Comparison of an AI-enabled single-lead ECG monitor and point-of-care testing for detecting intradialytic hypokalemia

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.Methods 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.Results 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).Conclusions 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.

Authors

Institutions

Publication Details

Journal
Renal Failure
Published
2026-10-30
DOI
https://doi.org/10.1080/0886022x.2026.2746815
Primary Topic
ECG Monitoring and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparison of an AI-enabled single-lead ECG monitor and point-of-care testing for detecting intradialytic hypokalemia

Jenq‐Shyong Chan, Wen‐Fang Chiang, Po-Jen Hsiao, Chi‐Ming Chu et al.
4 links
Renal Failure
ECG Monitoring and Analysis
article

Comparison of an AI-enabled single-lead ECG monitor and point-of-care testing for detecting intradialytic hypokalemia

Jenq‐Shyong Chan, Wen‐Fang Chiang, Po-Jen Hsiao, Chi‐Ming Chu, Kun-Lin Wu, Ruei-Lin Wang, Kun-Yi Lin
article en

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.Methods 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.Results 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).Conclusions 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.

Renal FailureVol. 48(1)
Tri-Service General Hospital (TW), National Central University (TW), Taoyuan Armed Forces General Hospital (TW), National Defense Medical Center (TW)
Taoyuan Armed Forces General Hospital
Good health and well-being
Openalex Percentile: Top 11%
ECG Monitoring and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon