Key result
AI-enabled single-lead ECG detects intradialytic hypokalemia with ~0.72 AUC, trailing point-of-care testing.
Why the study?
Point-of-care testing for intradialytic hypokalemia during maintenance hemodialysis is limited by its intermittent nature, and the performance of noninvasive AI-enabled ECG potassium monitoring had not been compared with point-of-care testing against a central laboratory standard.
Does an AI-enabled single-lead ECG monitor accurately detect intradialytic hypokalemia compared to point-of-care testing in patients undergoing maintenance hemodialysis?
Observational (n=74)
No
Does an AI-enabled single-lead ECG monitor accurately detect intradialytic hypokalemia compared to point-of-care testing in patients undergoing maintenance hemodialysis?
While point-of-care testing remains superior for analytical precision, AI-enabled single-lead ECG demonstrates moderate discriminatory performance (AUC 0.722, NPV 83.2%) as a noninvasive screening tool for intradialytic hypokalemia.
POC testing outperforms AI-ECG for intradialytic hypokalemia detection; leaves open whether refined models can approach point-of-care accuracy in larger cohorts.
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.
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Chiang et al. (2026) conducted an observational in Intradialytic hypokalemia in maintenance hemodialysis (n=74). AI-enabled single-lead ECG potassium estimation vs. Point-of-care (POC) testing (against central laboratory reference standard) was evaluated on Diagnostic discrimination for intradialytic hypokalemia (AUC) (ΔAUC 0.274, p=< 0.0001). AI-enabled single-lead ECG showed good discrimination for intradialytic hypokalemia (AUC 0.722), though point-of-care testing outperformed it (AUC 0.996; ΔAUC 0.274; p < 0.0001).
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