Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment

Abstract

Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitoring remains difficult outside hospital settings. We developed and validated Pocket-K, a single-lead AI-ECG system initialized from the ECGFounder foundation model for non-invasive hyperkalemia screening and handheld deployment. In this multicentre observational study using routinely collected clinical ECG and laboratory data, 34,439 patients contributed 62,290 ECG--potassium pairs. Lead I data were used to fine-tune the model. Data from Peking University People's Hospital were divided into development and temporal validation sets, and data from The Second Hospital of Tianjin Medical University served as an independent external validation set. Hyperkalemia was defined as venous serum potassium > 5.5 mmol/L. Pocket-K achieved AUROCs of 0.936 in internal testing, 0.858 in temporal validation, and 0.808 in external validation. For KDIGO-defined moderate-to-severe hyperkalemia (serum potassium >= 6.0 mmol/L), AUROCs increased to 0.940 and 0.861 in the temporal and external sets, respectively. External negative predictive value exceeded 99.3%. Model-predicted high risk below the hyperkalemia threshold was more common in patients with chronic kidney disease and heart failure. A handheld prototype enabled near-real-time inference, supporting future prospective evaluation in native handheld and wearable settings.

Figures (8)

• Figure 1: Overview of Pocket-K development, validation, and proof-of-concept deployment. In the development stage, ECG--$K^+$ pairs were constructed by linking each ECG to the nearest eligible venous serum potassium measurement within a $\pm$1-hour window. These paired data were used to fine-tune ECGFounder, a pretrained ECG foundation model, to develop Pocket-K. In the validation stage, model performance was assessed in three settings: an internal test set from the PKUPH development set, a temporal validation set at PKUPH, and an independent external validation set from SHTMU. In the proof-of-concept stage, a handheld device recorded a 30-s lead I ECG, which was processed through a smartphone-based workflow for near-real-time inference and generation of a hyperkalemia risk alert.
• Figure 2: ROC curves for model performance evaluation. (a) Internal testing ROC curve of PKUPH. (b) Temporal validation ROC curve of PKUPH. (c) Independent external ROC curve of SHTMU.
• Figure 3: ROC curves for detection of KDIGO-defined moderate-to-severe hyperkalemia (serum potassium $\geq$ 6.0 mmol/L). (a) Temporal validation ROC curve in PKUPH. (b) Independent external validation ROC curve in SHTMU.
• Figure 4: Signal-averaged waveform comparison. Average Lead I ECG waveforms are shown for patients correctly classified by the model as hyperkalemic (high risk, red line) and non-hyperkalemic (low risk, blue line). Shaded areas indicate standard deviation.
• Figure 5: Clinical profiles of reference-negative samples stratified by model-predicted risk. In the external validation set, all samples shown had serum potassium values below the diagnostic threshold for hyperkalemia. They were stratified into a model low-risk group and a model high-risk group according to model output. Chronic kidney disease and heart failure were more common in the model high-risk group. The prevalence of chronic kidney disease increased from 1.2% in the model low-risk group to 3.2% in the model high-risk group ($P<0.001$), and the prevalence of heart failure increased from 1.3% to 4.4% ($P<0.001$).