{
  "abstract": "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.",
  "authors": [
    {
      "affiliations": [
        "Viz Ai Inc, San Francisco, California, USA"
      ],
      "name": "Christopher J Love"
    },
    {
      "affiliations": [
        "Mount Sinai Fuster Heart Hospital, New York, New York, USA"
      ],
      "name": "Joshua Lampert"
    },
    {
      "affiliations": [
        "TriStar Centennial Medical Center, Nashville, Tennessee, USA"
      ],
      "name": "David Huneycutt"
    },
    {
      "affiliations": [
        "Valley Hospital, Ridgewood, New Jersey, USA"
      ],
      "name": "Dan L Musat"
    },
    {
      "affiliations": [
        "Methodist Healthcare System of San Antonio Ltd, San Antonio, Texas, USA"
      ],
      "name": "Mahek Shah"
    },
    {
      "affiliations": [
        "UC San Diego Health, San Diego, California, USA"
      ],
      "name": "Jorge E Silva Enciso"
    },
    {
      "affiliations": [
        "TriStar Centennial Medical Center, Nashville, Tennessee, USA"
      ],
      "name": "Bryan Doherty"
    },
    {
      "affiliations": [
        "TriStar Centennial Medical Center, Nashville, Tennessee, USA"
      ],
      "name": "James L Gentry"
    },
    {
      "affiliations": [
        "Methodist Healthcare System of San Antonio Ltd, San Antonio, Texas, USA"
      ],
      "name": "Michael D Kwan"
    },
    {
      "affiliations": [
        "Viz Ai Inc, San Francisco, California, USA"
      ],
      "name": "Ethan C Carter"
    },
    {
      "affiliations": [
        "Mount Sinai Fuster Heart Hospital, New York, New York, USA"
      ],
      "name": "Vivek Y Reddy"
    }
  ],
  "title": "Clinical implementation of an AI-enabled ECG for hypertrophic cardiomyopathy detection",
  "uid": "efe8cb20-093b-523e-bcba-c2f48d316e6e"
}