Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery—a systematic review
Ontology highlight
ABSTRACT:
SUBMITTER:
PROVIDER: S-EPMC10839520 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundMobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.ObjectiveThis review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research.MethodsWe conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV).ResultsThe findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF.ConclusionsA number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.
| S-EPMC6396075 | biostudies-literature
Project description:Using conditional immortalization, we generated the first lines of human cardiomyocytes with preserved cardiomyogenic differentiation capacity. These human immortalized Atrial Myocytes (hiAMs) display strict control over proliferation and differentiation, allowing massive (quadrillion-fold) expansion followed by differentiation towards fully functional (i.e. excitable and contractile) human atrial cardiomyocytes. Here, we compared the transcriptome of three hiAM monoclones after differentiation (D12) with hESC-derived AMs (D28) using RNA-sequencing.
2021-08-30 | GSE178473 | GEO
Project description:The analysis of P-wave template has been widely used to extract indices of Atrial Fibrillation (AF) risk stratification. The aim of this paper was to assess the potential of the analysis of the P-wave variability over time in patients suffering from atrial fibrillation. P-wave features extracted from P-wave template together with novel indices of P-wave variability have been estimated in a population of patients suffering from persistent AF and compared to those extracted from control subjects. We quantify the P-wave variability over time using three algorithms and we extracted three novel indices: one based on the cross-correlation coefficients among the P-waves (Cross-Correlation Index, CCI), one associated to variation in amplitude of the P-waves (Amplitude Dispersion Index, ADI), one sensible to the phase shift among P-waves (Warping Index, WI). The control group resulted to be characterized by shorter P-wave duration and by a less amount of fragmentation and variability, respect to AF patients. The parameter CCI shows the highest sensitivity (97.3%) and a good specificity (95%).
| S-EPMC4880942 | biostudies-other
Project description:BackgroundWearables satisfactorily detect atrial fibrillation (AF) longer than 1 hour. Our study aims to evaluate smartwatch performances for long-term AF monitoring, including AF with short durations.MethodsThis prospective study enrolled AF patients from 2020 to 2023. Diagnostic efficacy of the Amazfit smartwatch, with AF-identifying algorithms from photoplethysmography (PPG) and single-lead electrocardiogram (ECG), was compared with a 7-day Holter. Primary analysis included smartwatch diagnostics to identify AF longer than 5 minutes. Secondary analyses evaluated smartwatch performances under different settings and compared AF burdens between the smartwatch and Holter.ResultsThe study analyzed 72 patients (48 males, mean age 65.4 ± 8.5) with 914 AF episodes lasting 834.7 hours, including 142 longer-than-5-minute AF episodes. Smartwatch recording time was 8927.6 hours. By individual, sensitivities and specificities of AF longer than 5 minutes were 100.0% and 83.7% for PPG and 89.7% and 67.4% for the ECG algorithm. Positive and negative predictive values were 94.9% and 99.9% for PPG and 77.6% and 99.8% for ECG. Optimal AF durations to be identified by PPG and ECG algorithms were 1.358 and 16.708 minutes. Smartwatch performances varied across AF durations and between day-time and night-time. Strong correlations (PPG: ρ = 0.877; ECG: ρ = 0.769) and excellent agreements (PPG: ICC = 0.976; ECG: ICC = 0.927) were found between AF burdens calculated from smartwatch and Holter.ConclusionsCompared with long-term Holter, the wearable smartwatch had satisfying qualitative and quantitative diagnostic performances for continuous AF monitoring. Susceptibility to false positives led to modest specificity. Smartwatch performances were affected by AF durations and time periods.RegistrationChiCTR2000040035.
| S-EPMC11758528 | biostudies-literature
Project description:Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost.
| S-EPMC7913967 | biostudies-literature
Project description:Background Previous randomized control trials showed mixed results concerning the effect of omega-3 fatty acids (n-3 FAs) on atrial fibrillation (AF). The associations of n-3 FA blood levels with heart rhythm in patients with established AF are unknown. The goal of this study was to assess the associations of total and individual n-3 FA blood levels with AF type (paroxysmal versus nonparoxysmal), heart rate (HR), and HR variability in patients with AF. Methods and Results Total n-3 FAs, eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, and alpha-linolenic acid blood levels were determined in 1969 patients with known AF from the SWISS-AF (Swiss Atrial Fibrillation cohort). Individual and total n-3 FAs were correlated with type of AF, HR, and HR variability using standard logistic and linear regression, adjusted for potential confounders. Only a mild association with nonparoxysmal AF was found with total n-3 FA (odds ratio [OR], 0.97 [95% CI, 0.89-1.05]) and docosahexaenoic acid (OR, 0.93 [95% CI, 0.82-1.06]), whereas other individual n-3 FAs showed no association with nonparoxysmal AF. Higher total n-3 FAs (estimate 0.99 [95% CI, 0.98-1.00]) and higher docosahexaenoic acid (0.99 [95% CI, 0.97-1.00]) tended to be associated with slower HR in multivariate analysis. Docosapentaenoic acid was associated with a lower HR variability triangular index (0.94 [95% CI, 0.89-0.99]). Conclusions We found no strong evidence for an association of n-3 FA blood levels with AF type, but higher total n-3 FA levels and docosahexaenoic acid might correlate with lower HR, and docosapentaenoic acid with a lower HR variability triangular index.
