Three year clinical outcomes in a nationwide, observational, siteless clinical trial of atrial fibrillation screening-mHealth Screening to Prevent Strokes (mSToPS)

Steven R Steinhubl, Jill Waalen, Anirudh Sanyal, Alison M Edwards, Lauren M Ariniello, Gail S Ebner, Katie Baca-Motes, Robert A Zambon, Troy Sarich, Eric J Topol, Steven R Steinhubl, Jill Waalen, Anirudh Sanyal, Alison M Edwards, Lauren M Ariniello, Gail S Ebner, Katie Baca-Motes, Robert A Zambon, Troy Sarich, Eric J Topol

Abstract

Background: Atrial fibrillation (AF) is common, often without symptoms, and is an independent risk factor for mortality, stroke and heart failure. It is unknown if screening asymptomatic individuals for AF can improve clinical outcomes.

Methods: mSToPS was a pragmatic, direct-to-participant trial that randomized individuals from a single US-wide health plan to either immediate or delayed screening using a continuous-recording ECG patch to be worn for two weeks and 2 occasions, ~3 months apart, to potentially detect undiagnosed AF. The 3-year outcomes component of the trial was designed to compare clinical outcomes in the combined cohort of 1718 individuals who underwent monitoring and 3371 matched observational controls. The prespecified primary outcome was the time to first event of the combined endpoint of death, stroke, systemic embolism, or myocardial infarction among individuals with a new AF diagnosis, which was hypothesized to be the same in the two cohorts but was not realized.

Results: Over the 3 years following the initiation of screening (mean follow-up 29 months), AF was newly diagnosed in 11.4% (n = 196) of screened participants versus 7.7% (n = 261) of observational controls (p<0.01). Among the screened cohort with incident AF, one-third were diagnosed through screening. For all individuals whose AF was first diagnosed clinically, a clinical event was common in the 4 weeks surrounding that diagnosis: 6.6% experienced a stroke,10.2% were newly diagnosed with heart failure, 9.2% had a myocardial infarction, and 1.5% systemic emboli. Cumulatively, 42.9% were hospitalized. For those diagnosed via screening, none experienced a stroke, myocardial infarction or systemic emboli in the period surrounding their AF diagnosis, and only 1 person (2.3%) had a new diagnosis of heart failure. Incidence rate of the prespecified combined primary endpoint was 3.6 per 100 person-years among the actively monitored cohort and 4.5 per 100 person-years in the observational controls.

Conclusions: At 3 years, screening for AF was associated with a lower rate of clinical events and improved outcomes relative to a matched cohort, although the influence of earlier diagnosis of AF via screening on this finding is unclear. These observational data, including the high event rate surrounding a new clinical diagnosis of AF, support the need for randomized trials to determine whether screening for AF will yield a meaningful protection from strokes and other clinical events.

Trail registration: The mHealth Screening To Prevent Strokes (mSToPS) Trial is registered on ClinicalTrials.gov with the identifier NCT02506244.

Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Steve Steinhubl – NIH UL1TR002550 grant support; Qualcomm Foundation. Jill Waalen – No conflicts of interest Anirudh Sanyal - Employee, Healthagen, LLC Alison Edwards – Employee, Healthagen, LLC Lauren Ariniello - NIH UL1TR002550 grant support Gail Ebner - NIH UL1TR002550 grant support; Qualcomm Foundation Katie Baca-Motes - No conflicts of interest Bob Zambon- Employee, Janssen Pharmaceuticals (a subsidiary of Johnson & Johnson); Stockholder Johnson & Johnson. Troy Sarich – Employee, Johnson & Johnson; Stockholder Johnson & Johnson. Eric Topol – NIH UL1TR002550 grant support; Qualcomm Foundation This support does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1. CONSORT flow diagram.
Fig 1. CONSORT flow diagram.
Flow of participants beginning with all potentially eligible individuals, those enrolled and then those included in the 4 month, 1-year and 3-year analyses.
Fig 2. Atrial fibrillation diagnosis.
Fig 2. Atrial fibrillation diagnosis.
A.) Cumulative Probability of a diagnosis of atrial fibrillation in the 3 years following the initiating of monitoring. B.) Rate of new diagnosis of atrial fibrillation in the monitored and observational cohorts after completion of active monitoring. All new diagnoses occurring annually after 6 months from the initiation of screening.
Fig 3. Primary endpoint.
Fig 3. Primary endpoint.
Cumulative incidence of the combined primary endpoint of death, stroke, systemic emboli or myocardial infarction in actively monitored and observational control cohorts over the 3 years following initiation of screening.
Fig 4. Primary endpoint in those receiving…
Fig 4. Primary endpoint in those receiving a new diagnosis of atrial fibrillation.
Cumulative incidence of the combined primary endpoint in individuals diagnosed with atrial fibrillation in A.) the actively monitored and observational control cohorts, and B.) the actively monitored cohort based on whether their initial diagnosis of AF was via ECG patch screening or via a clinical diagnosis, and the observational control cohort.
Fig 5. Clinical events surrounding a new…
Fig 5. Clinical events surrounding a new atrial fibrillation diagnosis.
Clinical Events in the two weeks preceding and the 2 weeks following a new atrial fibrillation (AF) diagnosis, inclusive of the diagnosis date, in the observational cohort and the actively monitored cohort based on whether their initial diagnosis of AF was via ECG patch screening or via a clinical diagnosis.

