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Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A(2)DS(2) score and hyperglycemia
BACKGROUND: Stroke-associated pneumonia (SAP) is a common complication of cerebrovascular disease. The A(2)DS(2) score has been used to predict the risk of SAP. However, hyperglycemia is not included in this scale. The purpose of the present study was to explore whether the A(2)DS(2) scoring system...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876087/ https://www.ncbi.nlm.nih.gov/pubmed/31766993 http://dx.doi.org/10.1186/s12883-019-1497-x |
Sumario: | BACKGROUND: Stroke-associated pneumonia (SAP) is a common complication of cerebrovascular disease. The A(2)DS(2) score has been used to predict the risk of SAP. However, hyperglycemia is not included in this scale. The purpose of the present study was to explore whether the A(2)DS(2) scoring system and hyperglycemia could predict the risk of SAP more effectively than the conventional A(2)DS(2) scale. METHODS: This retrospective study enrolled 2552 patients with acute ischemic stroke. The A(2)DS(2) scores, fasting blood glucose level and blood glucose level on admission were collected. Regression analysis was used to identify the independent risk factors of SAP. ROC curve analysis was used to evaluate the specificity and sensitivity of the combined A(2)DS(2) score and fasting hyperglycemia for predicting SAP. RESULTS: Fasting hyperglycemia was an independent risk factor for SAP (OR = 2.95; 95% confidence interval: 2.11–4.12; P < 0.001). The area under curve of the combined A(2)DS(2) score and fasting hyperglycemia was significantly higher than that of the A(2)DS(2) score alone (0.814 vs. 0.793; P = 0.020). CONCLUSION: Fasting hyperglycemia is an independent risk factor for predicting SAP. Compared with the A2DS2 score, the modified A2DS2 score (combined A2DS2 score and fasting hyperglycemia) is more effective in predicting the risk of SAP. |
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