ArticleFrontiers in medicine2024
Identifying risk factors for acute respiratory distress syndrome in critically ill patients: a retrospective study.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for the occurrence and mortality of sepsis-associated lung injury: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Association of peripheral blood lymphocytes and immunoglobulins with disease severity in pediatric acute respiratory distress syndrome: an age-stratified retrospective study.BMC pediatrics · 2026Article
- Independent predictors of long-term pulmonary function recovery in patients with acute respiratory distress syndrome: a meta-analysis.American journal of translational research · 2026Review
- ICU 'Magic Numbers': The Role of Biomarkers in Supporting Clinical Decision-Making.Diagnostics (Basel, Switzerland) · 2025Review
- An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.Frontiers in medicine · 2025Article
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Authors and funding
15 authors.
Funding
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Abstract
Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition that can develop in critically ill patients. Early identification of risk factors associated with ARDS development is essential for timely intervention and improved patient outcomes. This study aimed to investigate the potential predictors of ARDS in critically ill patients admitted to the intensive care unit (ICU). Methods: We conducted a retrospective study involving 502 critically ill patients admitted to the ICUs of three hospitals. Demographic and clinical data, including laboratory test results, were collected during their ICU stay. Multivariable logistic regression analysis was performed to identify independent risk factors associated with the development of ARDS. Results: Among the 502 critically ill patients, 104 (20.7%) patients developed ARDS during their ICU stay, with a median time to development of 5.2 days. Multivariable logistic regression analysis revealed that age (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.01-1.13; Conclusions: Our study identified age, CRP, T lymphocyte count, and IL-6 as independent predictors of ARDS in critically ill patients admitted to the ICU. These findings highlight the importance of monitoring these parameters in critically ill patients to identify those at high risk of developing ARDS. Early recognition and intervention based on these risk factors may improve patient outcomes in the ICU setting. Further prospective studies are warranted to validate these results and develop a reliable predictive model for ARDS in critically ill patients.
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