Blood eosinophils and mortality in patients with acute respiratory distress syndrome: A propensity score matching analysis

Abstract

BACKGROUND: The effect of blood eosinophils (EOSs) on mortality in acute respiratory distress syndrome (ARDS) patients and whether corticosteroids aff ect this eff ect are unclear. METHODS: The Medical Information Mart for Intensive Care III database (version 1.4) was used to extract data. Patients with ARDS were selected for inclusion. Cox regression models using the backward stepwise method and propensity score matching (PSM) were used to assess the relationship between blood EOS counts and 28-day mortality. RESULTS: A total of 2,567 patients with ARDS were included, and the 28-day mortality rate was 24.19%. The crude 28-day mortality was signifi cantly lower in patients with EOS counts ≥2% (18.60% [85/457] vs. 25.40% [536/2,110], P=0.002) than in those with EOS counts <2%. In the Cox regression model, the EOS counts ≥2% showed a significant association with the decreased 28-day mortality (hazard ratio [HR] 0.731; 95% confi dence interval [95% CI] 0.581-0.921, P=0.008). In the corticosteroid non-use subgroup, EOS counts ≥2% was signifi cantly related to decreased 28-day mortality (HR 0.697, 95% CI 0.535-0.909, P=0.008), but the result was not signifi cant in the corticosteroid non-use subgroup model (P=0.860). A total of 457 well-matched pairs were obtained by a 1:1 matching algorithm after PSM. The 28-day mortality remained signifi cantly lower in the EOS counts ≥2% group (18.60% [85/457] vs. 26.70% [122/457], P=0.003). CONCLUSIONS Higher EOS counts are related to lower 28-day mortality in ARDS patients, and this relationship can be counteracted by using corticosteroids.

Predictive Value of the Baseline and Early Changes in Blood Eosinophils for Short-Term Mortality in Patients with Acute Respiratory Distress Syndrome

“…In present study, we found that patients with high level of baseline eosinophils (>0.3%) had significantly lower 30-day mortality than non-survivors in univariate and multivariate analyses, which is similar to previous studies. 13 , 24 The results from Kaplan–Meier curve and random forest model also supported the above findings. Of note, when we regarded blood eosinophils as a continuous variable, no significant association was found in the multivariate logistic regression analysis.…”

“…Up to date, there are only two published studies that have described the relationship between blood eosinophils and clinical outcomes in patients with ARDS. 11 , 13 Zhu et al 11 observed that following clinical treatment, patients with increased levels of blood eosinophils at discharge are associated with increased survival. Moreover, Chen et al 13 found that patients with higher baseline eosinophils had lower 28-day mortality.…”

“… 11 , 13 Zhu et al 11 observed that following clinical treatment, patients with increased levels of blood eosinophils at discharge are associated with increased survival. Moreover, Chen et al 13 found that patients with higher baseline eosinophils had lower 28-day mortality. However, in the two studies, the blood eosinophils were only detected at a single time point, and were not evaluated for their dynamic changes.…”

“…The reason might be the distribution of baseline eosinophil values was skewed, with a high proportion of zero or extremely low data, and this was also evident in previous studies. 11 , 13 The statistical power for the logistic regression would be considerably influenced in the case of highly skewed data. 26 By using a categorical variable, the data processing is significantly simplified, which leads to improved robustness of the model.…”

Current status and trends in researches based on public intensive care databases: A scientometric investigation

“…One main subject of research based on public intensive care databases was to analyze whether some factors affected the prognosis or mortality of critically ill patients. Researches focused on several common diseases such as acute respiratory distress syndrome, sepsis shock, acute renal failure, intracerebral hemorrhage and myocardial infarction [11][12][13][14][15] [22]. Jingjie Feng et al developed an improved continuous BP estimation method with more accuracy and less calibration frequency utilizing hemodynamic covariates in addition to pulse transit time [23].…”