References

    1. Weng LC, Preis SR, Hulme OL, Larson MG, Choi SH, Wang B, et al.. Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation. Circulation. 2018;137(10):1027–38. Epub 2017/11/14. doi: 10.1161/CIRCULATIONAHA.117.031431 ; PubMed Central PMCID: PMC5840011.
    1. Lippi G, Sanchis-Gomar F, Cervellin G. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Int J Stroke. 2021;16(2):217–21. Epub 2020/01/21. doi: 10.1177/1747493019897870 .
    1. Turakhia MP, Shafrin J, Bognar K, Trocio J, Abdulsattar Y, Wiederkehr D, et al.. Estimated prevalence of undiagnosed atrial fibrillation in the United States. PloS one. 2018;13(4):e0195088–e. doi: 10.1371/journal.pone.0195088 .
    1. Reiffel JA, Verma A, Kowey PR, Halperin JL, Gersh BJ, Wachter R, et al.. Incidence of Previously Undiagnosed Atrial Fibrillation Using Insertable Cardiac Monitors in a High-Risk Population: The REVEAL AF Study. JAMA Cardiology. 2017;2(10):1120–7. doi: 10.1001/jamacardio.2017.3180
    1. Odutayo A, Wong CX, Hsiao AJ, Hopewell S, Altman DG, Emdin CA. Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: systematic review and meta-analysis. BMJ (Clinical research ed). 2016;354:i4482. Epub 2016/09/08. doi: 10.1136/bmj.i4482.
    1. Piccini JP, Hammill BG, Sinner MF, Hernandez AF, Walkey AJ, Benjamin EJ, et al.. Clinical course of atrial fibrillation in older adults: the importance of cardiovascular events beyond stroke. Eur Heart J. 2014;35(4):250–6. Epub 2013/11/28. doi: 10.1093/eurheartj/eht483 ; PubMed Central PMCID: PMC3896863.
    1. Yao X, Gersh BJ, Sangaralingham LR, Shah ND, Noseworthy PA. Risk of cardiovascular events and incident atrial fibrillation in patients without prior atrial fibrillation: Implications for expanding the indications for anticoagulation. American heart journal. 2018;199:137–43. Epub 2018/05/15. doi: 10.1016/j.ahj.2018.02.005 .
    1. Jaakkola J, Mustonen P, Kiviniemi T, Hartikainen JE, Palomäki A, Hartikainen P, et al.. Stroke as the First Manifestation of Atrial Fibrillation. PLoS One. 2016;11(12):e0168010. Epub 2016/12/10. doi: 10.1371/journal.pone.0168010; PubMed Central PMCID: PMC5148080
    1. Wang TJ, Larson MG, Levy D, Vasan RS, Leip EP, Wolf PA, et al.. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation. 2003;107(23):2920–5. Epub 2003/05/29. doi: 10.1161/01.CIR.0000072767.89944.6E .
    1. Mairesse GH, Moran P, Van Gelder IC, Elsner C, Rosenqvist M, Mant J, et al.. Screening for atrial fibrillation: a European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLAECE). Europace. 2017;19(10):1589–623. Epub 2017/10/20. doi: 10.1093/europace/eux177 .
    1. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, et al.. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS). Eur Heart J. 2020. Epub 2020/08/30. doi: 10.1093/eurheartj/ehaa612
    1. Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, et al.. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA. 2018;320(2):146–55. doi: 10.1001/jama.2018.8102 .
    1. Waalen J, Edwards AM, Sanyal A, Zambon RA, Ariniello L, Ebner GS, et al.. Healthcare resource utilization following ECG sensor patch screening for atrial fibrillation. Heart Rhythm O2. 2020;1(5):351–8. Epub 2021/06/12. doi: 10.1016/j.hroo.2020.09.005 ; PubMed Central PMCID: PMC8183948.
