{
  "abstract": "Background: Long-term mortality remains unsatisfactorily high after transcatheter aortic valve replacement (TAVR). Conventional risk models are limited in capturing subclinical electrophysiological alterations associated with poor prognosis, which can be identified on routine preoperative electrocardiograms.\n\nObjectives: The authors aim to develop and validate an artificial intelligence–enhanced electrocardiogram (AI-ECG) model for predicting long-term mortality in post-TAVR patients.\n\nMethods: A total of 711 patients with severe aortic stenosis undergoing TAVR were enrolled from 2 centers. Patients from one center were divided into training and internal validation sets (7:3), and participants from another center served as the external validation cohort. Preoperative electrocardiogram images were analyzed using a Residual Network-18 model to generate mortality risk stratification. The primary endpoint was 3-year all-cause death.\n\nResults: The AI-ECG model demonstrated comparable discrimination between the internal and external patient cohorts, with areas under the receiver operating characteristics curve of 0.767 (95% CI: 0.657-0.877) vs 0.712 (95% CI: 0.627-0.795) (P for DeLong test = 0.428). High-risk patients (15.5% [39 of 251]) exhibited a 61.5% (24 of 39, 95% CI: 42.8%-74.1%) 3-year mortality rate vs 16.5% (35 of 212, 95% CI: 11.4%-21.4%) in low-risk patients (84.5% [212 of 251]) (log-rank P < 0.001). Adjusted for comorbidities, high-risk classification independently predicted mortality (adjusted HR: 3.49; 95% CI: 1.96-6.22). Subgroup analysis did not reveal significant interaction effects of the AI-ECG model across different patient populations. Decision curve analysis confirmed clinical net benefit across threshold probabilities (0.05-0.60).\n\nConclusions: The AI-ECG model provides noninvasive and accurate long-term risk stratification for TAVR patients, with promising clinical application value for individualized follow-up management.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China",
    "State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China",
    "Department of Cardiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China",
    "State Key Laboratory of Transvascular Implantation Devices, Hangzhou, China",
    "Heart Regeneration and Repair Key Laboratory of Zhejiang Province, Hangzhou, China",
    "Transvascular Implantation Devices Research Institute, Hangzhou, China",
    "Peking University, Beijing, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wence Shi"
    },
    {
      "affs": [
        1
      ],
      "name": "Peirou Yan"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Qifeng Zhu"
    },
    {
      "affs": [
        6
      ],
      "name": "Guo Pu"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Xianbao Liu"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Cheng Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Moyang Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guannan Niu"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhenyan Zhao"
    },
    {
      "affs": [
        0
      ],
      "name": "Zheng Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Dejing Feng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yongjian Wu"
    },
    {
      "affs": [
        2,
        3,
        4,
        5
      ],
      "name": "Jian'an Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongliang Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8a67vq84rvsws.bib",
  "cited_by": 2,
  "doi": "10.1016/j.jacasi.2026.05.034",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13700035/",
  "id": "8a67vq84rvsws",
  "issns": [
    "2772-3747"
  ],
  "landing_url": "https://doi.org/10.1016/j.jacasi.2026.05.034",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
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      "openalex",
      "2705|Cardiac Valve Diseases and Treatments"
    ]
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  "openalex": "W7196943745",
  "pdf_url": null,
  "pmcid": "PMC13700035",
  "pmid": "42559773",
  "published": "2026-08-06",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/8a67vq84rvsws.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "PMC13700035",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.jacasi.2026.05.034"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|Cardiac Valve Diseases and Treatments",
      "subfield": "cardiovascular"
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  ],
  "title": "Using AI-ECG to Stratify Long-Term Mortality Risk and Prognosis in TAVR Patients",
  "title_text": "Using AI-ECG to Stratify Long-Term Mortality Risk and Prognosis in TAVR Patients",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:36:31Z",
  "url": "https://research.draconitic.com/paper/8a67vq84rvsws",
  "venue": "JACC Asia",
  "versions": []
}