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ARDS Mortality Prediction Model Using Evolving Clinical Data and Chest Radiograph Analysis.

Ana Cysneiros1,2, Tiago Galvão3, Nuno Domingues3

  • 1Nova Medical School, Universidade de Lisboa, 1649-004 Lisbon, Portugal.

Biomedicines
|February 24, 2024
PubMed
Summary
This summary is machine-generated.

Predicting mortality in COVID-19-associated ARDS (C-ARDS) is possible using machine learning with clinical data and chest X-rays. This approach aids in tailoring treatment and improving patient outcomes.

Keywords:
ARDSdeep learningimagingmachine learning

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Area of Science:

  • Pulmonary and Critical Care Medicine
  • Infectious Diseases
  • Medical Imaging Analysis

Background:

  • COVID-19-associated ARDS (C-ARDS) emerged in late 2019, necessitating research into its pathophysiology.
  • Phenotyping C-ARDS is crucial for understanding its heterogeneity and improving patient management.
  • SARS-CoV-2 significantly impacted ARDS cases, highlighting the need for specific predictive models.

Purpose of the Study:

  • To develop and validate a predictive model for C-ARDS mortality.
  • To investigate the utility of machine learning in analyzing chest X-ray (CXR) features for mortality prediction.
  • To assess the combined predictive power of clinical variables and imaging data.

Main Methods:

  • Retrospective analysis of 110 C-ARDS patients (April 2020 - February 2021).
  • Evaluation of ventilation settings, arterial blood gases (PaO2/FiO2 ratio), and CXR on days 1 and 3.
  • Utilized a convolutional neural network (CNN) for CXR image analysis.
  • Developed a binary logistic regression model incorporating age, P/F ratios, and CNN-extracted CXR features.

Main Results:

  • The study included 110 C-ARDS patients with a mean age of 63.2 years; 61.2% were male.
  • Severe ARDS occurred in 25% of patients, with an overall mortality rate of 47.3%.
  • The predictive model achieved an Area Under the Receiver Operating Characteristic (ROC) curve of 0.862 on test data.

Conclusions:

  • Integrating evolving P/F ratios and CXR data can predict C-ARDS mortality in intensive care units.
  • Machine learning applied to imaging can reveal hidden patterns for ARDS phenotyping.
  • Combined clinical and machine learning imaging features offer superior mortality prediction compared to individual components.