Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke
BackgroundStroke-associated pneumonia (SAP) remains a neglected area despite its high morbidity and mortality. We aimed to establish an easy-to-use model for predicting SAP.MethodsTwo hundred seventy-five acute ischemic stroke (AIS) patients were enrolled, and 73 (26.55%) patients were diagnosed wit...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1505270/full |
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| author | Lulu Zhang Qi Wang Qi Wang Yidan Li Qi Fang Xiang Tang |
| author_facet | Lulu Zhang Qi Wang Qi Wang Yidan Li Qi Fang Xiang Tang |
| author_sort | Lulu Zhang |
| collection | DOAJ |
| description | BackgroundStroke-associated pneumonia (SAP) remains a neglected area despite its high morbidity and mortality. We aimed to establish an easy-to-use model for predicting SAP.MethodsTwo hundred seventy-five acute ischemic stroke (AIS) patients were enrolled, and 73 (26.55%) patients were diagnosed with SAP. T-test, Chi-square test and Fisher’s exact test were used to investigate the associations of patient characteristics with pneumonia and its severity, and multivariable logistic regression models were used to construct a prediction scale.ResultsThree variables with the most significant associations, including age, NGT placement, and right cerebral hemisphere lesions combined with gender, were used to construct a stroke-associated pneumonia prediction scale with high accuracy (AUC = 0.93). Youden index of our SAP prediction model was 0.77. The sensitivity and specificity of our SAP prediction model were 0.89 and 0.88, respectively.ConclusionWe identified the best predictive model for SAP in AIS patients. Our study aimed to be as clinically relevant as possible, focusing on features that are routinely available. The contribution of selected variables is visually displayed through SHapley Additive exPlanations (SHAP). Our model can help to distinguish AIS patients of high-risk, provide specific management, reduce healthcare costs and prevent life-threatening complications and even death. |
| format | Article |
| id | doaj-art-c7a01ae3ab0a43b1b74fc499bcb06ea2 |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-c7a01ae3ab0a43b1b74fc499bcb06ea22025-02-07T13:18:36ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-02-011610.3389/fneur.2025.15052701505270Individualized prediction of stroke-associated pneumonia for patients with acute ischemic strokeLulu Zhang0Qi Wang1Qi Wang2Yidan Li3Qi Fang4Xiang Tang5Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaClinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Biostatistics, School of Public Health, Fudan University, Shanghai, ChinaDepartment of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Neurology, The First Affiliated Hospital of Soochow University, Suzhou, ChinaBackgroundStroke-associated pneumonia (SAP) remains a neglected area despite its high morbidity and mortality. We aimed to establish an easy-to-use model for predicting SAP.MethodsTwo hundred seventy-five acute ischemic stroke (AIS) patients were enrolled, and 73 (26.55%) patients were diagnosed with SAP. T-test, Chi-square test and Fisher’s exact test were used to investigate the associations of patient characteristics with pneumonia and its severity, and multivariable logistic regression models were used to construct a prediction scale.ResultsThree variables with the most significant associations, including age, NGT placement, and right cerebral hemisphere lesions combined with gender, were used to construct a stroke-associated pneumonia prediction scale with high accuracy (AUC = 0.93). Youden index of our SAP prediction model was 0.77. The sensitivity and specificity of our SAP prediction model were 0.89 and 0.88, respectively.ConclusionWe identified the best predictive model for SAP in AIS patients. Our study aimed to be as clinically relevant as possible, focusing on features that are routinely available. The contribution of selected variables is visually displayed through SHapley Additive exPlanations (SHAP). Our model can help to distinguish AIS patients of high-risk, provide specific management, reduce healthcare costs and prevent life-threatening complications and even death.https://www.frontiersin.org/articles/10.3389/fneur.2025.1505270/fullstroke-associated pneumoniaacute ischemic strokepredictioneasy-to-use modelSHapley Additive exPlanations |
| spellingShingle | Lulu Zhang Qi Wang Qi Wang Yidan Li Qi Fang Xiang Tang Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke Frontiers in Neurology stroke-associated pneumonia acute ischemic stroke prediction easy-to-use model SHapley Additive exPlanations |
| title | Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke |
| title_full | Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke |
| title_fullStr | Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke |
| title_full_unstemmed | Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke |
| title_short | Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke |
| title_sort | individualized prediction of stroke associated pneumonia for patients with acute ischemic stroke |
| topic | stroke-associated pneumonia acute ischemic stroke prediction easy-to-use model SHapley Additive exPlanations |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1505270/full |
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