Endotyping in ARDS: one step forward in precision medicine
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View abstract on PubMed
Summary
This summary is machine-generated.Researchers identified three distinct subgroups of acute respiratory distress syndrome (ARDS) patients using clinical data and biomarkers. These ARDS patient groups show varying mortality rates, aiding personalized treatment strategies.
Area Of Science
- Critical Care Medicine
- Pulmonology
- Biomarker Discovery
Background
- The Berlin definition of ARDS relies solely on clinical criteria.
- Understanding ARDS pathobiology is crucial for personalized treatment approaches.
- This study aimed to define ARDS phenotypes/endotypes using clinical and pathophysiological data.
Purpose Of The Study
- To define and describe ARDS phenotypes/endotypes by integrating clinical and pathophysiological parameters.
- To identify distinct ARDS subgroups within a Canadian cohort.
- To explore the impact of these subgroups on patient mortality.
Main Methods
- A cohort of adult ARDS patients from Calgary, Canada, was analyzed.
- Plasma cytokine levels and clinical parameters were measured within 24 hours of ICU admission.
- A latent class model (LCM) was employed to identify ARDS subgroups and differentiating features.
Main Results
- The LCM identified three distinct ARDS subgroups (n=64, n=86, n=30) based on 23 differentiating features.
- Key discriminating features included IL-8, IL-6, IL-10, TNF-a, and serum lactate.
- Mortality rates varied significantly across the identified subgroups, with IL-8 and APACHE II strongly predicting mortality.
Conclusions
- Subgrouping ARDS patients using biomarkers and clinical data effectively categorizes this heterogeneous condition.
- The study identified three ARDS subgroups with differing mortality levels.
- This model holds potential for improving clinical trial design, prognostication, and treatment selection in ARDS.
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