TY  - JOUR
AU  - Boussina, Aaron
AU  - Wardi, Gabriel
AU  - Shashikumar, Supreeth Prajwal
AU  - Malhotra, Atul
AU  - Zheng, Kai
AU  - Nemati, Shamim
PY  - 2023
DA  - 2023/6/23
TI  - Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study
JO  - J Med Internet Res
SP  - e45614
VL  - 25
KW  - sepsis
KW  - phenotype
KW  - emergency service, hospital
KW  - disease progression
KW  - artificial intelligence
KW  - machine learning
KW  - emergency
KW  - infection
KW  - clinical phenotype
KW  - clinical phenotyping
KW  - transition model
KW  - transition modeling
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e45614
UR  - https://doi.org/10.2196/45614
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37351927
DO  - 10.2196/45614
ID  - info:doi/10.2196/45614
ER  -