Dataset Information

Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A2DS2 score and hyperglycemia.

ABSTRACT: BACKGROUND:Stroke-associated pneumonia (SAP) is a common complication of cerebrovascular disease. The A2DS2 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 A2DS2 scoring system and hyperglycemia could predict the risk of SAP more effectively than the conventional A2DS2 scale. METHODS:This retrospective study enrolled 2552 patients with acute ischemic stroke. The A2DS2 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 A2DS2 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 A2DS2 score and fasting hyperglycemia was significantly higher than that of the A2DS2 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.

SUBMITTER: Li Y

PROVIDER: S-EPMC6876087 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A<sup>2</sup>DS<sup>2</sup> score and hyperglycemia.

Li Yang Y Zhang Yu Y Ma Liansheng L Niu Xiaoyuan X Chang Junsen J

BMC neurology 20191125 1

<h4>Background</h4>Stroke-associated pneumonia (SAP) is a common complication of cerebrovascular disease. The A<sup>2</sup>DS<sup>2</sup> 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<sup>2</sup>DS<sup>2</sup> scoring system and hyperglycemia could predict the risk of SAP more effectively than the conventional A<sup>2</sup>DS<sup>2</sup> scale.<h4>Methods</h4>This retrospective s ...[more]

PMID: 31766993

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Project description:PurposeWe develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).MethodWe applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups - 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC).ResultsThe incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739-0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694-0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762-0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774-0.794].ConclusionICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

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Dataset Information

Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A2DS2 score and hyperglycemia.

Publications

Risk of stroke-associated pneumonia during hospitalization: predictive ability of combined A<sup>2</sup>DS<sup>2</sup> score and hyperglycemia.

Similar Datasets

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