# Eosinophil count combined with routine indicators enhances early risk prediction value for ARDS in ICU patients: a retrospective cohort study

## TL;DR

Adding eosinophil count to standard clinical indicators improves early prediction of ARDS risk in ICU patients.

## Contribution

Eosinopenia enhances early ARDS risk prediction when combined with routine clinical indicators in ICU patients.

## Key findings

- Model 3 (including BMI and diabetes) showed the best predictive performance with a C-statistic of 0.729.
- Eosinopenia provided incremental predictive value despite not being an independent ARDS risk factor.
- Age, pneumonia, and sepsis were identified as independent ARDS risk factors.

## Abstract

This study aims to systematically evaluate the incremental value of admission eosinopenia EOS(−) in enhancing the predictive efficacy of early-stage acute respiratory distress syndrome (ARDS) risk models for ICU patients.

This study employed a single-center retrospective cohort design, enrolling 482 adult ICU patients between 2020 and 2023. Using Cox proportional hazards regression, we constructed and compared three models: Model 1 (baseline clinical model) included age, pneumonia, sepsis, and SOFA score; Model 2 added lymphocytes and eosinophils to Model 1; Model 3 further adjusted for BMI and diabetes. Predictive performance was assessed using the C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and validated internally.

C-statistics for Models 1, 2, and 3 were 0.683, 0.710, and 0.729, respectively. Model 3 demonstrated optimal performance (NRI=0.185, p=0.004), with an adjusted C-statistic of 0.695. Multivariate analysis identified age, pneumonia, and sepsis as independent ARDS risk factors, while EOS(−) showed non-independence. Results from competing risks analysis were consistent with the primary analysis conclusions.

Although eosinopenia is not an independent predictor of ARDS, it provides incremental information for early risk stratification based on conventional clinical factors, thereby aiding in the identification of high-risk patients.