Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Abstract

IMPORTANCEFor patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.OBJECTIVE To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. DESIGN, SETTING, AND PARTICIPANTSA deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018.EXPOSURES Use of a deep-learning model. MAIN OUTCOMES AND MEASURESArea under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. RESULTSOf the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona.CONCLUSIONS AND RELEVANCE In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

Discovering hidden information in biosignals from patients using artificial intelligence

“…Introduction of AI in medicine enables the analysis of high-density data such as biosignals; in particular, the conventional 12-electrode ECG is at the forefront of this research. One example is a study that screens for hyperkalemia (serum potassium levels > 5.5 mEq/L) among patients with chronic kidney disease based on a 12-electrode ECG [18]. In the study, researchers used data from 1,576,581 ECGs collected from a single hospital in the United States from 449,380 patients with potassium test records taken within 12 hours of recording their ECG.…”

A Deep-learning Algorithm With the Real World Validation for Detecting Acute Myocardial Infarction

“…The application of deep learning technology in the cardiovascular eld for arrhythmias, dyskalemia, and valvular heart disease had become popularized recently. [19][20][21][27][28][29] However, no large scale study has been designed to apply deep learning technology for MI detection. Previous DLMs for MI detection by ECG were analyzed mainly from the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG Database.…”

“…[15][16][17][18] DLM have been con rmed to surpass the cardiologist level on ECG interpretation when they are trained by large annotated ECG datasets. [19][20][21] To our knowledge, the available ECG databases of AMI are relative small. [22] Our study aimed to develop a DLM to timely, objectively and precisely diagnose AMI by ECG.…”

Role of Artificial Intelligence in Kidney Disease

“…Additionally,the model is a screening test with low specificity, with upwards of 42% false-positive results, which may cause anxiety and inconvenience for patients. [22]. Pilia et al also use an artificial neural network to reconstruct the extracellular ionic concentrations for both potassium and calcium with an acceptable precision in CKD patients [23].…”