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Cited by 62ECG Monitoring and AnalysisPotassium and Related DisordersCardiac electrophysiology and arrhythmias

Point-of-Care Artificial Intelligence-Enabled ECG for Dyskalemia: A Retrospective Cohort Analysis for Accuracy and Outcome Prediction

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Lin, Chin, Chau, Tom, Lin, Chin-Sheng, Shang, Hung-Sheng, Fang, Wen-Hui, Lee, Ding-Jie, Lee, Chia-Cheng, Tsai, Shi-Hung +2 more·2022·npj Digital Medicine
DOI 10.1038/s41746-021-00550-0PMC PMC8770475
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Abstract

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K + and serum laboratory potassium measurement (Lab-K + ) within 1 h were included. ECG-K + had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K + to predict moderate-to-severe hypokalemia (Lab-K + ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K + ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K + concentration and adverse outcomes were more prominent for ECG-K + than for Lab-K + . ECG-K + and Lab-K + hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K + , patients with hypokalemic ECG-K + had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K + but dyskalemic ECG-K + (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K + not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

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  • In a cohort including more than 34,000 emergency department visits with paired ECG and laboratory potassium measurements, the model achieved AUC values greater than 0.95 for moderate-to-severe hyperkalemia and greater than 0.85 for moderate-to-severe hypokalemia.

    “In a cohort including more than 34,000 emergency department visits with paired ECG and laboratory potassium measurements, the model achieved AUC values greater than 0.95 for moderate-to-severe hyperkalemia and greater than 0.85 for moderate-to-severe hypokalemia.”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [15]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Potassium models may prioritize urgent laboratory testing without replacing serum measurement.

    “potassium models may prioritize urgent laboratory testing without replacing serum measurement [6,15].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [15]

    The Discussion (page unavailable) proposes using ECG-based potassium estimates to flag severe dyskalemia early and prepare interventions, but says to give those interventions upon receipt of a concordant laboratory result. This supports not replacing laboratory measurement; it does not specifically propose prioritizing urgent laboratory testing. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • AI-ECG has been applied to point-of-care potassium estimation.

    “Later work carried the approach to 12-lead detection of both hypokalemia and hyperkalemia [11] and point-of-care potassium estimation [15].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [15]

  • A point-of-care AI-enabled ECG model estimated serum potassium and detected both hypokalemia and hyperkalemia in emergency department patients.

    “Lin et al. developed a point-of-care AI-enabled ECG model to estimate serum potassium and detect both hypokalemia and hyperkalemia in emergency department patients.”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [15]

  • AI-ECG potassium signals may reflect not only serum electrolyte concentration but also underlying physiologic vulnerability.

    “Patients with discordance between laboratory and AI-predicted ECG potassium also had worse outcomes, suggesting that AI-ECG potassium signals may reflect not only serum electrolyte concentration but also underlying physiologic vulnerability [15].”

    From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [15]

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Research works printed but not linked: 35

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Authors
Lin, C., Chau, T., Lin, C.-S., Shang, H.-S., Fang, W.-H., Lee, D.-J., Lee, C.-C., Tsai, S.-H., Wang, C.-H., & Lin, S.-H.
Affiliations
  • Tri-Service General Hospital (TW)
  • Providence St. Vincent Medical Center (US)
Corresponding author
Lin, Shih-Hua
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2022-01-19
Volume
5
Issue
1
Pages
8-8
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-021-00550-0
PMC
PMC8770475
OpenAlex
W4205351422
Cited by
62
References
40
Citations by year
2026: 12 · 2025: 10 · 2024: 18 · 2023: 13 · 2022: 9
FWCI
8.70
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Science and Technology, Taiwan (MOST110-2314-B-016-010-MY3)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Potassium and Related Disorders
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
Keywords
dyskalemia, hypokalemia, hyperkalemia, emergency department visits, all-cause mortality, hospitalization, emergency department readmission, serum potassium, prognostic factors, clinical complexity

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