Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/34177
Title: Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.
Austin Authors: Young, Marcus ;Holmes, Natasha E ;Kishore, Kartik ;Amjad, Sobia;Gaca, Michele Jane ;Serpa Neto, Ary ;Reade, Michael C;Bellomo, Rinaldo 
Affiliation: Data Analytics Research and Evaluation (DARE) Centre
The University of Melbourne, Heidelberg, VIC, Australia.
Department of Infectious Diseases, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Victoria, 3000, Australia.
School of Computing and Information Systems, The University of Melbourne, Parkville, Melbourne, VIC, Australia.
Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.
Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.;Department of Intensive Care, Austin Hospital, 145 Studley Rd, Heidelberg, Melbourne, Australia.;Department of Critical Care, School of Medicine, The University of Melbourne, Parkville, Melbourne, VIC, Australia.;Department of Intensive Care, Royal Melbourne Hospital, Melbourne, Australia.
Joint Health Command, Australian Defence Force, Brisbane, QLD, Australia.;Department of Intensive Care Medicine, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
Issue Date: 4-Nov-2023
Date: 2023
Publication information: Critical Care (London, England) 2023-11-04; 27(1)
Abstract: Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients. We obtained electronic clinical notes, demographic information, outcomes, and treatment data from three medical-surgical ICUs. Using NLP, we screened for behavioural disturbance phenotypes based on words suggestive of an agitated state, a non-agitated state, or a combination of both. We studied 2931 patients. Of these, 225 (7.7%) were NLP-Dx-BD positive for the agitated phenotype, 544 (18.6%) for the non-agitated phenotype and 667 (22.7%) for the combined phenotype. Patients with these phenotypes carried multiple clinical baseline differences. On time-dependent multivariable analysis to compensate for immortal time bias and after adjustment for key outcome predictors, agitated phenotype patients were more likely to receive antipsychotic medications (odds ratio [OR] 1.84, 1.35-2.51, p < 0.001) compared to non-agitated phenotype patients but not compared to combined phenotype patients (OR 1.27, 0.86-1.89, p = 0.229). Moreover, agitated phenotype patients were more likely to die than other phenotypes patients (OR 1.57, 1.10-2.25, p = 0.012 vs non-agitated phenotype; OR 4.61, 2.14-9.90, p < 0.001 vs. combined phenotype). This association was strongest in patients receiving mechanical ventilation when compared with the combined phenotype (OR 7.03, 2.07-23.79, p = 0.002). A similar increased risk was also seen for patients with the non-agitated phenotype compared with the combined phenotype (OR 6.10, 1.80-20.64, p = 0.004). NLP-Dx-BD screening enabled identification of three behavioural disturbance phenotypes with different characteristics, prevalence, trajectory, treatment, and outcome. Such phenotype identification appears relevant to prognostication and trial design.
URI: https://ahro.austin.org.au/austinjspui/handle/1/34177
DOI: 10.1186/s13054-023-04695-0
ORCID: 0000-0002-6195-660X
0000-0001-8501-4054
0000-0002-6254-6063
0000-0002-1595-0370
0000-0001-7350-3299
0000-0003-1520-9387
0000-0003-1570-0707
0000-0002-1650-8939
Journal: Critical Care (London, England)
Start page: 425
PubMed URL: 37925406
ISSN: 1466-609X
Type: Journal Article
Subjects: Agitation
Antipsychotic drugs
Critical illness
Delirium
Intensive care
Mortality
Appears in Collections:Journal articles

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