Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach

Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity amon...

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Bibliographic Details
Published in:Journal of biomedical informatics Vol. 143; p. 104393
Main Authors: Tan, Yongsen, Huang, Jiahui, Zhuang, Jinhu, Huang, Haofan, Jiang, Song, She, Miaowen, Tian, Mu, Liu, Yong, Yu, Xiaxia
Format: Journal Article
Language:English
Published: United States 01.07.2023
Subjects:
Deep learning
Subphenotypes
Clustering
Acute kidney injury
ISSN:1532-0480, 1532-0480
Online Access:Get more information
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Summary:Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.
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ISSN:1532-0480
1532-0480
DOI:10.1016/j.jbi.2023.104393