Clinical trial NCT05442203: Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease
Official title: Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Atrial Fibrillation
Structural Heart Disease
Keywords: Heart Disease
Structural Heart Disease
Machine Learning
Electrocardiogram
Atrial Fibrillation
Lead sponsor: Tempus AI
Sponsor class: INDUSTRY
First posted: 2022-07-01
Enrollment: 1000
Sex: ALL
Minimum age (years): 40
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

Brief summary: Atrial fibrillation is an abnormal beating of the heart that can lead to stroke or heart failure. Structural heart diseases are conditions that affect the heart valves or heart muscle and can cause permanent heart damage if left untreated. Sometimes people have atrial fibrillation or structural heart disease and do not know it. The purpose of this study is to evaluate two devices that can predict who has or may develop atrial fibrillation or structural heart disease based on the results of an electrocardiogram.


Eligibility criteria: Inclusion Criteria:

* Retrospective Phase:
* Adults aged 40 or older.
* At least 1 ECG obtained during routine clinical care.
* Prospective Phase:
* AF Cohort:
* Adults aged 65 or older at the time of ECG.
* ECG obtained as part of a clinical care.
* Patient is able to identify a licensed healthcare provider to receive the results of the patch monitor.
* SHD Cohort:
* Adults aged 40 or older at the time of the ECG.
* ECG obtained as part of a clinical care between study start date and the end of study recruitment
* Patient is able to identify a licensed healthcare provider to receive the results of the echocardiogram.

Exclusion Criteria:

* Retrospective Phase:
* Patients who have previously requested that their data not be involved in any secondary use application such as a research study.
* Prospective Phase:
* AF Cohort:
* Any clinical or social factor that would prohibit completing the follow-up studies in a timely fashion.
* Patient currently admitted to the hospital (at time of consent)
* Permanent pacemaker or implanted cardiac defibrillator or implanted loop recorder.
* History of atrial fibrillation or atrial flutter.
* Cardiac surgery within 30 days prior to the index ECG
* Cardiac surgery planned within the next 6 months.
* Allergy to adhesive.
* SHD Cohort:
* Any clinical or social factor that would prohibit completing the follow-up studies in a timely fashion.
* Patient currently admitted to the hospital (at time of consent).
* History of SHD defined as any of the following: severe mitral regurgitation, severe tricuspid regurgitation, moderate or severe aortic stenosis, moderate or severe aortic regurgitation, moderate or severe mitral stenosis, left ventricular systolic dysfunction (LVEF ≤ 40%), or increased septal wall thickness \> 15 mm.
* Allergy to ultrasound gel.

