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Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study
- Source :
- Thorax. 77:13-21
- Publication Year :
- 2021
- Publisher :
- BMJ, 2021.
-
Abstract
- RationaleUsing latent class analysis (LCA), two subphenotypes of acute respiratory distress syndrome (ARDS) have consistently been identified in five randomised controlled trials (RCTs), with distinct biological characteristics, divergent outcomes and differential treatment responses to randomised interventions. Their existence in unselected populations of ARDS remains unknown. We sought to identify subphenotypes in observational cohorts of ARDS using LCA.MethodsLCA was independently applied to patients with ARDS from two prospective observational cohorts of patients admitted to the intensive care unit, derived from the Validating Acute Lung Injury markers for Diagnosis (VALID) (n=624) and Early Assessment of Renal and Lung Injury (EARLI) (n=335) studies. Clinical and biological data were used as class-defining variables. To test for concordance with prior ARDS subphenotypes, the performance metrics of parsimonious classifier models (interleukin 8, bicarbonate, protein C and vasopressor-use), previously developed in RCTs, were evaluated in EARLI and VALID with LCA-derived subphenotypes as the gold-standard.ResultsA 2-class model best fit the population in VALID (p=0.0010) and in EARLI (pConclusionPreviously described subphenotypes are generalisable to unselected populations of non-trauma ARDS.
- Subjects :
- Pulmonary and Respiratory Medicine
medicine.medical_specialty
ARDS
Concordance
Acute Lung Injury
Population
Lung injury
law.invention
03 medical and health sciences
0302 clinical medicine
law
Internal medicine
Humans
Medicine
Prospective Studies
education
Prospective cohort study
Respiratory Distress Syndrome
education.field_of_study
business.industry
030208 emergency & critical care medicine
medicine.disease
Intensive care unit
Latent class model
030228 respiratory system
Latent Class Analysis
Observational study
business
Biomarkers
Subjects
Details
- ISSN :
- 14683296 and 00406376
- Volume :
- 77
- Database :
- OpenAIRE
- Journal :
- Thorax
- Accession number :
- edsair.doi.dedup.....e71ed66330dd784fa0b81beb744df2cd
- Full Text :
- https://doi.org/10.1136/thoraxjnl-2021-217158