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Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial

Abstract

Digital smart devices have the capability of detecting atrial fibrillation (AF), but the efficacy of this type of digital screening has not been directly compared to usual care for detection of treatment-relevant AF. In the eBRAVE-AF trial (NCT04250220), we randomly assigned 5,551 policyholders of a German health insurance company who were free of AF at baseline (age 65 years (median; interquartile range (11) years, 31% females)) to digital screening (n = 2,860) or usual care (n = 2,691). In this siteless trial, for digital screening, participants used a certified app on their own smartphones to screen for irregularities in their pulse waves. Abnormal findings were evaluated by 14-day external electrocardiogram (ECG) loop recorders. The primary endpoint was newly diagnosed AF within 6 months treated with oral anti-coagulation by an independent physician not involved in the study. After 6 months, participants were invited to cross-over for a second study phase with reverse assignment for secondary analyses. The primary endpoint of the trial was met, as digital screening more than doubled the detection rate of treatment-relevant AF in both phases of the trial, with odds ratios of 2.12 (95% confidence interval (CI), 1.19–3.76; P= 0.010) and 2.75 (95% CI, 1.42–5.34; P = 0.003) in the first and second phases, respectively. This digital screening technology provides substantial benefits in detecting AF compared to usual care and has the potential for broad applicability due to its wide availability on ordinary smartphones. Future studies are needed to test whether digital screening for AF leads to better treatment outcomes.

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Fig. 1: CONSORT diagram.
Fig. 2: Cumulative event rates for the primary and secondary endpoints.
Fig. 3: Time-dependent association of AF and abnormal PPG measurements with MACCE.

Data availability

The data are not publicly available because they contain information of electronic health records and health insurance data, consented for research use by the eBRAVE-AF investigators. Making the data publicly available without additional consent or ethical approval might compromise patient privacy and the original ethical approval. If other investigators are interested in performing additional analyses, requests can be made to the corresponding authors (A.B. and S.M.), and analyses will be performed in collaboration with LMU Munich.

Code availability

The source code of the PPG analysis algorithm cannot be made publicly available because it is a certified medical product and the intellectual property of a medical company. Analysis of raw PPGs (for example, PPGs recorded by other devices) with the software used in this study is possible. Requests can be made to Preventicus GmbH. For data analysis, CRAN R version 4.2.1 was used with the base packages and the following additional CRAN packages: survival, survminer, rms, dplyr, ggplot2, svglite, stringr and timereg. Custom scripts can be accessed upon request for invitation via https://bitbucket.org/krizas/ebrave-af-scripts/src/master/.

References

  1. Kornej, J., Benjamin, E. J. & Magnani, J.W. Atrial fibrillation: global burdens and global opportunities. Heart https://doi.org/10.1136/heartjnl-2020-318480 (2021).

  2. Krijthe, B. P. et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur. Heart J. 34, 2746–2751 (2013).

    Article  Google Scholar 

  3. Hindricks, G. et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC. Eur. Heart J. 42, 373–498 (2021).

    Article  Google Scholar 

  4. Turakhia, M. P. et al. Estimated prevalence of undiagnosed atrial fibrillation in the United States. PLoS ONE 13, e0195088 (2018).

    Article  Google Scholar 

  5. Sposato, L. A. et al. Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. Lancet Neurol. 14, 377–387 (2015).

    Article  Google Scholar 

  6. Benjamin, E. J. et al. Research priorities in atrial fibrillation screening: a report from a National Heart, Lung, and Blood Institute virtual workshop. Circulation 143, 372–388 (2021).

    Article  Google Scholar 

  7. Perez, M. V. et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N. Engl. J. Med. 381, 1909–1917 (2019).

    Article  Google Scholar 

  8. Guo, Y. et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J. Am. Coll. Cardiol. 74, 2365–2375 (2019).

    Article  Google Scholar 

  9. Lubitz, S. A. et al. Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study. Circulation 144, E570–E571 (2021).

    Google Scholar 

  10. Halcox, J. P. J. et al. Assessment of remote heart rhythm sampling using the alivecor heart monitor to screen for atrial fibrillation: the REHEARSE-AF study. Circulation 136, 1784–1794 (2017).

    Article  Google Scholar 

  11. Lubitz, S. A. et al. Screening for atrial fibrillation in older adults at primary care visits: VITAL-AF randomized controlled trial. Circulation 145, 946–954 (2022).

    CAS  Article  Google Scholar 

  12. Svennberg, E. et al. Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): a multicentre, parallel group, unmasked, randomised controlled trial. Lancet 398, 1498–1506 (2021).

    Article  Google Scholar 

  13. Freyer, L. et al. Rationale and design of a digital trial using smartphones to detect subclinical atrial fibrillation in a population at risk: the eHealth-based Bavarian Alternative Detection of Atrial Fibrillation (eBRAVE-AF) trial. Am. Heart J. 241, 26–34 (2021).

