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Machine Learning Predicts ICU In-Hospital Mortality in ARDS Patients Aged 80 and Above: A Multinational Multicenter

Xiaozhu Liu¹, Xiangyu Sun², Xinhe Zhou³

¹Department of Critical Care Medicine, Clinical and Research Center on Acute Lung Injury, Emergency and Critical Care Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

Shock (Augusta, Ga.)

|April 8, 2026

Summary

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This summary is machine-generated.

This study developed an interpretable machine learning model to predict mortality in elderly patients with acute respiratory distress syndrome (ARDS) in the ICU. The Random Forest model accurately stratified patient risk, outperforming traditional methods.

Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Critical Care Medicine

Background:

Elderly patients with Acute Respiratory Distress Syndrome (ARDS) admitted to the Intensive Care Unit (ICU) have high mortality rates.
Accurate prediction and risk stratification are crucial for managing this vulnerable population.

Purpose of the Study:

To develop and validate an interpretable machine learning (ML) model for predicting mortality in ICU patients over 80 with ARDS.
To enable effective clinical risk stratification for this patient group.

Main Methods:

Utilized a multicenter cohort including data from Chinese medical institutions and the MIMIC-IV database.
Eight distinct ML models were trained and evaluated using metrics like AUC.
The best-performing model (Random Forest) was selected and interpreted using SHAP analysis.

Keywords:

ARDS MIMIC-IV Machine learning Prognostic prediction mortality

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Main Results:

The Random Forest model achieved the highest performance with an AUC of 0.835.
The ML model accurately predicted mortality and stratified patients by risk.
The Random Forest model significantly outperformed the APACHE II score and oxygenation index-based classification.

Conclusions:

An interpretable ML model was successfully developed for predicting mortality in elderly ARDS ICU patients.
The model provides accurate risk predictions and stratification, supported by SHAP analysis.
This offers a scientific basis for improved clinical management of high-risk ARDS patients.