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Multicenter Study
. 2021 May;26(3):e12839.
doi: 10.1111/anec.12839. Epub 2021 Mar 15.

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Joon-Myoung Kwon  1   2   3   4 , Min-Seung Jung  1 , Kyung-Hee Kim  2   5 , Yong-Yeon Jo  1 , Jae-Hyun Shin  1 , Yong-Hyeon Cho  1 , Yoon-Ji Lee  1 , Jang-Hyeon Ban  4 , Ki-Hyun Jeon  2   5 , Soo Youn Lee  2   5 , Jinsik Park  5 , Byung-Hee Oh  5
Affiliations
Multicenter Study

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Joon-Myoung Kwon et al. Ann Noninvasive Electrocardiol. 2021 May.

Abstract

Introduction: The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study.

Methods and results: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients. We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance.

Conclusion: The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

Keywords: artificial intelligence; deep learning; electrocardiography; electrolytes.

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Conflict of interest statement

KHK, KHJ, SYL, and BHO declare that they have no competing interests. JK and JP are co‐founder, and YYJ, MSJ, YJL, YHC, and JHS are researchers of Medical AI Co., a medical artificial intelligence company. JK and JHB are researchers of Bodyfriend Co. There are no products in development or marketed products to declare. This does not alter our adherence to Journal.

Figures

FIGURE 1
FIGURE 1
Study flowchart. ECG denotes electrocardiography
FIGURE 2
FIGURE 2
Architecture of deep learning‐based model for detecting electrolyte imbalance. Conv denotes convolutional neural network and ECG electrocardiography
FIGURE 3
FIGURE 3
Performances of deep learning‐based model for detecting electrolyte abnormalities. AUC denotes area under the receiver operating characteristic curve, DLM deep learning‐based model, and ECG electrocardiography
FIGURE 4
FIGURE 4
Sensitivity map of DLM for detecting electrolyte imbalance. DLM denoted deep learning‐based model
See this image and copyright information in PMC

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