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Background <p>Intensive Care Unit (ICU) mortality risk is high in rheumatoid arthritis (RA) patients, yet effective prognostic tools remain scarce.</p> Objectives <p>To develop and evaluate a machine learning (ML)-based prognostic model for predicting hospital mortality in severe RA patients.</p> Methods <p>This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) databases, including 1,951 chest X-rays from 984 patients with RA. The primary outcome was all-cause in-hospital mortality. Radiomics features were extracted using PyRadiomics, with 74 features retained after quality control. Key features were selected using the Boruta algorithm and integrated with clinical variables to develop three modeling strategies: clinical-only, radiomics-only, and combined models. Nine ML algorithms were applied using a 60/40 training-test split with 10-fold cross-validation. To address class imbalance (mortality rate: 7.7%), the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) evaluated the predictive incremental value of imaging omics features combined with clinical data compared to single-modality data. SHapley Additive exPlanations (SHAP) was used to interpret model predictions.</p> Results <p>A total of 984 RA patients were included, of whom 76 (7.7%) experienced in-hospital mortality. The neural network model demonstrated superior performance, with an area under the curve (AUC) of 0.887 (95% CI: 0.752–0.934) in the training set and 0.800 (95% CI: 0.714–0.856) in the test set. The combined clinical-radiomics model showed significant incremental value compared to the clinical-only model (NRI: 0.3773, 95% CI: 0.3499–0.4047, <i>P</i> &lt; 0.001; IDI: 0.2303, 95% CI: 0.2197–0.2409, <i>P</i> &lt; 0.001) and the radiomics-only model (NRI: 0.2249, 95% CI: 0.1510–0.2988, <i>P</i> &lt; 0.001; IDI: 0.1939, 95% CI: 0.1655–0.2222, <i>P</i> &lt; 0.001). Key predictive features included blood urea nitrogen (BUN), Wavelet-LL 10th Percentile, and Wavelet-Haar HH Entropy.</p> Conclusion <p>The integrated ML model combining chest radiomics and clinical data effectively predicts mortality risk in critically ill RA patients, offering good generalizability and interpretability. It provides a practical, interpretable framework for clinical risk stratification and lays the foundation for the development of intelligent prognostic systems for patients with severe RA and resource allocation.</p>

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Explainable machine learning-based mortality prediction in critically ill patients with rheumatoid arthritis-associated lung disease: a radiomics and clinical data integration study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Yubing He,
  • Rui Liang,
  • Yawen Zhang,
  • Xinghui Li

摘要

Background

Intensive Care Unit (ICU) mortality risk is high in rheumatoid arthritis (RA) patients, yet effective prognostic tools remain scarce.

Objectives

To develop and evaluate a machine learning (ML)-based prognostic model for predicting hospital mortality in severe RA patients.

Methods

This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) databases, including 1,951 chest X-rays from 984 patients with RA. The primary outcome was all-cause in-hospital mortality. Radiomics features were extracted using PyRadiomics, with 74 features retained after quality control. Key features were selected using the Boruta algorithm and integrated with clinical variables to develop three modeling strategies: clinical-only, radiomics-only, and combined models. Nine ML algorithms were applied using a 60/40 training-test split with 10-fold cross-validation. To address class imbalance (mortality rate: 7.7%), the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) evaluated the predictive incremental value of imaging omics features combined with clinical data compared to single-modality data. SHapley Additive exPlanations (SHAP) was used to interpret model predictions.

Results

A total of 984 RA patients were included, of whom 76 (7.7%) experienced in-hospital mortality. The neural network model demonstrated superior performance, with an area under the curve (AUC) of 0.887 (95% CI: 0.752–0.934) in the training set and 0.800 (95% CI: 0.714–0.856) in the test set. The combined clinical-radiomics model showed significant incremental value compared to the clinical-only model (NRI: 0.3773, 95% CI: 0.3499–0.4047, P < 0.001; IDI: 0.2303, 95% CI: 0.2197–0.2409, P < 0.001) and the radiomics-only model (NRI: 0.2249, 95% CI: 0.1510–0.2988, P < 0.001; IDI: 0.1939, 95% CI: 0.1655–0.2222, P < 0.001). Key predictive features included blood urea nitrogen (BUN), Wavelet-LL 10th Percentile, and Wavelet-Haar HH Entropy.

Conclusion

The integrated ML model combining chest radiomics and clinical data effectively predicts mortality risk in critically ill RA patients, offering good generalizability and interpretability. It provides a practical, interpretable framework for clinical risk stratification and lays the foundation for the development of intelligent prognostic systems for patients with severe RA and resource allocation.