{
  "abstract": "Background: Intradialytic hypokalemia is an underrecognized complication of maintenance hemodialysis (MHD). While point-of-care (POC) testing offers rapid biochemical confirmation, it remains limited by its intermittent nature. Artificial intelligence (AI)-enabled electrocardiography (ECG) provides a potential noninvasive alternative for trend monitoring; however, its performance in MHD has not been compared with POC testing against a central laboratory standard.\n\nMethods: This single-center prospective study compared AI-ECG potassium estimation and POC testing during MHD. An algorithm was trained on ECG–potassium pairs from 45 patients (2021–2022) and tested in 29 patients (2023). Concurrent laboratory–POC–ECG pairs enabled direct comparison, using central laboratory assays as the reference standard.\n\nResults: The AI-enabled ECG model achieved high accuracy within the non-hypokalemic range (mean absolute error (MAE) of 0.479 mmol/L), although the predictive variance expanded during hypokalemia (MAE of 1.000 mmol/L). POC testing demonstrated the highest laboratory concordance (MAE 0.201 mmol/L; ρ = 0.977). With respect to hypokalemia screening, the AI-ECG model maintained good discriminatory performance (area under the curve (AUC) of 0.722), which was paired with a high sensitivity (77.9%) and a robust negative predictive value (83.2%). POC testing significantly outperformed the AI-ECG model in diagnostic discrimination (AUC 0.996 vs. 0.722; ΔAUC 0.274; p < 0.0001).\n\nConclusions: The AI-enabled ECG model demonstrates preliminary screening potential for hypokalemia alongside reliable non-hypokalemic tracking. Although POC testing remains the superior modality for point-in-time analytical precision, AI-enabled ECG offers a noninvasive approach for intradialytic trend monitoring. However, its ability to improve clinical outcomes or safely reduce invasive testing requires formal prospective decision-impact studies.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Nephrology, Department of Internal Medicine, Taoyuan Armed Forces General Hospital, Taoyuan, Taiwan",
    "Division of Nephrology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan",
    "Department of Life Sciences, National Central University, Taoyuan, Taiwan",
    "Department of Orthopedic Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan",
    "Division of Biostatistics and Medical Informatics, Department of Epidemiology, School of Public Health, National Defense Medical University, Taipei, Taiwan",
    "Graduate Institute of Medical Sciences, National Defense Medical University, Taipei, Taiwan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Wen-Fang Chiang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Po-Jen Hsiao"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Kun-Lin Wu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Ruei-Lin Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Kun-Yi Lin"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Chi-Ming Chu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jenq-Shyong Chan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b2nhxbwrr991c.bib",
  "cited_by": 0,
  "doi": "10.1080/0886022x.2026.2746815",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13726304/",
  "id": "b2nhxbwrr991c",
  "issns": [
    "0886-022X",
    "1525-6049"
  ],
  "landing_url": "https://doi.org/10.1080/0886022x.2026.2746815",
  "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",
      "Artificial Intelligence and Machine Learning"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2705|ECG Monitoring and Analysis"
    ]
  ],
  "openalex": "W7224751486",
  "pdf_url": "https://www.tandfonline.com/doi/pdf/10.1080/0886022X.2026.2746815?needAccess=true",
  "pmcid": "PMC13726304",
  "pmid": "42960545",
  "published": "2026-10-30",
  "publisher": "Taylor & Francis",
  "ris": "https://research.draconitic.com/paper/b2nhxbwrr991c.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T12:22:41Z",
      "native_id": "PMC13726304",
      "source": "pmc",
      "url": "https://doi.org/10.1080/0886022x.2026.2746815"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|ECG Monitoring and Analysis",
      "subfield": "cardiovascular"
    }
  ],
  "title": "Comparison of an AI-enabled single-lead ECG monitor and point-of-care testing for detecting intradialytic hypokalemia",
  "title_text": "Comparison of an AI-enabled single-lead ECG monitor and point-of-care testing for detecting intradialytic hypokalemia",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:22:41Z",
  "url": "https://research.draconitic.com/paper/b2nhxbwrr991c",
  "venue": "Renal Failure",
  "versions": []
}