用单导联心电图+AI实现无创高钾血症筛查，可手机端实时检测。
Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment
- 基于ECGFounder模型微调，仅用一导联心电图识别高钾血症。
- 外部验证集准确率80.8%，阴性预测值超99.3%。
- 适合慢肾病和心衰患者居家监测，支持手持设备部署。
高钾血症是慢性肾病和心力衰竭患者中常见且危及生命的电解质紊乱，但院外频繁监测仍具挑战。我们开发并验证了Pocket-K，一个基于ECGFounder基础模型的单导联AI-ECG系统，用于非侵入式高钾血症筛查及便携设备部署。本多中心观察研究使用常规临床心电图与实验室数据，共纳入34,439名患者，生成62,290对心电图-血钾数据。以导联I数据进行模型微调，北京大学人民医院数据分为开发集与时间验证集，天津医科大学第二医院数据作为独立外部验证集。高钾血症定义为静脉血清钾>5.5 mmol/L。Pocket-K在内部测试中达到0.936的AUROC，时间验证集为0.858，外部验证集为0.808。对于KDIGO定义的中重度高钾血症（血钾≥6.0 mmol/L），时间验证集和外部验证集的AUROC分别达0.940和0.861。外部验证集阴性预测值超过99.3%。模型预测高风险但低于阈值的情况在慢性肾病和心衰患者中更常见。手持原型机实现近实时推理，支持未来在原生手持及可穿戴设备中的前瞻性评估。
原文摘要 · Abstract (English)
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.
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