Mobile apps improve atrial fibrillation detection rates in a low-risk population

Atrial fibrillation and normal or abnormal heart rate rythm

A new study has shown that mobile health (mHealth) devices are able to screen for the common cardiac disorder called atrial fibrillation (AF).

This condition which affects about a million people in the UK denotes an irregular heartbeat (called arrhythmia) originating in the abnormal fluttering contraction of the heart’s upper chambers, or atria. In contrast, the normal heart has a regular “sinus rhythm”, triggered by the regular electrical impulses from the sino-atrial (SA) node in the right atrium.

Many causes can cause atrial fibrillation, but the outcome is an abnormally random and sometimes extremely rapid heart rate which prevents the heart from fulfilling its pumping action. Since each beat occurs at no fixed interval from the last, and often at a very short interval, the heart cannot get time to relax and receive the infilling blood. This reduces the volume of blood pumped out of the heart and therefore the supply of oxygen to the whole body.

Another major complication of AF is the propensity to cause blood clots due to the stagnation of blood in the atria. The fluttering of the atrial wall muscle, in contrast to the normal vigorous and coordinated contraction of a healthy heart, fails to eject blood into the ventricles or lower heart chambers, and encourages stasis of blood, which in turn causes clots to form which eventually break off. These clots or clot fragments are then carried down to the ventricles and out through the large arteries, to lodge in the body veins or in the lungs, depending on which ventricle they are pumped from.

Thus AF is associated with a high risk of stroke due to a clot fragment in a brain vessel, lung embolism (blockage of a lung vessel by a clot), of death, of heart failure and dementia. However, the lack of early symptoms and failure to take medication as required contribute to the difficulty of managing this condition properly.

Screening for AF

Several screening techniques have been tested but do not show much benefit in terms of preventing complications of AF. Single-lead ECG approaches have been able to increase the detection rates of new-onset AF in high-risk groups, but without obvious impact on adverse sequelae like stroke or embolism. As a result, community-level screening is not recommended by public health organizations.

Brief screenings using ECG may miss most cases of AF when it does not occur continuously, so long-term observation is necessary. One study looked at the use of an adhesive skin patch which could provide ECG readings for 14 days, and found a better percentage of detection compared with delayed monitoring. However, it was not acceptable to a good proportion of participants and had to be discontinued.

The use of a smartwatch strap with a sensor for ECG recording is a better method for continuous monitoring, but only 66% of the signals picked up can be interpreted by the app algorithm in isolation, requiring modifications to enhance signal quality and eliminate motion artifacts.

The new study looked at the current use of fitness trackers, smart watches and mobile phones to help people remain healthy. It concludes that using a technique called photoplethysmography (PPG), it is possible to make a diagnosis of AF in a non-invasive and inexpensive manner.

PPG offers a reliable solution when coupled with a smart device. PPG is a simple technique based on optics, which senses changes in the volume of blood flowing through the microscopic network of blood vessels in living tissue. It is used in many settings to measure blood flow at the skin surface.

How was the study done?

The current study explored the success of the mAF App which is an AF mobile platform that brings together tools to support clinical decision making, treatment based on medical guidelines, educational materials and self-monitoring and self-care strategies, with follow up. The app can be downloaded and used with any compatible mobile device. The aim was to screen a low-risk population for AF and analyze the incidence of AF as well as the percentage of patients who were successfully treated with anticoagulation.

The researchers looked at the feasibility of using smart devices equipped with PPG technology for this screening, followed by adoption of a standard course of clinical management if AF was detected.

The study included almost 190 000 participants with smart mHealth devices which measured the pulse rate and rhythm for 14 days or longer. All participants were 18 years or above. Almost 92% of the signals were confirmed to be AF, showing the high sensitivity of this method. The number of patients who were suspected to have AF following this initial screening was about 420 (0.23%).

However, above 55 years the detection rate was much higher, at 2.62%, showing the validity of this tool in an older population with assumed higher risk as well.

Among this number, examination by qualified persons showed that AF was present in 87% of the suspected cases, for a yield of 227 from the entire group. These patients were then managed with integrated care using “the ABC (‘A’ Avoid stroke, ‘B’ Better symptom management, and ‘C’ Cardiovascular risk and comorbidity management) pathway”. Clot-dissolving therapy was successful in about 80%.

What did the study show?

Researcher Yutao Guo says, “Based on our present study, continuous home-monitoring with smart device based PPG technology could be a feasible, cost-effective approach for AF screening. This would help efforts at screening and detection of AF, as well as early interventions to reduce stroke and other AF-related complications.” This approach combines comfort, quality and continuity of signal monitoring without motion artifacts, allowing high predictive value for AF using smart devices.

The study is published in the Journal of the American College of Cardiology.

Journal reference:

Mobile health technology for atrial fibrillation screening using photoplethysmography-based smart devices: the HUAWEI heart study. Yutao Guo, Hao Wang, Hui Zhang, Tong Liu, Zhaoguang Liang, Yunlong Xia, Li Yan, Yunli Xing, Haili Shi, Shuyan Li, Yanxia Liu, FanLiu, Mei Feng, Yundai Chen, & Gregory Y.H. Lip. https://doi.org/10.1016/j.jacc.2019.08.019