Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality

Taylor D. Smith; Victor Ortiz-Soriano; Javier A. Neyra; Jin Chen

doi:10.1109/ICHI.2019.8904739

Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality

Taylor D. Smith, Victor Ortiz-Soriano, Javier A. Neyra, Jin Chen

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of kidney and non-kidney complications. Nearly 50% of patients in the intensive care unit (ICU) experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI serum creatinine (SCr) stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such characterization does not include either the progression or duration of AKI, both of which are associated with adverse outcomes. In this article, by leveraging a large volume of SCr temporal variabilities within the first 7 days of ICU stay, we propose a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is a feasible method of AKI subtyping and superior to the current AKI KDIGO definition for the association with hospital mortality in this subset of critically ill patients.

Original language	English
Title of host publication	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
ISBN (Electronic)	9781538691380
DOIs	https://doi.org/10.1109/ICHI.2019.8904739
State	Published - Jun 2019
Event	7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China Duration: Jun 10 2019 → Jun 13 2019

Publication series

Name	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference	7th IEEE International Conference on Healthcare Informatics, ICHI 2019
Country/Territory	China
City	Xi'an
Period	6/10/19 → 6/13/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Acute Kidney Injury
Dynamic Trajectory Alignment
KDIGO Trajectory Subtyping

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Health Informatics
Biomedical Engineering

Access to Document

10.1109/ICHI.2019.8904739

Cite this

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title = "Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality",

abstract = "Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of kidney and non-kidney complications. Nearly 50% of patients in the intensive care unit (ICU) experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI serum creatinine (SCr) stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such characterization does not include either the progression or duration of AKI, both of which are associated with adverse outcomes. In this article, by leveraging a large volume of SCr temporal variabilities within the first 7 days of ICU stay, we propose a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is a feasible method of AKI subtyping and superior to the current AKI KDIGO definition for the association with hospital mortality in this subset of critically ill patients.",

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Smith, TD, Ortiz-Soriano, V, Neyra, JA & Chen, J 2019, Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality. in 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019., 8904739, 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019, 7th IEEE International Conference on Healthcare Informatics, ICHI 2019, Xi'an, China, 6/10/19. https://doi.org/10.1109/ICHI.2019.8904739

Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality. / Smith, Taylor D.; Ortiz-Soriano, Victor; Neyra, Javier A. et al.

2019 IEEE International Conference on Healthcare Informatics, ICHI 2019. 2019. 8904739 (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Acute kidney injury (AKI) is a complex systemic syndrome associated with high morbidity and mortality and risk for the subsequent development of kidney and non-kidney complications. Nearly 50% of patients in the intensive care unit (ICU) experience AKI. AKI severity is a key metric for evaluating patients risk of hospital mortality. Current AKI serum creatinine (SCr) stratification is based on absolute changes in Serum Creatinine (SCr) and the maximal increase relative to the patients baseline value. However, such characterization does not include either the progression or duration of AKI, both of which are associated with adverse outcomes. In this article, by leveraging a large volume of SCr temporal variabilities within the first 7 days of ICU stay, we propose a novel model called Trajectory of Acute Kidney Injury (TAKI) for the identification of AKI trajectory subtypes. Experimental results demonstrate that TAKI is a feasible method of AKI subtyping and superior to the current AKI KDIGO definition for the association with hospital mortality in this subset of critically ill patients.

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Identifying Acute Kidney Injury Trajectory Phenotypes Associated with Hospital Mortality

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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