Skip to main navigation menu Skip to main content Skip to site footer

Original article

Vol. 151 No. 0708 (2021)

The SilenT AtRial FIBrillation (STAR-FIB) study programme – design and rationale

Cite this as:
Swiss Med Wkly. 2021;151:w20421



Anticoagulation of patients with screen-detected atrial fibrillation may prevent ischaemic strokes. The STAR-FIB study programme aims to determine the age- and sex-specific prevalence of silent atrial fibrillation and to develop a clinical prediction model to identify patients at risk of undiagnosed atrial fibrillation in a hospitalised patient population.


The STAR-FIB study programme includes a prospective cohort study and a case-control study of hospitalised patients aged 65–84 years, evenly distributed for both age and sex. We recruited 795 patients without atrial fibrillation for the cohort study (49.2% females; median age 74.8 years). All patients had three serial 7-day Holter ECGs to screen for silent atrial fibrillation. The primary endpoint will be any episode of atrial fibrillation or atrial flutter of ≥30 seconds duration. The age- and sex-specific prevalence of newly diagnosed atrial fibrillation will be estimated. For the case-control study, 120 patients with paroxysmal atrial fibrillation were recruited as cases (41.7% females; median age 74.6 years); controls will be randomly selected from the cohort study in a 2:1 ratio. All participants in the cohort study and all cases were prospectively evaluated including clinical, laboratory, echocardiographic and electrical parameters. A clinical prediction model for undiagnosed atrial fibrillation will be derived in the case-control study and externally validated in the cohort study.


The STAR-FIB study programme will estimate the age- and sex-specific prevalence of silent atrial fibrillation in a hospitalised patient population, and develop and validate a clinical prediction model to identify patients at risk of silent atrial fibrillation.