| S-EPMC10381984 | biostudies-literature
Project description:There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording (n = 18), (ii) analyzing the ability to detect irregularities of the rhythm (n = 7), and (iii) measuring the heartrate monitoring accuracy (n = 31). Arterial waveforms carry important physiological information and the comparison study revealed that the recordings made with the wearable device and with the gold standard device resulted in almost identical (r = 0.9-0.99) pulse waveforms. The device can measure the heart rhythm and possible irregularities in it. A clustering analysis demonstrates a perfect classification accuracy between atrial fibrillation (AF) and sinus rhythm. The heartrate monitoring study showed near perfect beat-to-beat accuracy (sensitivity = 99.1%, precision = 100%) on healthy subjects. In contrast, beat-to-beat detection from coronary artery disease patients was challenging, but the averaged heartrate was extracted successfully (95% CI: -1.2 to 1.1 bpm). In conclusion, the results indicate that the device could be useful in remote monitoring of cardiovascular diseases and personalized medicine.
| S-EPMC6550190 | biostudies-literature
Project description:To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the clinical parameters of patients with implanted pacemakers in comparison to logistic regression (LR). We included 721 patients without known atrial fibrillation or atrial flutter from a prospective multicenter (11 tertiary hospitals) registry comprising all geographical regions of Korea from September 2017 to July 2020. Predictive models of clinically relevant AHREs were developed using the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm. Model prediction training was conducted by seven hospitals, and model performance was evaluated using data from four hospitals. During a median follow-up of 18 months, clinically relevant AHREs were noted in 104 patients (14.4%). The three ML-based models improved the discrimination of the AHREs (area under the receiver operating characteristic curve: RF: 0.742, SVM: 0.675, and XGB: 0.745 vs. LR: 0.669). The XGB model had a greater resolution in the Brier score (RF: 0.008, SVM: 0.008, and XGB: 0.021 vs. LR: 0.013) than the other models. The use of the ML-based models in patient classification was associated with improved prediction of clinically relevant AHREs after pacemaker implantation.
| S-EPMC8741914 | biostudies-literature
Project description:Purpose: Silent brain infarcts (SBI) are frequently detected in patients with atrial fibrillation (AF), but it is unknown whether SBI are linked to autonomic dysfunction. We aimed to explore the association of autonomic dysfunction with SBI in AF patients. Methods: 1,358 AF patients without prior stroke or TIA underwent brain MRI and 5-min resting ECG. We divided our cohort into AF patients who presented in sinus rhythm (SR-group, n = 816) or AF (AF-group, n = 542). HRV triangular index (HRVI), standard deviation of normal-to-normal intervals, mean heart rate, root mean square root of successive differences of normal-to-normal intervals, 5-min total power and power in the low frequency, high frequency and very low frequency range were calculated. Primary outcome was presence of SBI in the SR group, defined as large non-cortical or cortical infarcts. Secondary outcomes were SBI volumes and topography. Results: Mean age was 72 ± 9 years, 27% were female. SBI were detected in 10.5% of the SR group and in 19.9% of the AF group (p < 0.001). HRVI <15 was the only HRV parameter associated with the presence of SBI after adjustment for clinical covariates in the SR group [odds ratio (OR) 1.67; 95% confidence interval (CI): 1.03-2.70; p = 0.037]. HRVI <15 was associated with larger brain infarct volumes [β (95% CI) -0.47 (-0.84; -0.09), p = 0.016] in the SR group and was more frequently observed in patients with right- than left-hemispheric SBI (p = 0.017). Conclusion: Impaired HRVI is associated with SBI in AF patients. AF patients with autonomic dysfunction might undergo systematic brain MRI screening to initiate intensified medical treatment. Clinical Trials Gov Identifier: NCT02105844.
| S-EPMC8175975 | biostudies-literature
Project description:The autonomic nervous system (ANS) plays an important role in the initiation and maintenance of atrial fibrillation (AF). However, the meaning of higher heart rate variability (HRV) in predicting AF remains unclear. Among 2100 patients in the Holter registry, a total of 782 hypertensive patients were included in this study. Baseline HRV was measured by time domain and frequency domain methods using 24-h Holter monitoring. The primary outcome was the development of AF. During an average follow-up of 1.1 years, 44 patients developed AF. Higher HRV parameters including high-frequency (P < 0.001), the square root of the mean squared differences of successive NN intervals (P < 0.001), and the percentage of NN intervals that are more than 50 ms different from the previous interval (P < 0.001) were associated with the occurrence of AF in univariate analysis. Premature atrial contractions burden, lower baseline heart rate, age, hemodialysis, coronary artery disease, and chronic heart failure were also associated with AF. In Cox regression analysis, higher HRV (representing excessive autonomic fluctuation) was an independent risk factor for AF. Excessive autonomic fluctuation represented by higher HRV in patients with hypertension was associated with an increased risk of AF.
| S-EPMC8904557 | biostudies-literature