TY - JOUR
T1 - Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection
AU - Love, Christopher J.
AU - Lampert, Joshua
AU - Huneycutt, David
AU - Musat, Dan L.
AU - Shah, Mahek
AU - Enciso, Jorge E.Silva
AU - Doherty, Bryan
AU - Gentry, James L.
AU - Kwan, Michael D.
AU - Carter, Ethan C.
AU - Reddy, Vivek Y.
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2025.
PY - 2025
Y1 - 2025
N2 - Background: Hypertrophic cardiomyopathy (HCM) is often underdiagnosed. Artificial intelligence (AI)-based notification of HCM suspicion on a 12-lead ECG has been proposed to assist patient identification and evaluation. However, there has been no study to date to assess clinical implementation of this approach. Methods: In an open-label, multicentre prospective cohort study, Viz HCM (Viz.ai) - an AI-ECG software alerting of suspected HCM - was implemented at five healthcare systems between January and December 2023 to identify patients >18 years of age without prior HCM diagnosis. The coprimary endpoints were the percentage of HCM-suspected cases viewed by users and the types of follow-up actions. Additional outcome measures included the time to follow-up, demographic characteristics of enrolled patients and follow-up outcomes. Results: Out of 145 848 patients screened with algorithm-compliant ECGs, 4348 (3%) were alerted for suspected HCM. Users viewed 69% (3017/4348) of AI-suspected HCM cases. 217 patients met the study criteria and were enrolled with broad representation across racial and ethnic groups - including 23% Black, 9% Asian and 12% Hispanic or Latino. Of the enrolled patients, 182 (84%) had an indication for a total of 243 follow-up actions. The median (interquartile) time from ECG to diagnostic imaging indicating HCM was 7.5 (1.0-37.2) days. From the 217 enrolled patients, 17 (7.8%) were newly diagnosed with HCM - 8 inpatient and 9 outpatient. During the study, deployment of an optimised algorithm operating point helped reduce the alert percentage of algorithm-screened patients from 4.4% (2097/47868) to 2.3% (2251/97980), p<0.0001, with no difference in the enrolment rate by alerts reviewed. Conclusion: An AI-based ECG device for HCM can be implemented successfully in a variety of clinical workflows to help identify new patients with HCM. Future study is warranted to assess scalability and comparisons to standard of care.
AB - Background: Hypertrophic cardiomyopathy (HCM) is often underdiagnosed. Artificial intelligence (AI)-based notification of HCM suspicion on a 12-lead ECG has been proposed to assist patient identification and evaluation. However, there has been no study to date to assess clinical implementation of this approach. Methods: In an open-label, multicentre prospective cohort study, Viz HCM (Viz.ai) - an AI-ECG software alerting of suspected HCM - was implemented at five healthcare systems between January and December 2023 to identify patients >18 years of age without prior HCM diagnosis. The coprimary endpoints were the percentage of HCM-suspected cases viewed by users and the types of follow-up actions. Additional outcome measures included the time to follow-up, demographic characteristics of enrolled patients and follow-up outcomes. Results: Out of 145 848 patients screened with algorithm-compliant ECGs, 4348 (3%) were alerted for suspected HCM. Users viewed 69% (3017/4348) of AI-suspected HCM cases. 217 patients met the study criteria and were enrolled with broad representation across racial and ethnic groups - including 23% Black, 9% Asian and 12% Hispanic or Latino. Of the enrolled patients, 182 (84%) had an indication for a total of 243 follow-up actions. The median (interquartile) time from ECG to diagnostic imaging indicating HCM was 7.5 (1.0-37.2) days. From the 217 enrolled patients, 17 (7.8%) were newly diagnosed with HCM - 8 inpatient and 9 outpatient. During the study, deployment of an optimised algorithm operating point helped reduce the alert percentage of algorithm-screened patients from 4.4% (2097/47868) to 2.3% (2251/97980), p<0.0001, with no difference in the enrolment rate by alerts reviewed. Conclusion: An AI-based ECG device for HCM can be implemented successfully in a variety of clinical workflows to help identify new patients with HCM. Future study is warranted to assess scalability and comparisons to standard of care.
KW - Cardiomyopathy, Hypertrophic
KW - Electrocardiography
UR - http://www.scopus.com/inward/record.url?scp=105003043816&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105003043816&partnerID=8YFLogxK
U2 - 10.1136/heartjnl-2024-325608
DO - 10.1136/heartjnl-2024-325608
M3 - Article
C2 - 40240132
AN - SCOPUS:105003043816
SN - 1355-6037
JO - Heart
JF - Heart
M1 - 5608
ER -