Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation A Systematic Review and Meta-analysis

Question What is the overall accuracy of smartphone camera applications that diagnose and screen for atrial fibrillation (AF)? Findings In this meta-analysis of 10 primary diagnostic accuracy studies with 3852 participants, all applications that used photoplethysmography signals to diagnose AF had high sensitivity and specificity. However, the modeled positive predictive value for screening an asymptomatic population aged 65 years and older with a history of hypertension was approximately 20% to 40%, although the negative predictive value was near 100%. Meaning In this study, smartphone camera applications had high sensitivity and specificity for diagnosing AF and appeared adequate for ruling out AF, but their modest positive predictive value suggests that these devices will generate a higher number of false-positive than true-positive results. This systematic review and meta-analysis assesses the accuracy of smartphone camera applications that diagnose atrial fibrillation among US adults aged 65 years and older. Importance Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear. Objective To determine the accuracy of smartphone camera applications that diagnose AF. Data Sources and Study Selection MEDLINE and Embase were searched until January 2019 for studies that assessed the accuracy of any smartphone applications that use the smartphone's camera to measure the amplitude and frequency of the user's fingertip pulse to diagnose AF. Data Extraction and Synthesis Bivariate random-effects meta-analyses were constructed to synthesize data. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy Studies reporting guideline. Main Outcomes and Measures Sensitivity and specificity were measured with bivariate random-effects meta-analysis. To simulate the use of these applications as a screening tool, the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (ie, age >= 65 years and age >= 65 years with hypertension) were modeled. Lastly, the association of methodological limitations with outcomes were analyzed with sensitivity analyses and metaregressions. Results A total of 10 primary diagnostic accuracy studies, with 3852 participants and 4 applications, were included. The oldest studies were published in 2016 (2 studies [20.0%]), while most studies (4 [40.0%]) were published in 2018. The applications analyzed the pulsewave signal for a mean (range) of 2 (1-5) minutes. The meta-analyzed sensitivity and specificity for all applications combined were 94.2% (95% CI, 92.2%-95.7%) and 95.8% (95% CI, 92.4%-97.7%), respectively. The PPV for smartphone camera applications detecting AF in an asymptomatic population aged 65 years and older was between 19.3% (95% CI, 19.2%-19.4%) and 37.5% (95% CI, 37.4%-37.6%), and the NPV was between 99.8% (95% CI, 99.83%-99.84%) and 99.9% (95% CI, 99.94%-99.95%). The PPV and NPV increased for individuals aged 65 years and older with hypertension (PPV, 20.5% [95% CI, 20.4%-20.6%] to 39.2% [95% CI, 39.1%-39.3%]; NPV, 99.8% [95% CI, 99.8%-99.8%] to 99.9% [95% CI, 99.9%-99.9%]). There were methodological limitations in a number of studies that did not appear to be associated with diagnostic performance, but this could not be definitively excluded given the sparsity of the data. Conclusions and Relevance In this study, all smartphone camera applications had relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, suggesting that using these applications in an asymptomatic population may generate a higher number of false-positive than true-positive results. Future research should address the accuracy of these applications when screening other high-risk population groups, their ability to help monitor chronic AF, and, ultimately, their associations with patient-important outcomes.