Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models
Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing healthcare burden. AIS patients are often accompanied by basic diseases, and atrial fibrillation (AF) is one of the common basic diseases. Despite the...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1595101/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850131611579842560 |
|---|---|
| author | Tai Su Peng Zhang Peng Zhang Bingyin Zhang Zihao Liu Zexing Xie Xiaomei Li Jixiang Ma Tao Xin |
| author_facet | Tai Su Peng Zhang Peng Zhang Bingyin Zhang Zihao Liu Zexing Xie Xiaomei Li Jixiang Ma Tao Xin |
| author_sort | Tai Su |
| collection | DOAJ |
| description | Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing healthcare burden. AIS patients are often accompanied by basic diseases, and atrial fibrillation (AF) is one of the common basic diseases. Despite the high prevalence of AF in AIS patients, few studies have specifically addressed SAP prediction in this comorbid population. We aimed to analyze the factors influencing the occurrence of SAP in patients with AIS and AF and to assess the risk of SAP development through an optimal predictive model. We performed a case-control study. This study included 4,496 hospitalized patients with AIS and AF in China between January 2020 and September 2023. The primary outcome was SAP during hospitalization. Univariate analysis and LASSO regression analysis methods were used to screen predictors. The patients with AIS and AF were randomly divided into a training set, validation set, and test set. Then, we established logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. The accuracy, sensitivity, specificity, area under the curve, Youden index and F1 score were adopted to evaluate the predictive value of each model. The optimal prediction model was visualized using a nomogram. In this study, SAP was identified in 10.16% of cases. The variables screened by univariate analysis and LASSO regression, variables such as coronary artery disease, hypertension, and dysphagia, identified by univariate and LASSO regression analyses (p < 0.05), were included in the LR, RF, and SVM. The LR model outperformed other models, achieving an AUC of 0.866, accuracy of 90.13%, sensitivity of 79.49%, specificity of 86.11%, F1 score of 0.80. A nomogram based on the LR model was developed to predict SAP risk, providing a practical tool for early identification of high-risk patients, and enabling targeted interventions to reduce SAP incidence and improve outcomes. |
| format | Article |
| id | doaj-art-16feb75b824f4e8b80efc75813c671eb |
| institution | OA Journals |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-16feb75b824f4e8b80efc75813c671eb2025-08-20T02:32:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-05-01810.3389/frai.2025.15951011595101Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning modelsTai Su0Peng Zhang1Peng Zhang2Bingyin Zhang3Zihao Liu4Zexing Xie5Xiaomei Li6Jixiang Ma7Tao Xin8School of Public Health and Management, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, ChinaDepartment of Neurology, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaShandong Institute of Neuroimmunology, Jinan, ChinaShandong Provincial Center for Disease Control and Prevention, Jinan, ChinaDepartment of Neurosurgery, Shandong Provincial Hospital, Shandong First Medical University, Jinan, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaSchool of Public Health and Management, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, ChinaShandong Provincial Center for Disease Control and Prevention, Jinan, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaStroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing healthcare burden. AIS patients are often accompanied by basic diseases, and atrial fibrillation (AF) is one of the common basic diseases. Despite the high prevalence of AF in AIS patients, few studies have specifically addressed SAP prediction in this comorbid population. We aimed to analyze the factors influencing the occurrence of SAP in patients with AIS and AF and to assess the risk of SAP development through an optimal predictive model. We performed a case-control study. This study included 4,496 hospitalized patients with AIS and AF in China between January 2020 and September 2023. The primary outcome was SAP during hospitalization. Univariate analysis and LASSO regression analysis methods were used to screen predictors. The patients with AIS and AF were randomly divided into a training set, validation set, and test set. Then, we established logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. The accuracy, sensitivity, specificity, area under the curve, Youden index and F1 score were adopted to evaluate the predictive value of each model. The optimal prediction model was visualized using a nomogram. In this study, SAP was identified in 10.16% of cases. The variables screened by univariate analysis and LASSO regression, variables such as coronary artery disease, hypertension, and dysphagia, identified by univariate and LASSO regression analyses (p < 0.05), were included in the LR, RF, and SVM. The LR model outperformed other models, achieving an AUC of 0.866, accuracy of 90.13%, sensitivity of 79.49%, specificity of 86.11%, F1 score of 0.80. A nomogram based on the LR model was developed to predict SAP risk, providing a practical tool for early identification of high-risk patients, and enabling targeted interventions to reduce SAP incidence and improve outcomes.https://www.frontiersin.org/articles/10.3389/frai.2025.1595101/fullacute ischemic strokeatrial fibrillationstroke-associated pneumoniamachine learning modelnomogram |
| spellingShingle | Tai Su Peng Zhang Peng Zhang Bingyin Zhang Zihao Liu Zexing Xie Xiaomei Li Jixiang Ma Tao Xin Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models Frontiers in Artificial Intelligence acute ischemic stroke atrial fibrillation stroke-associated pneumonia machine learning model nomogram |
| title | Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| title_full | Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| title_fullStr | Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| title_full_unstemmed | Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| title_short | Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| title_sort | risk prediction of stroke associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models |
| topic | acute ischemic stroke atrial fibrillation stroke-associated pneumonia machine learning model nomogram |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1595101/full |
| work_keys_str_mv | AT taisu riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT pengzhang riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT pengzhang riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT bingyinzhang riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT zihaoliu riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT zexingxie riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT xiaomeili riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT jixiangma riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels AT taoxin riskpredictionofstrokeassociatedpneumoniainacuteischemicstrokewithatrialfibrillationusingmachinelearningmodels |