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. 2025 Aug 30;25(1):415.
doi: 10.1186/s12890-025-03879-4.

A nomogram for individualized prediction of acute respiratory distress syndrome in patients with severe traumatic brain injury: a retrospective cohort study

Zixuan Wang  1   2 , Yan Xiao  3 , Min Zhu  1 , Siyao Xu  1 , Yuan Zhong  1 , Xiaohong Liu  4   5 , Jinqiang Zhuang  6
Affiliations

A nomogram for individualized prediction of acute respiratory distress syndrome in patients with severe traumatic brain injury: a retrospective cohort study

Zixuan Wang et al. BMC Pulm Med. .

Abstract

Background: The prognosis of patients with a concomitance of severe traumatic brain injury (sTBI) and acute respiratory distress syndrome (ARDS) is poor, and early identification of such patients can provide diagnostic and therapeutic assistance for clinical treatment. However, few studies have been conducted to identify the risk of ARDS in patients with sTBI. This study aimed to construct a risk prediction model for ARDS in patients with sTBI and evaluate its efficacy.

Methods: From 2016 to 2023, 502 patients diagnosed with sTBI were selected from the Affiliated Hospital of Yangzhou University. All participants were randomly allocated to either the training or validation group. Feature selection for constructing the prediction model and developing a nomogram was carried out using the least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analysis. The effectiveness and clinical relevance of the model were evaluated using receiver operating characteristic (ROC) curves, the area under the ROC curve (AUC), calibration curves, and the decision curve analysis (DCA).

Results: The study found that 32.9% of patients with sTBI developed ARDS. The model was established based on oxygen saturation measured by pulse oximetry (SpO2), pneumonia, and fluid volume in the first 24 h. The model showed good discriminative ability with AUC values of 0.841 for the training and 0.821 for the validation groups. Calibration curves demonstrated that the predicted results align well with the actual results. The DCA suggested that the nomogram could lead to clinically beneficial outcomes at a significant threshold.

Conclusions: The diagnostic nomogram for ARDS in sTBI patients demonstrated satisfactory predictive value, assisting clinicians in identifying high-risk patients for ARDS.

Trail registration: ChiCTR2400085916.

Keywords: Acute respiratory distress syndrome; Nomogram; Prediction model; Severe traumatic brain injury.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This retrospective analysis was reviewed and authorized by the Ethics Committee of School of Nursing, Yangzhou University (YZUHL20230023). This study was registered on Chinese Clinical Trial Registry (ChiCTR2400085916). Informed consent was waived due to the anonymity of the data. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study flowchart. TBI, traumatic brain injury; GCS, glasgow coma scale
Fig. 2
Fig. 2
Risk predictors selection using the LASSO regression model. A LASSO coefficient profiles of the 54 features. A coefficient profile plot was produced against the log (l) sequence. B Plot of the results of cross-validation, and the red dots in the figure represent the target parameters corresponding to each lambda. The largest lambda value is chosen when the cross-validation error is within one standard error of the minimum
Fig. 3
Fig. 3
Nomogram predictive model of ARDS in patients with sTBI. ARDS, acute respiratory distress syndrome; sTBI, severe traumatic brain injury; SpO2, oxygen saturation as measured by pulse oximetry
Fig. 4
Fig. 4
ROC curves. A ROC curve plot in the training group; (B) ROC curve plot in the validation group. ROC, receiver operating characteristic; AUC, area under the ROC curve
Fig. 5
Fig. 5
Calibration curves. A calibration curve plot in the training group; (B) calibration curve plot in the validation group
Fig. 6
Fig. 6
DCA curves. A DCA curve plot in the training group; (B) DCA curve calibration curve plot in the validation. DCA, decision curve analysis
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