Key result
A deep learning model using 12-lead ECGs successfully detected hypokalemia in emergency patients, achieving an area under the curve of 0.80 in internal validation and 0.77 in external validation.
Why the study?
A deep learning model enabling non-invasive hypokalemia screening from an ECG may improve detection of this life-threatening condition in emergency patients.
Does a 12-lead ECG-based deep learning model accurately detect hypokalemia in emergency patients?
Observational (n=8,251)
Yes
Does a 12-lead ECG-based deep learning model accurately detect hypokalemia in emergency patients?
Effect estimate: AUC 0.80 (95% CI 0.77-0.82)
A deep learning model using 12-lead ECGs can rapidly and non-invasively screen for hypokalemia in emergency patients with moderate accuracy.
May support ECG-based hypokalemia screening in emergencies; leaves open prospective validation before clinical use.
BACKGROUND: A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. METHODS: We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period. RESULTS: We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%. CONCLUSIONS: In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.
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Wang et al. (2021) conducted an observational in Hypokalemia (n=8,251). Deep learning model (DLM) using 12-lead ECG vs. Serum potassium concentration (blood test) was evaluated on Area under the receiver operating characteristic curve (AUC) for detecting hypokalemia (<3.5 mmol/L) in the internal validation data set (AUC 0.80, 95% CI 0.77-0.82). A deep learning model using 12-lead ECGs successfully detected hypokalemia in emergency patients, achieving an area under the curve of 0.80 in internal validation and 0.77 in external validation.
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