{
  "abstract": "Background Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted access to training data and uncertain generalisability. We developed and externally validated a generalisable AI-ECG model trained exclusively on publicly available data to predict outcomes after transcatheter aortic valve replacement (TAVR).Methods A transformer-based AI-ECG model was trained on 341 151 publicly available ECGs with mortality labels. The model was externally validated in a prospective cohort of 439 patients undergoing TAVR and additionally tested in a surgical aortic valve replacement cohort. Model performance for predicting 30-day and 1-year mortality was assessed using area under the receiver operating characteristic curve (AUC) and associations with clinical outcomes were evaluated using multivariable regression.Results In the TAVR cohort, a single pre-procedural ECG predicted 30-day mortality with an AUC of 0.85 (95% CI 0.78 to 0.92) and 1-year mortality with an AUC of 0.74 (95% CI 0.67 to 0.80). The derived AI-ECG risk score was independently associated with 1-year mortality (adjusted OR 1.70), major adverse cardiac events and major renal events. Predictive performance was consistent in an independent surgical cohort. High-risk ECG features included atrial fibrillation, prolonged QT interval, ST-T abnormalities and low voltage.Conclusions An AI-ECG model trained solely on publicly available data provides accurate and generalisable prediction of outcomes after TAVR using a single ECG. This approach demonstrates the feasibility of transparent, shareable AI models for cardiovascular risk stratification and supports a general-to-specific paradigm for clinical deployment.",
  "authors": [
    {
      "affiliations": [
        "National Taiwan University Hospital Hsin-Chu Branch Hsin-Chu Hospital, Hsinchu, Taiwan"
      ],
      "name": "Yu-Chung Hsiao"
    },
    {
      "affiliations": [
        "Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan"
      ],
      "name": "Kuo-Hsuan Hung"
    },
    {
      "affiliations": [
        "National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Chih-Fan Yeh"
    },
    {
      "affiliations": [
        "National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Ying-Hsien Chen"
    },
    {
      "affiliations": [
        "National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Tsung-Yu Ko"
    },
    {
      "affiliations": [
        "Renal Division, National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Chun-Fu Lai"
    },
    {
      "affiliations": [
        "Renal Division, National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Li-Chun Lin"
    },
    {
      "affiliations": [
        "Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Wei-Tien Chang"
    },
    {
      "affiliations": [
        "Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan"
      ],
      "name": "Yu Tsao"
    },
    {
      "affiliations": [
        "Division of Cardiology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan"
      ],
      "name": "Mao-Shin Lin"
    },
    {
      "affiliations": [
        "Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Hsien-Li Kao"
    },
    {
      "affiliations": [
        "National Taiwan University Hospital, Taipei, Taiwan"
      ],
      "name": "Chau-Chung Wu"
    }
  ],
  "title": "Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement",
  "uid": "54e0222a-4f51-50c7-8870-96c3caa1dfb5"
}