Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.
Acute Kidney Injury
/ classification
Aged
Alanine Transaminase
/ blood
Bilirubin
/ blood
Blood Urea Nitrogen
Comorbidity
Creatinine
/ blood
Databases, Factual
Deep Learning
Electronic Health Records
Female
Glutamyl Aminopeptidase
/ blood
Humans
L-Lactate Dehydrogenase
/ blood
Lactic Acid
/ blood
Leukocyte Count
Liver Diseases
/ epidemiology
Male
Middle Aged
Phenotype
Prognosis
Renal Dialysis
Sepsis
/ complications
Simplified Acute Physiology Score
United States
/ epidemiology
AKI
acute kidney injury
acute renal failure
deep learning
dialysis
mortality
subtypes
Journal
Clinical journal of the American Society of Nephrology : CJASN
ISSN: 1555-905X
Titre abrégé: Clin J Am Soc Nephrol
Pays: United States
ID NLM: 101271570
Informations de publication
Date de publication:
06 11 2020
06 11 2020
Historique:
received:
11
08
2019
accepted:
07
08
2020
pubmed:
10
10
2020
medline:
23
11
2021
entrez:
9
10
2020
Statut:
ppublish
Résumé
Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records. We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement. We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.
Sections du résumé
BACKGROUND AND OBJECTIVES
Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS
We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement.
RESULTS
We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for
CONCLUSIONS
Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.
Identifiants
pubmed: 33033164
pii: 01277230-202011000-00006
doi: 10.2215/CJN.09330819
pmc: PMC7646246
doi:
Substances chimiques
Lactic Acid
33X04XA5AT
Creatinine
AYI8EX34EU
L-Lactate Dehydrogenase
EC 1.1.1.27
Alanine Transaminase
EC 2.6.1.2
Glutamyl Aminopeptidase
EC 3.4.11.7
Bilirubin
RFM9X3LJ49
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1557-1565Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 by the American Society of Nephrology.
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