Endotyping in ARDS: one step forward in precision medicine.
Acute respiratory distress syndrome
Cluster analysis
Feature selection
Mixed data analysis
Journal
European journal of medical research
ISSN: 2047-783X
Titre abrégé: Eur J Med Res
Pays: England
ID NLM: 9517857
Informations de publication
Date de publication:
14 May 2024
14 May 2024
Historique:
received:
06
04
2023
accepted:
30
04
2024
medline:
15
5
2024
pubmed:
15
5
2024
entrez:
14
5
2024
Statut:
epublish
Résumé
The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort. A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality. The LCM suggested three subgroups (n ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.
Sections du résumé
BACKGROUND
BACKGROUND
The Berlin definition of acute respiratory distress syndrome (ARDS) includes only clinical characteristics. Understanding unique patient pathobiology may allow personalized treatment. We aimed to define and describe ARDS phenotypes/endotypes combining clinical and pathophysiologic parameters from a Canadian ARDS cohort.
METHODS
METHODS
A cohort of adult ARDS patients from multiple sites in Calgary, Canada, had plasma cytokine levels and clinical parameters measured in the first 24 h of ICU admission. We used a latent class model (LCM) to group the patients into several ARDS subgroups and identified the features differentiating those subgroups. We then discuss the subgroup effect on 30 day mortality.
RESULTS
RESULTS
The LCM suggested three subgroups (n
PERSPECTIVE
CONCLUSIONS
ARDS subgrouping using biomarkers and clinical characteristics is useful for categorizing a heterogeneous condition into several homogenous patient groups. This study found three ARDS subgroups using LCM; each subgroup has a different level of mortality. This model may also apply to developing further trial design, prognostication, and treatment selection.
Identifiants
pubmed: 38745261
doi: 10.1186/s40001-024-01876-7
pii: 10.1186/s40001-024-01876-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
284Informations de copyright
© 2024. The Author(s).
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