Identifying risk factors for acute respiratory distress syndrome in critically ill patients: a retrospective study - Zhou, Yang; Li, Congye; Mei, Shuya; Xu, Qiaoyi; Qin, Shaojie; Feng, Jinhua; Wang, Jiemin; Xing, Shunpeng; Wang, Wei; Li, Feng; Zhou, Quanhong; He, Zhengyu; et al. (Frontiers in Medicine, 2024)
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).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.1.07; 95% confidence interval [CI], 1.01-1.13; P=0.002), C-reactive protein (CRP) levels (OR, 1.11; 95% CI, 1.05-1.17; P=0.013), T lymphocyte count (OR, 0.82; 95% CI, 0.69-0.93; P=0.011), and interleukin-6 (IL-6) levels (OR, 1.17; 95% CI, 1.08-1.23; P=0.003) were independently associated with the development of ARDS in critically ill patients.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.