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

Summary

This study aims to develop and externally validate a web-based system integrating machine learning models for early diagnosis and clinical phenotyping of pneumonia-associated ARDS to facilitate precision treatment.

Abstract

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.

Introduction

Acute respiratory failure, especially acute respiratory distress syndrome (ARDS) after lung infection, is a common, devastating problem encountered in critically ill patients. Studies have shown that the incidence of ARDS is as high as 10% among patients in intensive care unit (ICU)¹, and the mortality rate is approximately 40%^2,3. Severe pneumonia is widely considered to be the main cause of ARDS⁴. Because the clinical symptoms of severe pneumonia and ARDS are similar, it is often difficult to distinguish ARDS from severe pneumonia. Therefore, early prediction o....

Protocol

This study accessed the Medical Information Mart for Intensive Care IV (MIMIC-IV) Database¹¹(Version 1.0, PhysioNet: https://physionet.org/content/mimiciv/1.0/) and Telehealth Intensive Care Unit (eICU) Database¹²(Version 2.0, PhysioNet: https://physionet.org/content/eicu-crd/2.0/) after completing the Protecting Human Research Participants examination (Record ID: 44151052). This study was conducted in accordance with the principles of the Declaration of Helsinki (2013), and patients had provided consent for their data to be captured in the two databases. Ethical approval was waived for this study because the data i....

Results

Participants
The MIMIC-IV database included data from 24,987 patients with pneumonia, of whom 14,121 had pneumonia-associated ARDS (Table 1). The eICU database included data from 20,676 patients with pneumonia, of whom 9946 had pneumonia-associated ARDS (Supplementary Table 1).

Establishment and verification of pneumonia-associated ARDS prediction model
We used the data of the MIMIC-IV cohort to construct a diagnos.......

Discussion

As we know, this is the first diagnostic model and clinical subgroup classification model using machine learning to report ARDS in pneumonia patients, and the largest study to report the diagnosis and clinical subgroup classification of pneumonia-associated ARDS. In this study, we derived and validated two machine learning-based models and translated them into web-based applications for clinical practice and subsequent research. In the eICU validation cohort, the prediction of which patients with pneumonia would develop .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Apache Tomcat	Apache software foundation	Version 9.0.85
Eclipse IDE	Eclipse	2023-09
Java Development Kit	Java	Version Java SE 8u381
RapidMiner Studio	Altair Engineering Inc.	Version 9.10.001
SPSS Statistics	IBM	Version 23.0