  1. Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation. 1998;98(10):946–52. doi:.
  2. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, et al.; ESC Scientific Document Group. 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;•••:2020.
  3. Freedman B, Camm J, Calkins H, Healey JS, Rosenqvist M, Wang J, et al.; AF-Screen Collaborators. Screening for Atrial Fibrillation: A Report of the AF-SCREEN International Collaboration. Circulation. 2017;135(19):1851–67. doi:.
  4. Halcox JPJ, Wareham K, Cardew A, Gilmore M, Barry JP, Phillips C, et al. Assessment of Remote Heart Rhythm Sampling Using the AliveCor Heart Monitor to Screen for Atrial Fibrillation: The REHEARSE-AF Study. Circulation. 2017;136(19):1784–94. doi:.
  5. 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:.
  6. Charitos EI, Stierle U, Ziegler PD, Baldewig M, Robinson DR, Sievers HH, et al. A comprehensive evaluation of rhythm monitoring strategies for the detection of atrial fibrillation recurrence: insights from 647 continuously monitored patients and implications for monitoring after therapeutic interventions. Circulation. 2012;126(7):806–14. doi:.
  7. Martinez C, Katholing A, Freedman SB. Adverse prognosis of incidentally detected ambulatory atrial fibrillation. A cohort study. Thromb Haemost. 2014;112(2):276–86. doi:.
  8. Healey JS, Alings M, Ha A, Leong-Sit P, Birnie DH, de Graaf JJ, et al.; ASSERT-II Investigators. Subclinical Atrial Fibrillation in Older Patients. Circulation. 2017;136(14):1276–83. doi:.
  9. Healey JS, Connolly SJ, Gold MR, Israel CW, Van Gelder IC, Capucci A, et al.; ASSERT Investigators. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med. 2012;366(2):120–9. doi:.
  10. Nasir JM, Pomeroy W, Marler A, Hann M, Baykaner T, Jones R, et al. Predicting Determinants of Atrial Fibrillation or Flutter for Therapy Elucidation in Patients at Risk for Thromboembolic Events (PREDATE AF) Study. Heart Rhythm. 2017;14(7):955–61. doi:.
  11. Reiffel JA, Verma A, Kowey PR, Halperin JL, Gersh BJ, Wachter R, et al.; REVEAL AF Investigators. Incidence of Previously Undiagnosed Atrial Fibrillation Using Insertable Cardiac Monitors in a High-Risk Population: The REVEAL AF Study. JAMA Cardiol. 2017;2(10):1120–7. doi:.
  12. Engdahl J, Svennberg E, Friberg L, Al-Khalili F, Frykman V, Kemp Gudmundsdottir K, et al. Stepwise mass screening for atrial fibrillation using N-terminal pro b-type natriuretic peptide: the STROKESTOP II study design. Europace. 2017;19(2):297–302. doi:.
  13. Diederichsen SZ, Haugan KJ, Køber L, Højberg S, Brandes A, Kronborg C, et al. Atrial fibrillation detected by continuous electrocardiographic monitoring using implantable loop recorder to prevent stroke in individuals at risk (the LOOP study): Rationale and design of a large randomized controlled trial. Am Heart J. 2017;187:122–32. doi:.
  14. A study to determine if identification of undiagnosed atrial fibrillation in people at Least 70 years of age reduces the risk of stroke (GUARD-AF).
  15. Kirchhof P, Blank BF, Calvert M, Camm AJ, Chlouverakis G, Diener HC, et al. Probing oral anticoagulation in patients with atrial high rate episodes: Rationale and design of the Non-vitamin K antagonist Oral anticoagulants in patients with Atrial High rate episodes (NOAH-AFNET 6) trial. Am Heart J. 2017;190:12–8. doi:.
  16. Lopes RD, Alings M, Connolly SJ, Beresh H, Granger CB, Mazuecos JB, et al. Rationale and design of the Apixaban for the Reduction of Thrombo-Embolism in Patients With Device-Detected Sub-Clinical Atrial Fibrillation (ARTESiA) trial. Am Heart J. 2017;189:137–45. doi:.
  17. Diederichsen SZ, Haugan KJ, Kronborg C, Graff C, Højberg S, Køber L, et al. Comprehensive Evaluation of Rhythm Monitoring Strategies in Screening for Atrial Fibrillation: Insights From Patients at Risk Monitored Long Term With an Implantable Loop Recorder. Circulation. 2020;141(19):1510–22. doi:.
  18. Khurshid S, Healey JS, McIntyre WF, Lubitz SA. Population-Based Screening for Atrial Fibrillation. Circ Res. 2020;127(1):143–54. doi:.
  19. Schnabel RB, Sullivan LM, Levy D, Pencina MJ, Massaro JM, D’Agostino RB, Sr, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study. Lancet. 2009;373(9665):739–45. doi:.
  20. Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013;2(2):e000102. doi:.
  21. Chamberlain AM, Agarwal SK, Folsom AR, Soliman EZ, Chambless LE, Crow R, et al. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study). Am J Cardiol. 2011;107(1):85–91. doi:.
  22. Brunner KJ, Bunch TJ, Mullin CM, May HT, Bair TL, Elliot DW, et al. Clinical predictors of risk for atrial fibrillation: implications for diagnosis and monitoring. Mayo Clin Proc. 2014;89(11):1498–505. doi:.