Primary outcomes:
measure: Positive-predictive value (PPV) of the AF device at six months
timeFrame: 12 months
measure: Positive-predictive value (PPV) of the SHD device at six months
timeFrame: 12 months
Secondary outcomes:
measure: Positive-predictive value (PPV) of the AF device at 12 months
timeFrame: 18 months
measure: Positive-predictive value (PPV) of the SHD device at 12 months
timeFrame: 18 months
Other outcomes:
Locations:
city: Grand Rapids
country: United States
facility: Corewell Health
state: Michigan
city: Cincinnati
country: United States
facility: TriHealth
state: Ohio
city: Danville
country: United States
facility: Geisinger Medical Center
state: Pennsylvania
Study officials:
affiliation: Tempus AI, Inc.
name: John Pfeifer, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Gopinathannair R, Etheridge SP, Marchlinski FE, Spinale FG, Lakkireddy D, Olshansky B. Arrhythmia-Induced Cardiomyopathies: Mechanisms, Recognition, and Management. J Am Coll Cardiol. 2015 Oct 13;66(15):1714-28. doi: 10.1016/j.jacc.2015.08.038.
citation: Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med. 2007 Jun 19;146(12):857-67. doi: 10.7326/0003-4819-146-12-200706190-00007.
citation: Connolly SJ, Ezekowitz MD, Yusuf S, Eikelboom J, Oldgren J, Parekh A, Pogue J, Reilly PA, Themeles E, Varrone J, Wang S, Alings M, Xavier D, Zhu J, Diaz R, Lewis BS, Darius H, Diener HC, Joyner CD, Wallentin L; RE-LY Steering Committee and Investigators. Dabigatran versus warfarin in patients with atrial fibrillation. N Engl J Med. 2009 Sep 17;361(12):1139-51. doi: 10.1056/NEJMoa0905561. Epub 2009 Aug 30.
citation: Patel MR, Mahaffey KW, Garg J, Pan G, Singer DE, Hacke W, Breithardt G, Halperin JL, Hankey GJ, Piccini JP, Becker RC, Nessel CC, Paolini JF, Berkowitz SD, Fox KA, Califf RM; ROCKET AF Investigators. Rivaroxaban versus warfarin in nonvalvular atrial fibrillation. N Engl J Med. 2011 Sep 8;365(10):883-91. doi: 10.1056/NEJMoa1009638. Epub 2011 Aug 10.
citation: Granger CB, Alexander JH, McMurray JJ, Lopes RD, Hylek EM, Hanna M, Al-Khalidi HR, Ansell J, Atar D, Avezum A, Bahit MC, Diaz R, Easton JD, Ezekowitz JA, Flaker G, Garcia D, Geraldes M, Gersh BJ, Golitsyn S, Goto S, Hermosillo AG, Hohnloser SH, Horowitz J, Mohan P, Jansky P, Lewis BS, Lopez-Sendon JL, Pais P, Parkhomenko A, Verheugt FW, Zhu J, Wallentin L; ARISTOTLE Committees and Investigators. Apixaban versus warfarin in patients with atrial fibrillation. N Engl J Med. 2011 Sep 15;365(11):981-92. doi: 10.1056/NEJMoa1107039. Epub 2011 Aug 27.
citation: Page RL, Wilkinson WE, Clair WK, McCarthy EA, Pritchett EL. Asymptomatic arrhythmias in patients with symptomatic paroxysmal atrial fibrillation and paroxysmal supraventricular tachycardia. Circulation. 1994 Jan;89(1):224-7. doi: 10.1161/01.cir.89.1.224.
citation: Reiffel JA, Verma A, Kowey PR, Halperin JL, Gersh BJ, Wachter R, Pouliot E, Ziegler PD; 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 Oct 1;2(10):1120-1127. doi: 10.1001/jamacardio.2017.3180.
citation: Healey JS, Connolly SJ, Gold MR, Israel CW, Van Gelder IC, Capucci A, Lau CP, Fain E, Yang S, Bailleul C, Morillo CA, Carlson M, Themeles E, Kaufman ES, Hohnloser SH; ASSERT Investigators. Subclinical atrial fibrillation and the risk of stroke. N Engl J Med. 2012 Jan 12;366(2):120-9. doi: 10.1056/NEJMoa1105575.
citation: Steinhubl SR, Waalen J, Edwards AM, Ariniello LM, Mehta RR, Ebner GS, Carter C, Baca-Motes K, Felicione E, Sarich T, Topol EJ. Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA. 2018 Jul 10;320(2):146-155. doi: 10.1001/jama.2018.8102.
citation: Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, Hung G, Lee J, Kowey P, Talati N, Nag D, Gummidipundi SE, Beatty A, Hills MT, Desai S, Granger CB, Desai M, Turakhia MP; Apple Heart Study Investigators. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019 Nov 14;381(20):1909-1917. doi: 10.1056/NEJMoa1901183.
citation: Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, Yan L, Xing Y, Shi H, Li S, Liu Y, Liu F, Feng M, Chen Y, Lip GYH; MAFA II Investigators. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol. 2019 Nov 12;74(19):2365-2375. doi: 10.1016/j.jacc.2019.08.019. Epub 2019 Sep 2.
citation: Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, Sinner MF, Sotoodehnia N, Fontes JD, Janssens AC, Kronmal RA, Magnani JW, Witteman JC, Chamberlain AM, Lubitz SA, Schnabel RB, Agarwal SK, McManus DD, Ellinor PT, Larson MG, Burke GL, Launer LJ, Hofman A, Levy D, Gottdiener JS, Kaab S, Couper D, Harris TB, Soliman EZ, Stricker BH, Gudnason V, Heckbert SR, Benjamin EJ. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013 Mar 18;2(2):e000102. doi: 10.1161/JAHA.112.000102.
citation: Christophersen IE, Yin X, Larson MG, Lubitz SA, Magnani JW, McManus DD, Ellinor PT, Benjamin EJ. A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study. Am Heart J. 2016 Aug;178:45-54. doi: 10.1016/j.ahj.2016.05.004. Epub 2016 May 17.