    Article  Google Scholar 

  14. Lubitz, S. et al. Detection of atrial fibrillation in a large population using wearable devices: the Fitbit Heart Study. Circulation 144, 25 (2021).

    Google Scholar 

  15. Smartphone adoption rate worldwide in 2020 and 2025, by region. https://www.statista.com/statistics/1258906/worldwide-smartphone-adoption-rate-telecommunication-by-region/:statista (2022).

  16. Svendsen, J. H. et al. Implantable loop recorder detection of atrial fibrillation to prevent stroke (The LOOP Study): a randomised controlled trial. Lancet 398, 1507–1516 (2021).

    CAS  Article  Google Scholar 

  17. US Preventive Services Task Force et al. Screening for atrial fibrillation: US Preventive Services Task Force recommendation statement. JAMA 327, 360–367 (2022).

    Article  Google Scholar 

  18. Kaplan, R. M. et al. Stroke risk as a function of atrial fibrillation duration and CHA2DS2-VASc score. Circulation 140, 1639–1646 (2019).

    Article  Google Scholar 

  19. Chen, L. Y. et al. Atrial fibrillation burden: moving beyond atrial fibrillation as a binary entity: a scientific statement from the American Heart Association. Circulation 137, e623–e644 (2018).

    Article  Google Scholar 

  20. Anter, E., Jessup, M. & Callans, D. J. Atrial fibrillation and heart failure: treatment considerations for a dual epidemic. Circulation 119, 2516–2525 (2009).

    Article  Google Scholar 

  21. Soliman, E. Z. et al. Atrial fibrillation and risk of ST–segment–elevation versus non–ST–segment–elevation myocardial infarction: the Atherosclerosis Risk in Communities (ARIC) study. Circulation 131, 1843–1850 (2015).

    Article  Google Scholar 

  22. Massicotte–Azarniouch, D. et al. Incident atrial fibrillation and the risk of congestive heart failure, myocardial infarction, end-stage kidney disease, and mortality among patients with a decreased estimated GFR. Am. J. Kidney Dis. 71, 191–199 (2018).

    Article  Google Scholar 

  23. Lutsey, P. L. et al. Atrial fibrillation and venous thromboembolism: evidence of bidirectionality in the Atherosclerosis Risk in Communities Study. J. Thromb. Haemost. 16, 670–679 (2018).

    CAS  Article  Google Scholar 

  24. Conen, D. et al. Risk of malignant cancer among women with new-onset atrial fibrillation. JAMA Cardiol. 1, 389–396 (2016).

    Article  Google Scholar 

  25. Kornej, J., Borschel, C. S., Benjamin, E. J. & Schnabel, R. B. Epidemiology of atrial fibrillation in the 21st century: novel methods and new insights. Circ. Res. 127, 4–20 (2020).

    CAS  Article  Google Scholar 

  26. Zens, M. et al. Development of a modular research platform to create medical observational studies for mobile devices. JMIR Res. Protoc. 6, e99 (2017).

    Article  Google Scholar 

  27. Matsumoto, M. & Nishimura, T. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8, 3–30 (1998).

    Article  Google Scholar 

  28. Koenig, N. et al. Validation of a new heart rate measurement algorithm for fingertip recording of video signals with smartphones. Telemed. J. E Health 22, 631–636 (2016).

    Article  Google Scholar 

  29. Krivoshei, L. et al. Smart detection of atrial fibrillation. Europace 19, 753–757 (2017).

    PubMed  Google Scholar 

  30. Brasier, N. et al. Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). Europace 21, 41–47 (2019).

    Article  Google Scholar 

  31. Svennberg, E. et al. Mass screening for untreated atrial fibrillation: the STROKESTOP study. Circulation 131, 2176–2184 (2015).

    Article  Google Scholar 

  32. Steinhubl, S. R. et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: the mSToPS randomized clinical trial. JAMA 320, 146–155 (2018).

    Article  Google Scholar 

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Acknowledgements

The authors thank Versicherungskammer Bayern (VKB) for the possibility to conduct the study. In particular, the authors thank B. Daschner of VKB. Without her continuous support, the completion of the study would not have been possible.

Author information

Authors and Affiliations

Authors

Contributions

A.B., S.M. and K.D.R. designed the study and analyzed and interpreted the data. A.B. and K.D.R. wrote the protocol. L.v.S. did the technical implementation and served as project manager. L.F. coordinated the clinical conduct of the study. M.Z. and L.v.S. were responsible for customizing the study app (eBRAVE-AF app). Data acquisition was done by all authors. U.M. and K.D.R. did the statistical analyses. A.B. wrote the first draft, with input from K.D.R., S.M. and U.M., and submitted the final version for publication. A.B., K.D.R., U.M. and S.M. had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors reviewed the final draft and agree with its content and conclusions. A.B. and S.M. contributed equally.

Corresponding authors

Correspondence to Steffen Massberg or Axel Bauer.

