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Background <p>Ventilator-associated pneumonia (VAP) represents a complication occurring in patients undergoing mechanical ventilation. This study aimed to develop a Bayesian network model to identify factors associated with the occurrence of VAP.</p> Methods <p>A retrospective cohort analysis was conducted using data from patients aged ≥ 60 years who underwent mechanical ventilation in the Department of Intensive care unit at the Second Hospital of Shanxi Medical University between June 2018 and June 2022. Collected variables included demographic characteristics, clinical conditions, medication use, catheterization-related data, laboratory findings, nursing-related and mechanical ventilation-related data. The chi-square test and logistic regression analysis were applied to determine factors associated with VAP. A Bayesian network model was subsequently constructed using the max-min hill-climbing algorithm.</p> Results <p>A total of 502 patients were included, comprising 332 males and 170 females, with a median age of 72 years (range: 60–100 years). The prevalence of VAP was 9.6% (48/502 patients). The constructed Bayesian network included 10 nodes and 17 directed edges. Direct associations with VAP were identified for antibiotic use exceeding three agents, reintubation, mechanical ventilation methods, and an Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 15. Indirect associations were observed for disease category, diabetes mellitus, presence of an indwelling central venous catheter, transfusion, and corticosteroid administration. The area under the receiver operating characteristic curve for the Bayesian network model was 0.84 (95% CI: 0.79–0.90).</p> Conclusion <p>The Bayesian network model elucidates the interrelationships among multiple factors associated with VAP. The model may provide ancillary information to help clinicians identify patient profiles associated with higher VAP risk and facilitate the implementation of early during mechanical ventilation.</p>

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Bayesian network model for identification of factors associated with ventilator-associated pneumonia in mechanically ventilated patients in the ICU: retrospective cohort study

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  • 发表日期：

    2026年7月18日

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  • Liyu Zhang,
  • Cailing Wang,
  • Jing He,
  • Xiuxiu Li,
  • Wenyan Wang,
  • Yuping Tang,
  • Yinghui Zhang

摘要

Background

Ventilator-associated pneumonia (VAP) represents a complication occurring in patients undergoing mechanical ventilation. This study aimed to develop a Bayesian network model to identify factors associated with the occurrence of VAP.

Methods

A retrospective cohort analysis was conducted using data from patients aged ≥ 60 years who underwent mechanical ventilation in the Department of Intensive care unit at the Second Hospital of Shanxi Medical University between June 2018 and June 2022. Collected variables included demographic characteristics, clinical conditions, medication use, catheterization-related data, laboratory findings, nursing-related and mechanical ventilation-related data. The chi-square test and logistic regression analysis were applied to determine factors associated with VAP. A Bayesian network model was subsequently constructed using the max-min hill-climbing algorithm.

Results

A total of 502 patients were included, comprising 332 males and 170 females, with a median age of 72 years (range: 60–100 years). The prevalence of VAP was 9.6% (48/502 patients). The constructed Bayesian network included 10 nodes and 17 directed edges. Direct associations with VAP were identified for antibiotic use exceeding three agents, reintubation, mechanical ventilation methods, and an Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 15. Indirect associations were observed for disease category, diabetes mellitus, presence of an indwelling central venous catheter, transfusion, and corticosteroid administration. The area under the receiver operating characteristic curve for the Bayesian network model was 0.84 (95% CI: 0.79–0.90).

Conclusion

The Bayesian network model elucidates the interrelationships among multiple factors associated with VAP. The model may provide ancillary information to help clinicians identify patient profiles associated with higher VAP risk and facilitate the implementation of early during mechanical ventilation.