technicians. HRV parameters were processed automatically with MATLAB software.

Linear HRV analysis

Based on the recommendations of the North American Society of Pacing Electrophysiology and the European Society of Cardiology, conventional linear HRV analysis was performed⁵¹. We analyzed time domain and frequency domain parameters. Time domain analysis included mean RR, SDRR, pNN₂₀, and pNN₅₀, representing autonomic nervous system modulation of heart rhythm. Frequency domain analysis, including HF (0.15–0.4 Hz), LF (0.04–0.15 Hz), and VLF (0.003–0.04 Hz) was conducted after Fourier transformation.

Non-linear HRV analysis

For non-linear HRV analysis, we analyzed MSE and DFA according to fractal and stochastic theories, respectively. Normal heartbeats have inter-beat fluctuations as long-range correlation behavior in time series. Pathophysiologically, the long-range correlation behavior disappears due to dysregulation of the competitive mechanism of sympathetic and parasympathetic systems.

DFA is used to quantify the correlation property in non-stationary inter-beat interval dynamics in time series²⁷. Initially, we integrated the inter-beat interval time series, and then divided it into boxes of equal length. In each box, the trend represented the fractal correlation of the time series. On a double log graph, the slope of the line was defined as the α exponent, representing the fractal correlation property of the time series. Both short-range and long-range time scales were used, and calculated as (α1: 4–11 beats) and (α2: 11–64 beats), respectively⁵².

Multiscale entropy (MSE) analysis is used to measure the complexity of finite length time series. Traditional single scale entropy estimation yields lower entropy in times series. “Coarse graining” proceeds multiple time scales and provides rich information of the complexity of the system. To estimate entropy, we calculated sample entropy (SampEn) for each coarse-grained time series, and then plotted this as a function of the scale factor. To quantify the complexity of the heartbeat dynamics in short and long time scales, we calculated scale 5, the linear-fitted slope of scale 1–5 (slope 5), area 1–5 and area 6-20²⁹.

Statistical analysis

Continuous variables were expressed as mean ± standard deviation for normally distributed variables, and median (interquartile range, 25th and 75th percentiles) for non-normally distributed variables. Categorical variables were expressed as absolute and relative frequencies (percentage). Comparisons were made using the independent t-test and Mann-Whitney U test between two groups. The chi-square test or Fisher’s exact test was used to examine differences between proportions. Logistic regression analysis was used to assess associations between variables and high-risk PH. Significant determinants in univariable logistic regression analysis (p < 0.05) including creatinine, PAH group 1, serum creatinine level, plasma NT-pro BNP level, mPAP, PVR, LF/HF ratio, DFAα1, slope 1–5, MSE scale 5, and area 6–20 were then tested in multivariable logistic regression analysis with stepwise selection to identify independent factors that could predict high-risk PH. Category-free (continuous) NRI and IDI were used to evaluate improvements in the accuracy of the prediction after adding a single nonlinear parameter into a logistic regression model using only linear parameters^53,54. The significance of NRI and IDI statistics was based on approximate normal distributions. All statistical analyses were performed using R software 4.0.3 (http://www.r-project.org/) and SPSS version 25 for Windows (SPSS Inc., IL, USA). The significance level was set at 0.05 (p < 0.05).

Acknowledgements. This study was supported by grants from National Taiwan University Hospital (NTUH 109-A141) and Department of Health, Executive Yuan, R.O.C. (PTH 107-43, PTH 108-29, PTH 109-13). M.T.L. and C.L. gratefully acknowledge support from the Ministry of Science and Technology (grant no. 104-3115-E-008-001 and 104-2745-B-008-001) and the joint foundations (grant no. 103CGH-NCU-A1, VGHUST103-G1-3-3). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author contributions. Yen-Hung Lin conceived and designed the experiments. Shu-Yu Tang, Cheng-Hsuan Tsai, Hsi-Pin Ma, Chi-Sheng Hung, Chen Lin, Men-Tzung Lo and Yu-Wei Chi analyzed the data. Shu-Yu Tang, Cheng-Hsuan Tsai and Yen-Tin Lin wrote the paper. Chung-Kang Peng, Ping-Hung Kuo, Hsao-Hsun Hsu and Cho-Kai Wu made scientific comments on the manuscript

Competing Interests: The authors declare no competing interests.

Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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

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