Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography

Version 1 : Received: 20 January 2021 / Approved: 21 January 2021 / Online: 21 January 2021 (09:03:32 CET)

How to cite: Kwon, J.; Lee, S.Y.; Lee, Y.; Jo, Y.; Jung, M.; Cho, Y.; Shin, J.; Ban, J.; Kim, K.; Park, J.; Oh, B. Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography. Preprints 2021, 2021010408 (doi: 10.20944/preprints202101.0408.v1). Kwon, J.; Lee, S.Y.; Lee, Y.; Jo, Y.; Jung, M.; Cho, Y.; Shin, J.; Ban, J.; Kim, K.; Park, J.; Oh, B. Deep Learning Model for Detection of Hypoalbuminemia Using Electrocardiography. Preprints 2021, 2021010408 (doi: 10.20944/preprints202101.0408.v1).

Abstract

Background: Albumin has a pivotal role in the homeostasis of osmotic pressure and is associated with cardiovascular, nephrotic, hepatic, and nutritional diseases. The detection of hypoalbuminemia is a cornerstone for diagnosis of hidden diseases and patient deterioration. We developed and validated a deep-learning-based model (DLM) for detection of hypoalbuminemia using electrocardiography (ECG). Methods: This historical cohort study included data from consecutive patients from two hospitals. The patient data in one hospital were divided into development (82,499 ECGs from 54,248 patients) and internal validation (20,664 ECGs from 20,664 patients) datasets, whereas the patient data in the other hospital were included in only an external validation (37,421 ECGs from 37,421 patients) dataset. An DLM was developed using a 12-lead ECG signal, age, and sex from the development dataset. The endpoint was hypoalbuminemia, defined by serum albumin concentration below 3.5 g/dL. Results: During the internal and external validations, the areas under the receiver operating characteristic curve of the DLM for the detection of hypoalbuminemia were 0.887 (0.877–0.897) and 0.888 (0.880–0.896), respectively. Among the 27,400 individuals without hypoalbuminemia at the initial laboratory exam, those identified by the DLM as higher-risk patients had a significantly larger change in developing hypoalbuminemia than those in the low-risk group (7.09% vs. 1.01%, p < 0.001) during 24 months. The sensitivity map showed that the DLM focused on the T wave and QRS complex for the detection of hypoalbuminemia. Conclusions: The DLM exhibited a high accuracy for hypoalbuminemia detection and prediction using 12-, 6-, and single-lead ECGs.

Keywords

Electrocardiography; Albumins; Deep Learning; Artificial Intelligence

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