Evaluating the impact of ESICM 2023 guidelines and the new global definition of ARDS on clinical outcomes: insights from MIMIC-IV cohort data.

Abstract

Background: In 2023, the European Society of Intensive Care Medicine (ESICM) recommended updated criteria for acute respiratory distress syndrome (ARDS). In 2024, Matthay et al. updated the global ARDS definition in AJRCCM, titled "A New Global Definition of Acute Respiratory Distress Syndrome." However, the impact of this new definition on ARDS treatments is currently unknown.

Objective: This study aims to determine the effect of the new ARDS definition on patients with hypoxemic respiratory failure and study the heterogeneity of patients in the new definition to guide treatment.

Methods: Clinical consultation data from the Medical Information Mart for Intensive Care IV database were extracted using Structured Query Language based on the PostgreSQL tool (version 10.0). Data were analyzed using Python (version 3.9) and the deep learning framework Pytorch. Kaplan-Meier survival analysis was used to compare survival between the old and new definitions. A hierarchical clustering approach was applied to identify potential ARDS clinical subtypes.

Results: The new definition diagnosed ARDS earlier and included individuals with lower mortality rates compared with the Berlin definition. Patients meeting the new definition but not the Berlin criteria exhibited a favorable response to non-invasive ventilation strategies (p = 0.009). The XGBoost classifier, trained to predict subphenotypes, achieved an AUC of 0.88 ± 0.02 on the training set. Additionally, mortality was significantly associated with patients with hypoxemia compared with survivors, particularly regarding respiratory parameters. Easily accessible metrics, such as respiratory rate and urea nitrogen (BUN), can help diagnose ARDS in high-risk populations in resource-limited settings.

Conclusions: The new ARDS definition offers advantages in earlier detection, more accurate grading, and more precise diagnosis in resource-limited settings compared with the Berlin definition. This study also established a robust prediction model for early ARDS identification, improving the patient prognosis and reducing the mortality rate.