  23. Lopez Soto A, Formiga F, Bosch X. Garcia Alegria J and en representacion de los investigadores del estudio E [Prevalence of atrial fibrillation and related factors in hospitalized old patients: ESFINGE study]. Med Clin (Barc). 2012;138:231–7.
  24. Haeberlin A, Roten L, Schilling M, Scarcia F, Niederhauser T, Vogel R, et al. Software-based detection of atrial fibrillation in long-term ECGs. Heart Rhythm. 2014;11(6):933–8. doi:.
  25. Lijmer JG, Mol BW, Heisterkamp S, Bonsel GJ, Prins MH, van der Meulen JH, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA. 1999;282(11):1061–6. doi:.
  26. Ransohoff DF, Feinstein AR. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med. 1978;299(17):926–30. doi:.
  27. Engdahl J, Andersson L, Mirskaya M, Rosenqvist M. Stepwise screening of atrial fibrillation in a 75-year-old population: implications for stroke prevention. Circulation. 2013;127(8):930–7. doi:.
  28. Frankel S, Eachus J, Pearson N, Greenwood R, Chan P, Peters TJ, et al. Population requirement for primary hip-replacement surgery: a cross-sectional study. Lancet. 1999;353(9161):1304–9. doi:.
  29. STATA survey data reference manual. Release 14.
  30. Steyerberg EW. Clinical prediction models. A practical approach to development, validation, and updating. New York, NY: Springer; 2009.
  31. Karch A, Koch A, Zapf A, Zerr I, Karch A. Partial verification bias and incorporation bias affected accuracy estimates of diagnostic studies for biomarkers that were part of an existing composite gold standard. J Clin Epidemiol. 2016;78:73–82. doi:.
  32. Gelman A. Scaling regression inputs by dividing by two standard deviations. Stat Med. 2008;27(15):2865–73. doi:.
  33. Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al.; ESC Scientific Document Group. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37(38):2893–962. doi:.
  34. Mairesse GH, Moran P, Van Gelder IC, Elsner C, Rosenqvist M, Mant J, et al.; ESC Scientific Document Group. 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. doi:.
  35. Lowres N, Neubeck L, Redfern J, Freedman SB. Screening to identify unknown atrial fibrillation. A systematic review. Thromb Haemost. 2013;110(2):213–22. doi:.
  36. Rooney MR, Soliman EZ, Lutsey PL, Norby FL, Loehr LR, Mosley TH, et al. Prevalence and Characteristics of Subclinical Atrial Fibrillation in a Community-Dwelling Elderly Population: The ARIC Study. Circ Arrhythm Electrophysiol. 2019;12(10):e007390. doi:.
  37. Svennberg E, Engdahl J, Al-Khalili F, Friberg L, Frykman V, Rosenqvist M. Mass Screening for Untreated Atrial Fibrillation: The STROKESTOP Study. Circulation. 2015;131(25):2176–84. doi:.
  38. Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, et al.; MAFA II Investigators. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol. 2019;74(19):2365–75. doi:.
  39. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al.; Apple Heart Study Investigators. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381(20):1909–17. doi:.
  40. Diederichsen SZ, Haugan KJ, Brandes A, Graff C, Krieger D, Kronborg C, et al. Incidence and predictors of atrial fibrillation episodes as detected by implantable loop recorder in patients at risk: From the LOOP study. Am Heart J. 2020;219:117–27. doi:.
  41. Sanna T, Diener HC, Passman RS, Di Lazzaro V, Bernstein RA, Morillo CA, et al.; CRYSTAL AF Investigators. Cryptogenic stroke and underlying atrial fibrillation. N Engl J Med. 2014;370(26):2478–86. doi:.
  42. Lowres N, Neubeck L, Salkeld G, Krass I, McLachlan AJ, Redfern J, et al. Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost. 2014;111(6):1167–76. doi:.
  43. Desteghe L, Raymaekers Z, Lutin M, Vijgen J, Dilling-Boer D, Koopman P, et al. Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting. Europace. 2017;19(1):29–39.
  44. Linker DT, Murphy TB, Mokdad AH. Selective screening for atrial fibrillation using multivariable risk models. Heart. 2018;104(18):1492–9. doi:.
  45. Schnabel RB, Larson MG, Yamamoto JF, Sullivan LM, Pencina MJ, Meigs JB, et al. Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community. Circulation. 2010;121(2):200–7. doi:.
  46. De Vos CB, Weijs B, Crijns HJ, Cheriex EC, Palmans A, Habets J, et al. Atrial tissue Doppler imaging for prediction of new-onset atrial fibrillation. Heart. 2009;95(10):835–40. doi:.
  47. Dewland TA, Vittinghoff E, Mandyam MC, Heckbert SR, Siscovick DS, Stein PK, et al. Atrial ectopy as a predictor of incident atrial fibrillation: a cohort study. Ann Intern Med. 2013;159(11):721–8. doi:.
  48. Dhala A, Underwood D, Leman R, Madu E, Baugh D, Ozawa Y, et al.; Multicenter PHi-Res Study. Signal-averaged P-wave analysis of normal controls and patients with paroxysmal atrial fibrillation: a study in gender differences, age dependence, and reproducibility. Clin Cardiol. 2002;25(11):525–31. doi:.

Most read articles by the same author(s)

1 2 3 > >>