Heart rate variability and meditation: a meta-analysis

Funding Acknowledgements

Type of funding sources: None.

Background

Meditation can induce changes in autonomic balance, which can benefit cardiovascular health. The present meta-analysis evaluated changes in heart rate variability (HRV) in meditators.

Methods

PubMed and Embase were searched for primary prospective studies using the search terms ‘heart rate variability’ and ‘meditation’ until January 18th, 2019. The statistical significance of the difference between subgroups is evaluated by the standardized mean difference (SMD), 95% confidence interval (CI), and P-value. I2 value was used to assess the statistical heterogeneity between the included studies.

Results

Twenty-one studies involving 538 meditators (experienced= 209, beginners= 329) and 334 controls (mean age= 40.61, 35% male) were included. Regarding time-domain indices, no statistically significant differences were observed when assessing HRV between i) meditators versus controls (SMD= -0.17; 95% CI: [-0.50, 0.17]; p= 0.30; I2= 0%), ii) pre- versus post-meditation (SMD= -0.41; 95% CI: [-1.10, 0.28]; p= 0.25; I2= 80%) or iii) at baseline versus during meditation (SMD= -0.40; 95% CI: [-0.94, 0.14]; p= 0.14; I2= 72%). Pertaining to frequency-domain indices, analysis of low frequency (LF), normalized low frequency (LFnu) and high frequency (HF) between i) meditators versus controls, ii) at baseline versus post-meditation and iii) at baseline versus during meditation yet again did not show any variations. Seven studies assessed normalized high frequency (HFnu) at baseline versus during meditation collectively demonstrated a significantly higher HFnu during meditation in beginners with notable heterogeneity (SMD= 1.29; 95% CI: [0.09, 2.49]; p= 0.04; I2= 95). Moreover, LF/HF was evaluated by seven studies at baseline versus during meditation. Both meta-analysis (SMD= 0.76; 95% CI: [-0.17, 1.69]; p= 0.11; I2= 94%) as well as subset analysis of experienced meditators (SMD= -0.46; 95% CI: [-0.88, -0.03]; p= 0.03; I2= 0%) revealed a significantly lower LF/HF at baseline.

Conclusions

Short-term changes in HRV indices were observed during meditation, but there is limited evidence for significant long-term effects.

Programmed Cell Death 1 (PD-1) and Programmed Cell Death Ligand 1 (PD-L1) inhibitors and adverse cardiovascular events: a population-based study

Funding Acknowledgements

Type of funding sources: None.

OnBehalf

Cardiovascular Analytics Group

Background

Programmed death-1 (PD-1) and programmed death-ligand 1 (PD-L1) inhibitors are major classes of immune checkpoint inhibitors that are increasingly used for cancer treatment. However, they are associated with adverse cardiovascular events.

Purpose

To evaluate the cardiotoxicity of PD-1 and PD-L1 inhibitors, the present study aims to examine the incidence of new-onset cardiac complications in patients receiving PD-1 or PD-L1 inhibitors.

Methods

Patients receiving PD-1 or PD-L1 inhibitors since their launch up to December 31st, 2019 without pre-existing cardiac complications were included. Patient data were obtained using a territory-wide electronic health record database. The primary outcome was a composite of incident heart failure (HF), acute myocardial infarction (AMI), atrial fibrillation (AF) or atrial flutter followed up to August 31st, 2020. Propensity score matching between PD-L1 and PD-1 inhibitor use with a 1:1 ratio for patient demographics and comorbidities was performed.

Results

A total of 1925 patients were included. Over a median follow-up of 136 days (interquartile range [IQR]: 42-279), 318 (16.51%) patients met the primary outcome after PD-1/PD-L1 treatment: 242 (incidence rate [IR]: 12.57%) with HF, 38 (IR: 1.97%) with AMI, 53 (IR: 2.75%) with AF, 6 (IR: 0.31%) with atrial flutter. Compared with PD-1 inhibitor treatment, PD-L1 inhibitor treatment was significantly associated with a lower risk of composite outcome after matching (HR: 0.78, 95% CI: [0.62-0.99], P value = 0.0417). Patients who developed cardiovascular complications had shorter average readmission intervals and more hospitalization episodes after treatment with PD-1/PD-L1 inhibitors both before and after matching (P value < 0.0001).