citation: Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1.
citation: Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, vanMaanen DP, Hartzel DN, Ruhl JA, Lagerman BF, Rocha DB, Stoudt NJ, Schneider G, Johnson KW, Zimmerman N, Leader JB, Kirchner HL, Griessenauer CJ, Hafez A, Good CW, Fornwalt BK, Haggerty CM. 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 Mar 30;143(13):1287-1298. doi: 10.1161/CIRCULATIONAHA.120.047829. Epub 2021 Feb 16.
citation: Ross J Jr, Braunwald E. Aortic stenosis. Circulation. 1968 Jul;38(1 Suppl):61-7. doi: 10.1161/01.cir.38.1s5.v-61. No abstract available.
citation: Cheitlin MD, Gertz EW, Brundage BH, Carlson CJ, Quash JA, Bode RS Jr. Rate of progression of severity of valvular aortic stenosis in the adult. Am Heart J. 1979 Dec;98(6):689-700. doi: 10.1016/0002-8703(79)90465-4.
citation: Davies SW, Gershlick AH, Balcon R. Progression of valvar aortic stenosis: a long-term retrospective study. Eur Heart J. 1991 Jan;12(1):10-4. doi: 10.1093/oxfordjournals.eurheartj.a059815.
citation: Curtis JP, Sokol SI, Wang Y, Rathore SS, Ko DT, Jadbabaie F, Portnay EL, Marshalko SJ, Radford MJ, Krumholz HM. The association of left ventricular ejection fraction, mortality, and cause of death in stable outpatients with heart failure. J Am Coll Cardiol. 2003 Aug 20;42(4):736-42. doi: 10.1016/s0735-1097(03)00789-7.
citation: Martinez-Naharro A, Baksi AJ, Hawkins PN, Fontana M. Diagnostic imaging of cardiac amyloidosis. Nat Rev Cardiol. 2020 Jul;17(7):413-426. doi: 10.1038/s41569-020-0334-7. Epub 2020 Feb 10.
citation: Writing Committee Members; Otto CM, Nishimura RA, Bonow RO, Carabello BA, Erwin JP 3rd, Gentile F, Jneid H, Krieger EV, Mack M, McLeod C, O'Gara PT, Rigolin VH, Sundt TM 3rd, Thompson A, Toly C. 2020 ACC/AHA Guideline for the Management of Patients With Valvular Heart Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021 Feb 2;77(4):450-500. doi: 10.1016/j.jacc.2020.11.035. Epub 2020 Dec 17.
citation: Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P, Muraru D, Picard MH, Rietzschel ER, Rudski L, Spencer KT, Tsang W, Voigt JU. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2015 Mar;16(3):233-70. doi: 10.1093/ehjci/jev014.
citation: Alexander KM, Orav J, Singh A, Jacob SA, Menon A, Padera RF, Kijewski MF, Liao R, Di Carli MF, Laubach JP, Falk RH, Dorbala S. Geographic Disparities in Reported US Amyloidosis Mortality From 1979 to 2015: Potential Underdetection of Cardiac Amyloidosis. JAMA Cardiol. 2018 Sep 1;3(9):865-870. doi: 10.1001/jamacardio.2018.2093.
citation: d'Arcy JL, Coffey S, Loudon MA, Kennedy A, Pearson-Stuttard J, Birks J, Frangou E, Farmer AJ, Mant D, Wilson J, Myerson SG, Prendergast BD. Large-scale community echocardiographic screening reveals a major burden of undiagnosed valvular heart disease in older people: the OxVALVE Population Cohort Study. Eur Heart J. 2016 Dec 14;37(47):3515-3522. doi: 10.1093/eurheartj/ehw229. Epub 2016 Jun 26.
citation: Maron MS, Hellawell JL, Lucove JC, Farzaneh-Far R, Olivotto I. Occurrence of Clinically Diagnosed Hypertrophic Cardiomyopathy in the United States. Am J Cardiol. 2016 May 15;117(10):1651-1654. doi: 10.1016/j.amjcard.2016.02.044. Epub 2016 Mar 2.
citation: Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet. 2006 Sep 16;368(9540):1005-11. doi: 10.1016/S0140-6736(06)69208-8.
citation: Kwon JM, Lee SY, Jeon KH, Lee Y, Kim KH, Park J, Oh BH, Lee MM. Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. J Am Heart Assoc. 2020 Apr 7;9(7):e014717. doi: 10.1161/JAHA.119.014717. Epub 2020 Mar 21.
citation: Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.
citation: Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, Murphree DH, Michelena HI, Enriquez-Sarano M, Carter RE, Johnson PW, Noseworthy PA, Lopez-Jimenez F, Oh JK. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021 Aug 7;42(30):2885-2896. doi: 10.1093/eurheartj/ehab153.
citation: Ulloa-Cerna AE, Jing L, Pfeifer JM, Raghunath S, Ruhl JA, Rocha DB, Leader JB, Zimmerman N, Lee G, Steinhubl SR, Good CW, Haggerty CM, Fornwalt BK, Chen R. rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography. Circulation. 2022 Jul 5;146(1):36-47. doi: 10.1161/CIRCULATIONAHA.121.057869. Epub 2022 May 9.
citation: Britton M, Gustafsson C. Non-rheumatic atrial fibrillation as a risk factor for stroke. Stroke. 1985 Mar-Apr;16(2):182-8. doi: 10.1161/01.str.16.2.182.
citation: Wolf PA, Dawber TR, Thomas HE Jr, Kannel WB. Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: the Framingham study. Neurology. 1978 Oct;28(10):973-7. doi: 10.1212/wnl.28.10.973.
citation: Stewart S, Hart CL, Hole DJ, McMurray JJ. A population-based study of the long-term risks associated with atrial fibrillation: 20-year follow-up of the Renfrew/Paisley study. Am J Med. 2002 Oct 1;113(5):359-64. doi: 10.1016/s0002-9343(02)01236-6.