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Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study

BACKGROUND: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient...

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Autores principales: Boussina, Aaron, Wardi, Gabriel, Shashikumar, Supreeth Prajwal, Malhotra, Atul, Zheng, Kai, Nemati, Shamim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337434/
https://www.ncbi.nlm.nih.gov/pubmed/37351927
http://dx.doi.org/10.2196/45614
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author Boussina, Aaron
Wardi, Gabriel
Shashikumar, Supreeth Prajwal
Malhotra, Atul
Zheng, Kai
Nemati, Shamim
author_facet Boussina, Aaron
Wardi, Gabriel
Shashikumar, Supreeth Prajwal
Malhotra, Atul
Zheng, Kai
Nemati, Shamim
author_sort Boussina, Aaron
collection PubMed
description BACKGROUND: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient’s physiological state and the interventions they receive. OBJECTIVE: We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling. METHODS: Forty commonly used clinical variables from the electronic health record were used as inputs to a feed-forward neural network trained to predict the onset of sepsis. Using spectral clustering on the representations from this network, we derived and validated consistent phenotypes across a diverse cohort of patients with sepsis. We modeled phenotype dynamics as a Markov decision process with transitions as a function of the patient’s current state and the interventions they received. RESULTS: Four consistent and distinct phenotypes were derived from over 11,500 adult patients who were admitted from the University of California, San Diego emergency department (ED) with sepsis between January 1, 2016, and January 31, 2020. Over 2000 adult patients admitted from the University of California, Irvine ED with sepsis between November 4, 2017, and August 4, 2022, were involved in the external validation. We demonstrate that sepsis phenotypes are not static and evolve in response to physiological factors and based on interventions. We show that roughly 45% of patients change phenotype membership within the first 6 hours of ED arrival. We observed consistent trends in patient dynamics as a function of interventions including early administration of antibiotics. CONCLUSIONS: We derived and describe 4 sepsis phenotypes present within 6 hours of triage in the ED. We observe that the administration of a 30 mL/kg fluid bolus may be associated with worse outcomes in certain phenotypes, whereas prompt antimicrobial therapy is associated with improved outcomes.
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spelling pubmed-103374342023-07-13 Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study Boussina, Aaron Wardi, Gabriel Shashikumar, Supreeth Prajwal Malhotra, Atul Zheng, Kai Nemati, Shamim J Med Internet Res Original Paper BACKGROUND: Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient’s physiological state and the interventions they receive. OBJECTIVE: We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling. METHODS: Forty commonly used clinical variables from the electronic health record were used as inputs to a feed-forward neural network trained to predict the onset of sepsis. Using spectral clustering on the representations from this network, we derived and validated consistent phenotypes across a diverse cohort of patients with sepsis. We modeled phenotype dynamics as a Markov decision process with transitions as a function of the patient’s current state and the interventions they received. RESULTS: Four consistent and distinct phenotypes were derived from over 11,500 adult patients who were admitted from the University of California, San Diego emergency department (ED) with sepsis between January 1, 2016, and January 31, 2020. Over 2000 adult patients admitted from the University of California, Irvine ED with sepsis between November 4, 2017, and August 4, 2022, were involved in the external validation. We demonstrate that sepsis phenotypes are not static and evolve in response to physiological factors and based on interventions. We show that roughly 45% of patients change phenotype membership within the first 6 hours of ED arrival. We observed consistent trends in patient dynamics as a function of interventions including early administration of antibiotics. CONCLUSIONS: We derived and describe 4 sepsis phenotypes present within 6 hours of triage in the ED. We observe that the administration of a 30 mL/kg fluid bolus may be associated with worse outcomes in certain phenotypes, whereas prompt antimicrobial therapy is associated with improved outcomes. JMIR Publications 2023-06-23 /pmc/articles/PMC10337434/ /pubmed/37351927 http://dx.doi.org/10.2196/45614 Text en ©Aaron Boussina, Gabriel Wardi, Supreeth Prajwal Shashikumar, Atul Malhotra, Kai Zheng, Shamim Nemati. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.06.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Boussina, Aaron
Wardi, Gabriel
Shashikumar, Supreeth Prajwal
Malhotra, Atul
Zheng, Kai
Nemati, Shamim
Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title_full Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title_fullStr Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title_full_unstemmed Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title_short Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
title_sort representation learning and spectral clustering for the development and external validation of dynamic sepsis phenotypes: observational cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337434/
https://www.ncbi.nlm.nih.gov/pubmed/37351927
http://dx.doi.org/10.2196/45614
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