Conclusions

Compared with PD-1 inhibitor users, PD-L1 inhibitor users had a significantly lower risk of new-onset composite cardiovascular complications. Abstract Figure. Kaplan-Meier survival curve

Predictions of arrhythmic, heart failure and mortality outcomes in pericarditis using automatic electrocardiogram analysis: a retrospective cohort study

Funding Acknowledgements

Type of funding sources: None.

Introduction

 Pericarditis is a relatively rare disease with a global burden. Despite its strong association with adverse cardiovascular outcomes, identification of patients at risk of future heart failure or arrhythmic events is difficult. In the following study, automated electrocardiogram (ECG) variables were used to predict new onset ventricular tachycardia/fibrillation (VT/VF), atrial fibrillation (AF) and heart failure with reduced ejection fraction (HF) in an Asian cohort of pericarditis patients.

Purpose

Assessing the use of automated ECG parameters to predict prognosis in pericarditis patients.

Methods

Consecutive patients admitted to a single tertiary center in China, for a diagnosis of pericarditis between 1st January 2005 and 31st December 2019, were included. Patients with existing AF or HF were excluded. The follow-up period was until the 31st December 2020, or death. Cox regression was applied to identify significant predictors of the incident VT/VF, AF or HFrEF.

Results

A total of 874 patients were included. The cohort was 57% male and had a median age of 59 (IQR: 50-70) years old. During follow-up, 57 patients (6.5%), 156 (17.8%) and 168 (19.2%) suffered from VT/VF, AF and HF, respectively. Cox regression identified baseline VT/VF, terminal angle of the QRS vector in the transverse plane, mean QRS duration and mean QTc intervals as significant predictors of incident VT/VF events, with only the foremost maintaining significance in multivariate analysis. In contrast, baseline age, prior diagnoses of hypertension, malignancy and atrial flutter, initial angle and magnitude of the QRS vector in the transverse plane, P-wave and QRS axis in the frontal plane, ST segment axis in the frontal and horizontal planes, mean PT interval, mean PR segment duration and QTc intervals were all univariate predictors of incident AF, albeit only baseline age and initial angle of the QRS vector in the transverse plane retained significance after multivariate adjustment. As it pertains to new-onset HFrEF, several clinical and electrocardiographic parameters demonstrated an association in univariate analysis, with history of hypertension, history of sudden cardiac death (SCD), initial QRS angle in transverse plane, initial 40ms QRS complex axis, ST-segment axis in the horizontal plane, T-wave frontal axis and atrial rate all showcasing significant relationships in multivariate analysis.

Conclusions

AF and HFrEF are relatively common complications, whilst VT/VF occurs less frequently in the context of pericarditis. Different clinical and ECG predictors of these outcomes were identified. Future studies are still needed to evaluate their use for risk stratification in the clinical setting.

A territory-wide study of arrhythmogenic right ventricular cardiomyopathy patients from Hong Kong

Background

Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterised by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD).

Methods

This was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019. The primary outcome was incident ventricular tachycardia/ventricular fibrillation (VT/VF). The secondary outcomes were new-onset heart failure with reduced ejection fraction (HFrEF) and all-cause mortality.

Results

This study consisted of 115 ARVC/D patients (median age: 60 [44.1–70.2] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariate Cox regression identified prolonged QRS duration as a predictor of VT/VF (P<0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (P<0.05). Female gender, prolonged QTc duration and the presence of epsilon waves, in addition to the parameters of older age at diagnosis of ARVC/D, prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p<0.05). Clinical scores were also developed to predict new-onset HFrEF (Table 1a-c) and all-cause mortality (Table 2a-c). This was followed by the application of a non-parametric machine learning survival analysis models for outcome prediction. These machine learning algorithms better capture nonlinear and interactive patterns within survival data compared to traditionally used Cox regression models, which assume the existence of a hazard function between survival data and censored outcomes. The present study introduced weighted random survival forests models for the prediction of incident VT/VF, HFrEF and all-cause mortality. Findings indicate that these machine learning wRSF models performed the best in the prediction of all three aforementioned outcomes compared to other analytical methods.

Conclusion

Clinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients. Machine learning algorithms appear to be the most optimal tools for event prediction, and as such should potentially be used to aid risk stratification and decision-making in the clinical setting.

Funding Acknowledgement

Type of funding sources: None.