    1. Baca-Motes K, Edwards AM, Waalen J, Edmonds S, Mehta RR, Ariniello L, et al.. Digital recruitment and enrollment in a remote nationwide trial of screening for undiagnosed atrial fibrillation: Lessons from the randomized, controlled mSToPS trial. Contemp Clin Trials Commun. 2019;14:100318. Epub 2019/01/19. doi: 10.1016/j.conctc.2019.100318; PubMed Central PMCID: PMC6329362.
    1. Steinhubl SR, Mehta RR, Ebner GS, Ballesteros MM, Waalen J, Steinberg G, et al.. Rationale and design of a home-based trial using wearable sensors to detect asymptomatic atrial fibrillation in a targeted population: The mHealth Screening To Prevent Strokes (mSToPS) trial. American heart journal. 2016;175:77–85. Epub 2016/05/18. doi: 10.1016/j.ahj.2016.02.011 .
    1. Alonso A, Agarwal SK, Soliman EZ, Ambrose M, Chamberlain AM, Prineas RJ, et al.. Incidence of atrial fibrillation in whites and African-Americans: the Atherosclerosis Risk in Communities (ARIC) study. American heart journal. 2009;158(1):111–7. doi: 10.1016/j.ahj.2009.05.010 .
    1. Wong JA, Conen D, Van Gelder IC, McIntyre WF, Crijns HJ, Wang J, et al.. Progression of Device-Detected Subclinical Atrial Fibrillation and the Risk of Heart Failure. Journal of the American College of Cardiology. 2018;71(23):2603–11. doi: 10.1016/j.jacc.2018.03.519
    1. Mikkelsen AP, Hansen ML, Olesen JB, Hvidtfeldt MW, Karasoy D, Husted S, et al.. Substantial differences in initiation of oral anticoagulant therapy and clinical outcome among non-valvular atrial fibrillation patients treated in inpatient and outpatient settings. Europace. 2016;18(4):492–500. Epub 2015/10/08. doi: 10.1093/europace/euv242 .
    1. Gladstone DJ, Wachter R, Schmalstieg-Bahr K, Quinn FR, Hummers E, Ivers N, et al.. Screening for Atrial Fibrillation in the Older Population: A Randomized Clinical Trial. JAMA Cardiol. 2021;6(5):558–67. Epub 2021/02/25. doi: 10.1001/jamacardio.2021.0038 ; PubMed Central PMCID: PMC7905702
    1. EHRA 2021: The STROKESTOP-Study Radcliffe Cardiology: Radcliffe Cardiology; 2021. Available from: .
    1. Khurshid S, Healey JS, McIntyre WF, Lubitz SA. Population-Based Screening for Atrial Fibrillation. Circulation research. 2020;127(1):143–54. doi: 10.1161/CIRCRESAHA.120.316341
    1. Quer G, Freedman B, Steinhubl SR. Screening for atrial fibrillation: predicted sensitivity of short, intermittent electrocardiogram recordings in an asymptomatic at-risk population. Europace. 2020;22(12):1781–7. Epub 2020/10/01. doi: 10.1093/europace/euaa186 ; PubMed Central PMCID: PMC7758473.
    1. Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al.. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation. 2021;143(13):1287–98. Epub 2021/02/17. doi: 10.1161/CIRCULATIONAHA.120.047829 ; PubMed Central PMCID: PMC7996054.
    1. Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation. JAMA network open. 2020;3(1):e1919396–e. doi: 10.1001/jamanetworkopen.2019.19396
    1. Guo Y, Lane DA, Wang L, Zhang H, Wang H, Zhang W, et al.. Mobile Health Technology to Improve Care for Patients With Atrial Fibrillation. Journal of the American College of Cardiology. 2020;75(13):1523–34. Epub 2020/04/04. doi: 10.1016/j.jacc.2020.01.052 .
    1. Guimarães PO, Krishnamoorthy A, Kaltenbach LA, Anstrom KJ, Effron MB, Mark DB, et al.. Accuracy of Medical Claims for Identifying Cardiovascular and Bleeding Events After Myocardial Infarction: A Secondary Analysis of the TRANSLATE-ACS Study. JAMA cardiology. 2017;2(7):750–7. doi: 10.1001/jamacardio.2017.1460 .

Source: PubMed

3
Abonnere