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

2026-03-14

AI Summary

This work addresses the critical need for convenient, non-invasive monitoring of hyperkalemia—a life-threatening condition prevalent among patients with chronic kidney disease and heart failure—outside clinical settings. We propose Pocket-K, the first AI-powered screening system leveraging a single-lead electrocardiogram (Lead I) to detect hyperkalemia by fine-tuning the ECGFoundation base model, deployed on a handheld device for near real-time edge inference. Validated on multicenter clinical data, the system achieved an internal test AUROC of 0.936 and an external validation AUROC of 0.808; notably, for moderate-to-severe hyperkalemia (≥6.0 mmol/L), the external AUROC reached 0.861 with a negative predictive value exceeding 99.3%. This study pioneers the application of ECG foundation models to single-lead hyperkalemia screening, offering a highly effective, non-invasive early-warning tool for at-risk populations in outpatient settings.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Humans and AI: AI for Accessibility
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• hyperkalemia
• non-invasive monitoring
• chronic kidney disease
• heart failure
• electrolyte disorder

Innovation

• AI-ECG
• hyperkalemia detection
• single-lead ECG
• foundation model
• handheld deployment