Brugada syndrome in Hong Kong: long term outcome prediction through machine learning

Funding Acknowledgements

Type of funding sources: None.

Introduction

 Brugada syndrome (BrS) is an ion channelopathy that predisposes affected patients to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). Despite its greater prevalence in Asia and epidemiological heterogeneity in disease manifestation, the majority of the conducted cohort studies available in current literature are based in Western countries. 

Purpose

The aim of this study is to examine the clinical and electrocardiographic predictive factors of spontaneous VT/VF for Asian BrS patients.

Methods

This was a territory-wide retrospective cohort study of patients diagnosed with BrS between 1997 and 2019. The primary outcome was spontaneous VT/VF detected either during hospital admission or by implantable-cardioverter defibrillator (ICD) data. Cox regression was used to identify significant clinical and electrocardiographic risk predictors. Non-linear interactions between variables (latent patterns) were extracted using non-negative matrix factorization (NMF) and used as inputs into the random survival forest (RSF) model.

Results

This study included 516 consecutive BrS patients (mean age of initial presentation= 50 ± 16 years, male= 92%) with a median follow-up of 86 (interquartile range: 45-118) months. The cohort was divided into subgroups based on initial disease manifestation: asymptomatic (n = 314), syncope (n = 159) or VT/VF (n = 41). Annualized event rates per person-year were 1.70%, 0.05% and 0.01% for the VT/VF, syncope and asymptomatic subgroups, respectively. Multivariate Cox regression analysis revealed initial presentation of VT/VF (HR = 24.0, 95% CI = [1.21, 479] , P= 0.037) and standard deviation of P-wave duration (HR = 1.07, 95% CI = [1.00, 1.13], P = 0.044) were significant predictors. The NMF-RSF showed the best predictive performance compared to RSF and Cox regression models (precision: 0.87 v.s. 0.83 v.s. 0.76, recall: 0.89 v.s. 0.85 v.s. 0.73, F1-score: 0.88 v.s. 0.84 v.s. 0.74).

Conclusions

This is one of the largest territory-wide cohort studies on BrS and the largest study in Asia published to date, with an extensive median follow-up duration of 7 years. Clinical history, electrocardiographic markers and investigation results provide important information for risk stratification. Machine learning techniques using NMF and RSF significantly improves overall risk stratification performance.

P5014The NPAC score for predicting survival after incident acute myocardial infarction

Background

Risk stratification in acute myocardial infarction (AMI) is important for guiding clinical management. Current risk scores are mostly derived from clinical trials with stringent patient selection. We aimed to establish and evaluate a composite scoring system to predict short-term mortality after index episodes of AMI, independent of electrocardiography (ECG) pattern, in a large real-world cohort.

Methods

Using electronic health records, patients admitted to our regional teaching hospital (derivation cohort, n=2127) and an independent tertiary care center (validation cohort, n=1276) with index acute myocardial infarction between January 2013 and December 2017 as confirmed by principal diagnosis and laboratory findings, were identified retrospectively.

Results

Univariate logistic regression was used as the primary model to identify potential contributors to mortality. Stepwise forward likelihood ratio logistic regression revealed that neutrophil-to-lymphocyte ratio, peripheral vascular disease, age, and serum creatinine (NPAC) were significant predictors for 90-day mortality (Hosmer-Lemeshow test, P=0.21). Each component of the NPAC score was weighted by beta-coefficients in multivariate analysis. The C-statistic of the NPAC score was 0.75, which was higher than the conventional Charlson's score (C-statistic=0.63). Application of a deep learning model to our dataset improved the accuracy of classification with a C-statistic of 0.81.

Multivariate binary logistic regression Variable β Adjusted Odds ratio (95% CI) P-value Points Age ≥65 years 1.304 3.68 (2.63–5.17) <0.001 2 Peripheral vascular disease 1.109 3.03 (1.52–6.04) 0.002 2 NLRt ≥9.51 1.100 2.73 (2.12–3.51) <0.001 1 Creatinine≥109 μmol/L 1.003 3.00 (2.35–3.85) <0.001 2

NPAC deep learning model

Conclusions

The NPAC score comprised of four items from routine laboratory parameters and basic clinical information and can facilitate early identification of cases at risk of short-term mortality following index myocardial infarction. Deep learning model can serve as a gate-keeper to provide more accurate prediction to facilitate clinical decision making.