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<p>Extubation failure remains a major challenge in critically ill patients and is associated with adverse clinical outcomes. Current extubation decisions rely heavily on weaning tests and subjective interpretation of chest radiographs. This study aimed to develop a clinically feasible multimodal machine learning (ML) framework that integrates routinely available data to provide complementary support for extubation decision-making. A total of 921 individuals were included and classified into extubation-with-reintubation and extubation-without-reintubation groups. The proposed framework integrated baseline demographics, weaning measurements, radiographic assessments, and segmented post-intubation chest x-rays (i.e., tracheal, left lung, and right lung regions). Optimal base ML models for each modality were selected based on the area under the receiver operating characteristic curve and integrated using a stacking ensemble approach. Feature importance analyses were performed at both the modality and feature levels. The extubation-with-reintubation group comprised a higher proportion of elderly patients with higher Charlson comorbidity index scores than the extubation-without-reintubation group. Individuals requiring reintubation exhibited significantly higher respiratory rates, lower tidal volumes, greater rapid shallow breathing indices, and longer intervals from intubation to weaning tests and extubation (all <i>p</i> &lt; 0.01). The multimodal ensemble outperformed rule-based and single-modality models, achieving an accuracy of 79.46%. Weaning measurements, demographics, and radiographic assessments were the most influential contributors to extubation outcome prediction. A multimodal ML framework integrating segmented post-intubation chest x-rays with routinely collected clinical data shows potential as a complementary, objective decision-support tool for extubation without requiring additional measurements. Prospective studies are needed to further validate these findings.</p>

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Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Kun-Ta Lee,
  • Haseeb Ali,
  • I-Jung Liu,
  • Wen-Te Liu,
  • Rachel Chien,
  • Ying-Ying Chen,
  • Yen-Ling Chen,
  • Ping-Chung Pao,
  • Pei-Shan Luo,
  • Kuan-Yuan Chen,
  • Kang-Yun Lee,
  • Tzu-Tao Chen,
  • Arnab Majumdar,
  • Jiunn-Horng Kang,
  • Po-Hao Feng,
  • Cheng-Yu Tsai

摘要

Extubation failure remains a major challenge in critically ill patients and is associated with adverse clinical outcomes. Current extubation decisions rely heavily on weaning tests and subjective interpretation of chest radiographs. This study aimed to develop a clinically feasible multimodal machine learning (ML) framework that integrates routinely available data to provide complementary support for extubation decision-making. A total of 921 individuals were included and classified into extubation-with-reintubation and extubation-without-reintubation groups. The proposed framework integrated baseline demographics, weaning measurements, radiographic assessments, and segmented post-intubation chest x-rays (i.e., tracheal, left lung, and right lung regions). Optimal base ML models for each modality were selected based on the area under the receiver operating characteristic curve and integrated using a stacking ensemble approach. Feature importance analyses were performed at both the modality and feature levels. The extubation-with-reintubation group comprised a higher proportion of elderly patients with higher Charlson comorbidity index scores than the extubation-without-reintubation group. Individuals requiring reintubation exhibited significantly higher respiratory rates, lower tidal volumes, greater rapid shallow breathing indices, and longer intervals from intubation to weaning tests and extubation (all p < 0.01). The multimodal ensemble outperformed rule-based and single-modality models, achieving an accuracy of 79.46%. Weaning measurements, demographics, and radiographic assessments were the most influential contributors to extubation outcome prediction. A multimodal ML framework integrating segmented post-intubation chest x-rays with routinely collected clinical data shows potential as a complementary, objective decision-support tool for extubation without requiring additional measurements. Prospective studies are needed to further validate these findings.