A Study to Evaluate Accuracy and Validity of the "Chang Gung" Ventricular Systolic Dysfunction Screening Software

A Study to Evaluate Accuracy and Validity of the "Chang Gung" Ventricular Systolic Dysfunction Screening Software

A Study to Evaluate Accuracy and Validity of the "Chang Gung" Ventricular Systolic Dysfunction Screening Software

The purpose of this research is to test a software tool called the "Chang Gung" Ventricular Systolic Dysfunction screening software, which uses a 12-lead electrocardiogram to determine if a patient has left ventricular systolic dysfunction. The goal is to determine if the software can accurately identify patients with this condition, which would help doctors diagnose and treat it more effectively. The trial will involve using the software on patients and comparing its results to those obtained through echocardiograms, which are currently the gold standard for diagnosing left ventricular systolic dysfunction. Only patients who meet specific eligibility criteria will be able to participate in the trial, and the software will be administered by trained healthcare professionals. The study will help determine if the software is a useful tool for diagnosing left ventricular systolic dysfunction, which could lead to earlier diagnosis and better outcomes for patients. The research team will collect and analyze data on the accuracy of the software and its usability in clinical practice. Overall, this study will provide important information for doctors and patients about a new tool for diagnosing left ventricular systolic dysfunction.

This is a retrospective study conducted in Taiwan that aimed to test the performance of "Chang Gung" Ventricular Systolic Dysfunction screening software using electrocardiogram (ECG) and echocardiography data. The study data was obtained from a database that includes six hospitals of Chang Gung Memorial Hospital, between January 1, 2006, and December 31, 2019. The software was developed using a training set of 133,225 data and validated using a set of 57,134 data. For clinical validation, a total of 1,172 test data were randomly selected from the testing set, stratified by hospital classification, age group and gender. The hospital classification, age group and gender ratios were based on the proportion of the testing data. The test data were also stratified by the presence or absence of left ventricular systolic dysfunction(LVSD) for test group and control group, defined as a heart output rate of less than 40% within 14 days before and after the ECG recording. During the clinical trial, a cardiologist with 15 years of experience in treating cardiovascular disease examined the ECG data without any exclusion criteria. The cardiologist also confirmed the accuracy of the left ventricular ejection fraction (LVEF) measurement, which was defined as LVSD in the echocardiography reports. The LVEF was extracted from legally-binding echocardiography reports, not by the cardiologist during the clinical trial. The ECG data were screened and filtered for quality before being input into the software. The primary outcome was the sensitivity of the software, which was defined as not inferior to 0.86. The study also analyzed secondary outcome measures, including the area under the receiver operating characteristic curve, accuracy, specificity, positive predictive value, negative predictive value, false-positive rate, and false-negative rate.

The investigators collected retrospective data and gave to the software to interpret. Echocardiography as the gold standard, and tested whether the software could correctly interpret left ventricular systolic dysfunction.

This software is suitable for 12-lead ECG signals of adults over 20 years old, and assists doctors in screening patients for left ventricular systolic dysfunction.

A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

The proportions of positive results in statistics and diagnostic tests that are true positive results

The proportions of negative results in statistics and diagnostic tests that are true negative results

Inclusion Criteria: Equal or greater than twenty years old. Static 12-lead electrocardiogram of General Electric MUSE XML format file. The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500). Echocardiography data within 14 days before or after the electrocardiogram acquisition time. The electrocardiogram signal is 500 Hz. The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz. The length of the electrocardiogram signal is ten seconds (the electrocardiogram output of model MAC5500 is ten seconds, and there is no need to capture a segment). Exclusion Criteria: Cases used in the model development process. Lacks any electrode. Contain any electrode lacks a segment.

Savarese G, Lund LH. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017 Apr;3(1):7-11. doi: 10.15420/cfr.2016:25:2.

Kannel WB, Ho K, Thom T. Changing epidemiological features of cardiac failure. Br Heart J. 1994 Aug;72(2 Suppl):S3-9. doi: 10.1136/hrt.72.2_suppl.s3. No abstract available.

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.

Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Colvin MM, Drazner MH, Filippatos GS, Fonarow GC, Givertz MM, Hollenberg SM, Lindenfeld J, Masoudi FA, McBride PE, Peterson PN, Stevenson LW, Westlake C. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017 Aug 8;136(6):e137-e161. doi: 10.1161/CIR.0000000000000509. Epub 2017 Apr 28. No abstract available.

Bozkurt B, Hershberger RE, Butler J, Grady KL, Heidenreich PA, Isler ML, Kirklin JK, Weintraub WS. 2021 ACC/AHA Key Data Elements and Definitions for Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Clinical Data Standards for Heart Failure). J Am Coll Cardiol. 2021 Apr 27;77(16):2053-2150. doi: 10.1016/j.jacc.2020.11.012. Epub 2020 Nov 26. No abstract available.

Taylor CJ, Ordonez-Mena JM, Roalfe AK, Lay-Flurrie S, Jones NR, Marshall T, Hobbs FDR. Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ. 2019 Feb 13;364:l223. doi: 10.1136/bmj.l223. Erratum In: BMJ. 2019 Oct 8;367:l5840.

Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O'Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation. 2019 Mar 5;139(10):e56-e528. doi: 10.1161/CIR.0000000000000659. No abstract available. Erratum In: Circulation. 2020 Jan 14;141(2):e33.