Key result
Deep learning algorithm detects hyperkalemia and hypokalemia from 12-lead ECGs with ~0.93 AUROC.
Why the study?
Because dyskalemia can cause fatal arrhythmias and cardiac arrest, timely monitoring of serum potassium levels is crucial, motivating the need for rapid, noninvasive detection via electrocardiograms.
Does a deep learning algorithm applied to electrocardiograms accurately detect hyperkalemia and hypokalemia?
Cohort (n=258,685)
Yes
Does a deep learning algorithm applied to electrocardiograms accurately detect hyperkalemia and hypokalemia?
Effect estimate: AUROC 0.929 (95% CI 0.916-0.941)
Deep learning models can accurately detect hyperkalemia and hypokalemia from standard ECGs, providing a rapid and noninvasive diagnostic tool.
Dyskalemia is a common electrolyte abnormality. Since dyskalemia can cause fatal arrhythmias and cardiac arrest in severe cases, it is crucial to monitor serum potassium (K+) levels on time. We developed deep learning models to detect hyperkalemia (K+ ≥ 5.5 mEq/L) and hypokalemia (K+ < 3.5 mEq/L) from electrocardiograms (ECGs), which are noninvasive and can be quickly measured. The retrospective cohort study was conducted at two hospitals from 2006 to 2020. The training set, validation set, internal testing cohort, and external validation cohort comprised 310,449, 15,828, 23,849, and 130,415 ECG-K+ samples, respectively. Deep learning models demonstrated high diagnostic performance in detecting hyperkalemia (AUROC 0.929, 0.912, 0.887 with sensitivity 0.926, 0.924, 0.907 and specificity 0.706, 0.676, 0.635 for 12-lead, limb-lead, lead I ECGs) and hypokalemia (AUROC 0.925, 0.896, 0.885 with sensitivity 0.912, 0.896, 0.904 and specificity 0.790, 0.734, 0.694) in the internal testing cohort. The group predicted to be positive by the hyperkalemia model showed a lower 30-day survival rate compared to the negative group (p < 0.001), supporting the clinical efficacy of the model. We also compared the importance of ECG segments (P, QRS, and T) on dyskalemia prediction of the model for interpretability. By applying these models in clinical practice, it will be possible to diagnose dyskalemia simply and quickly, thereby contributing to the improvement of patient outcomes.
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An et al. (2024) conducted a cohort in Dyskalemia (Hyperkalemia and Hypokalemia) (n=258,685). Deep learning algorithm (DeepECG-Hyperkalemia and DeepECG-Hypokalemia) vs. Serum potassium laboratory tests (ground truth) was evaluated on Detection of hyperkalemia (K+ ≥ 5.5 mEq/L) using 12-lead ECG in the internal testing cohort (AUROC 0.929, 95% CI 0.916-0.941). A deep learning algorithm using 12-lead electrocardiograms demonstrated high diagnostic performance for detecting hyperkalemia with an AUROC of 0.929 and hypokalemia with an AUROC of 0.925.
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