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. 2025 Jul 23:12:1627298.
doi: 10.3389/fmed.2025.1627298. eCollection 2025.

In-depth analysis of risk factors for postoperative pulmonary infection in patients with basal ganglia haemorrhage and construction of prediction model: based on domestic and international cutting-edge clinical research and big data analysis

Min Chen  1 , Longbiao Da  1 , Chun Huang  1 , Jie Liu  1 , Jian Tang  1 , Zhengjiang Zha  1
Affiliations

In-depth analysis of risk factors for postoperative pulmonary infection in patients with basal ganglia haemorrhage and construction of prediction model: based on domestic and international cutting-edge clinical research and big data analysis

Min Chen et al. Front Med (Lausanne). .

Abstract

Background: Basal ganglia haemorrhage is a common and serious cerebrovascular disease with a high rate of disability and mortality. Postoperative patients often face many complications, among which pulmonary infection is particularly prominent. Lung infections not only significantly prolong patients' hospital stay and increase healthcare costs, but also greatly affect the prognostic regression of patients, and may even lead to a rapid deterioration of the condition, which is one of the most important causes of death in patients with basal ganglia haemorrhage.

Objective: To investigate the high-risk factors for the development of postoperative pulmonary infections in patients with basal ganglia haemorrhage and to develop a predictive model.

Methods: A total of 317 patients were collected in this study, of which 126 patients developed postoperative lung infections; the patients enrolled in this study were randomly divided into a training set and a validation set according to the ratio of 7:3, of which 221 were in the training set and 96 were in the validation set. Past medical history, smoking and alcohol consumption, and relevant information during hospitalisation were collected separately to study the correlation factors affecting the emergence of postoperative lung infection in patients, and to establish a prediction model.

Results: The potentially relevant factors were included in a one-way logistic regression and after analysing the results, a history of smoking, duration of ventilator use, preoperative tracheal intubation, preoperative vomiting, and preoperative GCS (Glasgow Coma Scale) scores were identified as potential risk factors for the development of postoperative pulmonary infections in patients with basal ganglia haemorrhage, p < 0.2; The data obtained were further included in a multifactorial review, and smoking history, duration of ventilator use, preoperative tracheal intubation, preoperative vomiting, and preoperative GCS scores were independent risk factors for the development of postoperative pulmonary infections in patients with basal ganglia haemorrhage, p < 0.05.

Conclusion: The prediction model derived from this study provides a powerful tool for clinicians to identify patients at high risk of postoperative lung infection at an early stage.

Keywords: GCS score; basal ganglia haemorrhage; lung infection; predictive model; smoking.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Nomogram prediction of the risk of developing postoperative pulmonary infection in patients with basal ganglia haemorrhage.
Figure 2
Figure 2
Internal validation of nomograms: calibration curves.
Figure 3
Figure 3
Validation of nomograms: ROC curves. ROC, Receiver operating characteristic.
Figure 4
Figure 4
Decision curves in nomogram models.
See this image and copyright information in PMC

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