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
BackgroundBasal 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’...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1627298/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849421614246002688 |
|---|---|
| author | Min Chen Longbiao Da Chun Huang Jie Liu Jian Tang Zhengjiang Zha |
| author_facet | Min Chen Longbiao Da Chun Huang Jie Liu Jian Tang Zhengjiang Zha |
| author_sort | Min Chen |
| collection | DOAJ |
| description | BackgroundBasal 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.ObjectiveTo investigate the high-risk factors for the development of postoperative pulmonary infections in patients with basal ganglia haemorrhage and to develop a predictive model.MethodsA 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.ResultsThe 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-bd57717c487043e3990aceff376a3d4e |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-bd57717c487043e3990aceff376a3d4e2025-08-20T03:31:24ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.16272981627298In-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 analysisMin ChenLongbiao DaChun HuangJie LiuJian TangZhengjiang ZhaBackgroundBasal 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.ObjectiveTo investigate the high-risk factors for the development of postoperative pulmonary infections in patients with basal ganglia haemorrhage and to develop a predictive model.MethodsA 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.ResultsThe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1627298/fullbasal ganglia haemorrhagelung infectionsmokingGCS scorepredictive model |
| spellingShingle | Min Chen Longbiao Da Chun Huang Jie Liu Jian Tang Zhengjiang Zha 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 Frontiers in Medicine basal ganglia haemorrhage lung infection smoking GCS score predictive model |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | basal ganglia haemorrhage lung infection smoking GCS score predictive model |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1627298/full |
| work_keys_str_mv | AT minchen indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis AT longbiaoda indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis AT chunhuang indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis AT jieliu indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis AT jiantang indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis AT zhengjiangzha indepthanalysisofriskfactorsforpostoperativepulmonaryinfectioninpatientswithbasalgangliahaemorrhageandconstructionofpredictionmodelbasedondomesticandinternationalcuttingedgeclinicalresearchandbigdataanalysis |