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Predicting prolonged hospitalization in asthma patients: model development and external validation.

Xinkai Ma^1,2, Peiqi Li^1,2, Yupeng Li^1,2

¹The Second Hospital of Shanxi Medical University, Taiyuan, China.

The Journal of Asthma : Official Journal of the Association for the Care of Asthma

|May 3, 2025

Summary

This study developed an effective machine learning model to predict prolonged hospital stays in asthma patients. The Extreme Gradient Boosting model identified key predictors for better patient management.

Keywords:

Prolonged hospitalization XGBoost anxiety comorbid pneumonia length of stay machine learning prediction model

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Published on: May 14, 2012

Area of Science:

Artificial Intelligence in Medicine
Clinical Informatics
Respiratory Medicine

Background:

Prolonged hospitalization in asthma patients poses a significant clinical and economic burden.
Accurate prediction of prolonged stays is crucial for resource allocation and patient care optimization.

Purpose of the Study:

To develop and validate a machine learning (ML) model for predicting prolonged hospitalization in asthma patients.
To identify key clinical factors associated with extended hospital stays.

Main Methods:

A retrospective cohort study involving 2820 asthma patients for internal validation and 1714 patients for external validation.
Utilized LASSO and logistic regression for feature selection, employing nine ML algorithms.
The Extreme Gradient Boosting (XGBoost) model was selected based on performance metrics.

Main Results:

The XGBoost model demonstrated strong predictive performance with an AUC of 0.829 (internal) and 0.745 (external).
Key predictors included age, oxygen saturation, red blood cell count, hemoglobin, and comorbidities like pneumonia and COPD.
Decision curve analysis confirmed the model's good clinical utility.

Conclusions:

The XGBoost model effectively predicts prolonged hospitalization in asthma patients.
This tool can aid clinicians in identifying at-risk individuals for proactive management.