Development and External Validation of a Web-Based Application for the Prediction of Pneumonia-Associated ARDS

Acute respiratory distress syndrome (ARDS) is a highly heterogeneous disease with clinical manifestations that may overlap with severe pneumonia, posing challenges for accurate differentiation. Therefore, early prediction and bedside rapid subtype clustering of ARDS patients are urgently needed. This study aims to develop a web-based system, which includes validated models of early bedside diagnosis and clinical subgroup classification, for predicting the development and phenotypes of pneumonia-associated ARDS. Diagnostic and subgroup models were developed and validated from the two large databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) and Telehealth Intensive Care Unit (eICU) and were incorporated into a web-based prediction system. Data from patients with pneumonia hospitalized for more than 24 h between 2008 and 2019 were analyzed. The MIMIC-IV derivation cohort included 24,987 patients with pneumonia (14,121 with pneumonia-associated ARDS); the eICU verification cohort included 20,676 patients with pneumonia (9946 with pneumonia-associated ARDS). In diagnosis, the stacking method of machine learning performed best with an AUC of 0.919, an accuracy of 70.00%, a precision of 69.88% and a recall of 82.27% in the MIMIC-IV derivation cohort. The AUC, accuracy, precision, and recall of the eICU validation cohort were 0.915, 70.87%, 69.70% and 69.70% respectively. Pneumonia-associated ARDS was classified into three clinical phenotypes with different clinical characteristics and outcomes, all of which responded differently to treatment. Among patients in clusters 0 and 1, the in-hospital mortality rates were higher among those who received early corticosteroid treatment than among those who did not, whereas among patients in cluster 2, the in-hospital mortality rate was lower among those who received corticosteroids than among those who did not. We performed a web transformation of the diagnosis prediction and clinical subgroup classification of pneumonia-associated ARDS. Our web-based models of early bedside diagnosis and clinical subgroup classification of pneumonia-associated ARDS may assist clinicians in diagnosing and treating the disease and in promoting individualized precision treatment.