Ethics declarations

Competing interests

All authors declare no competing interests with respect to the study or the products investigated in the study. None of the authors has a financial, academic or intellectual property conflict of interest with the mobile app company Preventicus.

Peer review

Peer review information

Nature Medicine thanks Mohamed Elshazly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Michael Basson, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study app and PPG app.

(a) shows a screenshot from the study app, (b-c) show app-based PPG self-measurements. (b) shows regular pulse wave sequence. (c) shows a rapid and irregular pulse wave sequence indicative of atrial fibrillation.

Extended Data Fig. 2 AF detection during digital screening.

Quantity and quality of PPG measurements in participants with usually detected AF during digital screening.

Extended Data Fig. 3 AF diagnosis following abnormal PPGs.

AF diagnosis (or lack thereof) following abnormal PPGs.

Extended Data Fig. 4 Details of PPG measurements.

(a-b): Plethysmographic (PPG) measurements during both phases of the trial (panel A phase 1; panel B phase 2). Solid blue line shows proportion of participants who performed ≥1 measurement per week, dotted blue line shows proportion of participants who performed ≥2 measurements per week. The percentage refers to the total number of subjects assigned to digital screening according to the modified intention-to-treat principle. The drop in PPG measurements at the end of the second study phase is due to the shorter average measurement duration in this phase, as app usage was limited to a total of 12 months and participants took a median of 10 days (IQR 22 days) to cross-over. (c-d): Number of total measurements performed by participants during the study stratified by age groups (< 59 yeas; 59 – 64 years; 65 – 71 years and > 71 years) for group 1 (c) and group 2 (d). For this analysis only active participants performing at least one PPG-measurement during the entire study duration were considered (Group 1 N = 2,484; age < 59 N = 549; age 59 – 64 N = 616; age 65 – 71 N = 787; age > 71 N = 532; Group 2 N = 2,140; age < 59 N = 452; age 59 – 64 N = 502; age 65 – 71 N = 739; age > 71 N = 447). Two-sided p-values were calculated by means of Kruskal-Wallis one-way analysis of variance; the exact p-value for (c) is 9.62 * 10−9; differences were considered statistically significant when the two-sided p-value was less than 0.05. There was no adjustment for multiple comparisons. (e-f): Number of total measurements performed by males and females during the study for group 1 (e) and group 2 (f). For this analysis only active participants performing at least one PPG-measurement during the entire study duration were considered (Group 1 N = 2,484; females N = 767; males = 1,717; Group 2 N = 2,140; females N = 658; males N = 1,482). Two-sided p-values were calculated by means of Mann-Whitney U test; the exact p-value for (e) is 1.76 * 10−5; differences were considered statistically significant when the two-sided p-value was less than 0.05. There was no adjustment for multiple comparisons. Boxplots in (c-f) show median and IQR. The whisker boundaries define the 10th and 90th percentiles.

Extended Data Fig. 5 Detection of treatment-relevant AF as function of PPG measurement frequency and CHA2DS2-VASc-Score.

Odds ratio for reaching the primary endpoint as function of number of photoplethysmographic (PPG) measurements and CHA2DS2-VASc-Score. Darker colors represent higher odds ratios. The red vertical line marks the number of the 76 scheduled PPG measurements per participant over the six months digital screening phase, the blue line marks the median of the 53 PPG measurements per participant observed in the study.

Extended Data Fig. 6 Sensitivity of AF detection as function of PPG measurement frequency and AF burden.

Model describing the weekly sensitivity for detecting atrial fibrillation (AF) as a function of the number of measurements per week and the AF-burden. We used the timestamps of the available 300,509 PPG-measurements in order to calculate the frequency of measurements for the various parts of the day. Based on this frequency pattern we subsequently simulated PPG-measurements for increasing AF-burden (we consequently increased AF-burden from 1 h/week, [0.69% AF-burden] to 168 h / week [100% AF-burden] by 1 h steps and performed 10.000 simulations for each AF-burden step), which was defined as and finally calculated the sensitivity of the AF-detection. For this model, 89% sensitivity of the PPG-algorithm was assumed. With 2 measurements / day (according to the study protocol for the first 2 weeks) an AF-burden > 12.5% would be required to detect AF with > 80% sensitivity in one week. With 2 measurements / week (according to the study protocol from week 3 to month 6) an AF-burden > 62.5% would be required to detect AF with > 80% sensitivity in one week.

Extended Data Table 1 Modes of AF diagnoses
Extended Data Table 2 Causes of death
Extended Data Table 3 Components of MACCE
Extended Data Table 4 Per-protocol analysis for the primary endpoint

Supplementary information

Supplementary Information

Supplementary Tables 1–3

Reporting Summary

Supplementary Data

Study protocol and statistical analysis plan

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Rizas, K.D., Freyer, L., Sappler, N. et al. Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat Med 28, 1823–1830 (2022). https://doi.org/10.1038/s41591-022-01979-w

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