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Objective <p>The aim of this study is to investigate risk factors for acute respiratory distress syndrome (ARDS) in severe acute brain injury (SABI) patients and construct a nomogram-based predictive model.</p> Methods <p>A retrospective analysis was conducted on 200 SABI patients admitted to Lishui Hospital between January 2021 and April 2025, who were randomly allocated into training group (<i>n</i> = 140) and validation group (<i>n</i> = 60). ARDS risk factors were identified and incorporated into a predictive model. Model performance was evaluated via Receiver operating characteristic curve (ROC curve), calibration plots, and decision curve analysis (DCA).</p> Results <p>Multivariate logistic regression revealed three independent predictors of ARDS in SABI patients: Sepsis, PaO₂/FiO₂, Pulmonary infection, (all <i>P</i> &lt; 0.05). The area under the ROC curve (AUC) was 0.778 for the training set and 0.754 for the validation set. Calibration curves demonstrated good predicted-observation agreement, while DCA confirmed the clinical utility of the nomogram.</p> Conclusion <p>This study developed and validated a nomogram prediction model incorporating three variables: Sepsis, PaO₂/FiO₂ and Pulmonary infection. The model demonstrated good discriminative ability and calibration in predicting the risk of ARDS in patients with SABI, thereby facilitating early risk stratification and supporting clinical decision-making.</p>

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A nomogram for predicting the risk of acute respiratory distress syndrome in patients with severe acute brain injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Qiaoqiao You,
  • Chang Su,
  • Jianping Ye,
  • Miaomiao Chen,
  • JunLong Xu,
  • Xian Zhang,
  • Wen Hu

摘要

Objective

The aim of this study is to investigate risk factors for acute respiratory distress syndrome (ARDS) in severe acute brain injury (SABI) patients and construct a nomogram-based predictive model.

Methods

A retrospective analysis was conducted on 200 SABI patients admitted to Lishui Hospital between January 2021 and April 2025, who were randomly allocated into training group (n = 140) and validation group (n = 60). ARDS risk factors were identified and incorporated into a predictive model. Model performance was evaluated via Receiver operating characteristic curve (ROC curve), calibration plots, and decision curve analysis (DCA).

Results

Multivariate logistic regression revealed three independent predictors of ARDS in SABI patients: Sepsis, PaO₂/FiO₂, Pulmonary infection, (all P < 0.05). The area under the ROC curve (AUC) was 0.778 for the training set and 0.754 for the validation set. Calibration curves demonstrated good predicted-observation agreement, while DCA confirmed the clinical utility of the nomogram.

Conclusion

This study developed and validated a nomogram prediction model incorporating three variables: Sepsis, PaO₂/FiO₂ and Pulmonary infection. The model demonstrated good discriminative ability and calibration in predicting the risk of ARDS in patients with SABI, thereby facilitating early risk stratification and supporting clinical decision-making.