Screening for Atrial Fibrillation During Automatic Blood Pressure Measurements

Atrial Fibrillation Screening During Routine Automated Office, Home, and Ambulatory Blood Pressure Measurement: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

BACKGROUND

Atrial fibrillation (AF) is often asymptomatic and undiagnosed. As AF and hypertension often coexist, opportunistic AF detection during routine automated blood pressure (BP) measurement appears to be an attractive screening method.

METHODS

A systematic literature search was conducted to identify studies assessing the diagnostic test accuracy of office, home, or 24-hour ambulatory BP measuring devices with AF detection algorithms versus reference electrocardiography. Analyses were performed per participant (AF status based on several BP readings; most office/home devices) or per reading (AF status based on individual readings; all ambulatory devices). A meta-analysis stratified by device type (office/home/ambulatory) was conducted to calculate pooled measures of diagnostic accuracy. Sensitivity/meta-regression analyses were also performed.

RESULTS

Among 3096 records initially retrieved, 23 diagnostic test accuracy studies were included. Data derived from 11 093 individuals (weighted age 69 years, males 56%, hypertensives 79%, diabetics 24%, and AF prevalence 17%) indicated a pooled sensitivity 0.97 (95% CI, 0.92-0.99), specificity 0.93 (95% CI, 0.90-0.95), and accuracy 0.93 (95% CI, 0.89-0.95), with generally consistent results using office, home, or ambulatory BP devices (slightly lower specificity with the latter). The positive and negative predictive values were 0.70 (95% CI, 0.60-0.80) and 0.99 (95% CI, 0.98-1.00), respectively. Sensitivity analyses indicated lower specificity in studies implementing reading versus participant analyses. Most studies presented a low risk of bias and minor applicability concerns.

CONCLUSIONS

There is considerable and consistent evidence suggesting high diagnostic accuracy of AF detection algorithms implemented in automated BP monitors during routine BP measurements in and out of the office. AF diagnosis requires verification (electrocardiography) before treatment is administered.

Atrial fibrillation detection in primary care during blood pressure measurements and using a smartphone cardiac monitor

Improved atrial fibrillation (AF) screening methods are required. We detected AF with pulse rate variability (PRV) parameters using a blood pressure device (BP+; Uscom, Sydney, Australia) and with a Kardia Mobile Cardiac Monitor (KMCM; AliveCor, Mountain View, CA). In 421 primary care patients (mean (range) age: 72 (31–99) years), we diagnosed AF (n = 133) from 12-lead electrocardiogram recordings, and performed PRV and KMCM measurements. PRV parameters detected AF with area under curve (AUC) values of up to 0.92. Using the mean of two sequential readings increased AUC to up to 0.94 and improved positive predictive value at a given sensitivity (by up to 18%). The KMCM detected AF with 83% sensitivity and 68% specificity. 89 KMCM recordings were “unclassified” or blank, and PRV detected AF in these with AUC values of up to 0.88. When non-AF arrhythmias (n = 56) were excluded, the KMCM device had increased specificity (73%) and PRV had higher discrimination performance (maximum AUC = 0.96). In decision curve analysis, all PRV parameters consistently achieved a positive net benefit across the range of clinical thresholds. In primary care, AF can be detected by PRV accurately and by KMCM, especially in the absence of non-AF arrhythmias or when combinations of measurements are used.

Diagnosis of atrial fibrillation based on arterial pulse wave foot point detection using artificial neural networks

BACKGROUND

Atrial fibrillation (AF) is a common arrhythmia that is strongly related to the risk of stroke. Some methods in the literature approach AF diagnosis based on cardiovascular signals of several minutes in length. However, many traditional methods utilized to monitor health status in terms of AF rely on electrocardiograms, which are time consuming and require specialized equipment to collect. By contrast, more practical systems focus on noninvasively collected short-term cardiovascular signals, such as arterial pulse waveforms (APWs).

METHODS

In this paper, an AF diagnosis algorithm based on the processing of parameters extracted from short-length heart period (HP) measures is proposed. The HP is obtained by locating foot points (FPOs) in 10-second epochs of APW signals. The algorithm consists of two main stages. First, five parameters representative of the APW morphology are extracted to train an artificial neural network (ANN) model for FPO detection. The moving interpolation difference method and an improved second derivative maximum method are employed for APW parameter extraction. Second, 13 temporal-domain, frequency-domain and nonlinear HP parameters are extracted from the previously identified FPOs. These are subsequently orthogonalized using principal component analysis to train a second ANN for effective AF diagnosis.

RESULTS

Both ANNs were trained and validated on a labeled data set with 20-fold cross-validation, achieving a mean sensitivity and specificity of 97.53% and 90.13%, respectively, for AF diagnosis and an F1 score of 99.18% for FPO identification.

CONCLUSIONS

This paper presents a validated solution for the diagnosis of AF from APW records using parameters derived from HP measures. In addition, compared to that of a commercial BP+ device, improved FPO detection performance is achieved, making the proposed algorithm a strong candidate for the automatic detection of FPOs in oscillometric devices.

Pulse rate variability predicts atrial fibrillation and cerebrovascular events in a large, population-based cohort

BACKGROUND

Many patients with atrial fibrillation (AF) present with stroke as their first clinical manifestation and since improved AF screening methods are thus required, we investigated whether pulse rate variability parameters predict future AF and cerebrovascular events.

METHODS

In an observational cohort study of 5000 community-resident adults (58% male; 50-84 years), the beat-to-beat variability of suprasystolic brachial blood pressure waveforms was measured with root mean square of successive differences (RMSSD) and irregularity index (IrrIx). Based on outcome-oriented and previously validated thresholds for detecting AF, RMSSD and IrrIx were dichotomised at 100 ms and 7.7%, respectively. Participants were followed up for 4.6 years (median), accruing 249 AF and 120 cerebrovascular events in the total sample (n = 5000), and 133 AF and 90 cerebrovascular events among those without prior AF diagnosis (n = 4296).

RESULTS

In multivariable-adjusted analyses, an elevated RMSSD (>100 ms) or IrrIx (>7.7%) was strongly associated with a higher risk of AF (hazard ratios (HRs) = 2.00-2.95) and cerebrovascular events (HRs = 1.91-2.28), even among people without prior AF diagnosis: HRs for AF = 1.70-2.05 and cerebrovascular events = 2.00-2.28. These associations were strongest in the highest RMSSD tertile >100 ms or IrrIx tertile >7.7%: HRs for AF = 2.32-4.47 and cerebrovascular events = 2.43-3.69. Among those without prior AF diagnosis, the highest categorical net reclassification improvement for 5-year cerebrovascular risk was 14% (95% confidence interval: 7-21%).

CONCLUSIONS

Elevated RMSSD or IrrIx values indicative of the presence of AF predict future AF and cerebrovascular events; more so with increasing pulse irregularity and even among those without prior AF diagnosis.