PDF(Pubmed)
Abstract：
UNASSIGNED: Stroke-associated pneumonia (SAP) commonly complicates acute ischemic stroke (AIS) and significantly worsens outcomes. Type 2 diabetes mellitus (T2DM) may contribute to malnutrition, impair innate immunity function, and increase the probability of SAP occurrence in AIS patients. We aimed to determine early predictors of SAP in AIS patients with T2DM and to construct a nomogram specifically for predicting SAP in this population by combining the A2DS2 score with available nutrition-related parameters.
UNASSIGNED: A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training (n = 887) and validation groups (n = 443). Univariate and multivariate binary logistic regression analyses were applied to determine the predictors of SAP in the training group. A nomogram was established according to the identified predictors. The areas under the receiver operating characteristic curve (AUROC) and calibration plots were performed to access the predictive values of the nomogram. The decision curve was applied to evaluate the net benefits of the nomogram.
UNASSIGNED: The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the A2DS2 score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, A2DS2-Nutrition, was constructed based on these five predictors. The AUROC for A2DS2-Nutrition (0.820, 95% CI: 0.794-0.845) was higher than the A2DS2 score (0.691, 95% CI: 0.660-0.722) in the training group. Similarly, it showed a better predictive performance than the A2DS2 score [AUROC = 0.864 (95% CI: 0.828-0.894) vs. AUROC = 0.763 (95% CI: 0.720-0.801)] in the validation group. These results were well calibrated in the two groups. Moreover, the decision curve revealed that the A2DS2-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the A2DS2 score in both groups.
UNASSIGNED: The A2DS2 score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP in AIS patients with T2DM. Thus, the proposed A2DS2-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.
UNASSIGNED: A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training (n = 887) and validation groups (n = 443). Univariate and multivariate binary logistic regression analyses were applied to determine the predictors of SAP in the training group. A nomogram was established according to the identified predictors. The areas under the receiver operating characteristic curve (AUROC) and calibration plots were performed to access the predictive values of the nomogram. The decision curve was applied to evaluate the net benefits of the nomogram.
UNASSIGNED: The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the A2DS2 score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, A2DS2-Nutrition, was constructed based on these five predictors. The AUROC for A2DS2-Nutrition (0.820, 95% CI: 0.794-0.845) was higher than the A2DS2 score (0.691, 95% CI: 0.660-0.722) in the training group. Similarly, it showed a better predictive performance than the A2DS2 score [AUROC = 0.864 (95% CI: 0.828-0.894) vs. AUROC = 0.763 (95% CI: 0.720-0.801)] in the validation group. These results were well calibrated in the two groups. Moreover, the decision curve revealed that the A2DS2-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the A2DS2 score in both groups.
UNASSIGNED: The A2DS2 score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP in AIS patients with T2DM. Thus, the proposed A2DS2-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.
摘要：
未经证实：卒中相关性肺炎(SAP)通常会使急性缺血性卒中(AIS)复杂化，并显著恶化转归。2型糖尿病（T2DM）可能导致营养不良，损害先天免疫功能，增加AIS患者发生SAP的概率。我们的目的是确定AIST2DM患者SAP的早期预测因子，并通过将A2DS2评分与可用的营养相关参数相结合，构建专门用于预测该人群SAP的列线图。
UNASSIGNED：回顾性招募了1,330例2型糖尿病患者。患者被随机分配到训练组（n=887）和验证组（n=443）。应用单变量和多变量二元逻辑回归分析来确定训练组中SAP的预测因子。根据确定的预测因子建立列线图。进行受试者工作特征曲线下面积(AUROC)和校准图以获得列线图的预测值。决策曲线用于评估列线图的净效益。
未经评估：SAP的发生率在训练组和验证组中分别为9%和9.7%，分别。结果显示，A2DS2评分，中风分类,老年营养风险指数，血红蛋白,快速血糖是SAP的独立预测因子。一个新颖的列线图,A2DS2-营养，是基于这五个预测因子构建的。训练组A2DS2-Nutrition的AUROC(0.820,95%CI:0.794-0.845)高于A2DS2评分(0.691,95%CI:0.660-0.722)。同样，它显示出比A2DS2评分更好的预测性能[AUROC=0.864（95％CI：0.828-0.894）与验证组AUROC=0.763(95%CI:0.720-0.801)]。这些结果在两组中都得到了很好的校准。此外，决策曲线显示,与A2DS2评分相比,A2DS2-Nutrition为2型糖尿病AIS患者提供了额外的净获益.
未经评估：A2DS2得分，中风分类,老年营养风险指数，血红蛋白,空腹血糖是2型糖尿病AIS患者SAP的独立预测因子。因此，提出的A2DS2-Nutrition可能是AIS合并T2DM患者SAP发生的简单可靠的预测模型.
UNASSIGNED：回顾性招募了1,330例2型糖尿病患者。患者被随机分配到训练组（n=887）和验证组（n=443）。应用单变量和多变量二元逻辑回归分析来确定训练组中SAP的预测因子。根据确定的预测因子建立列线图。进行受试者工作特征曲线下面积(AUROC)和校准图以获得列线图的预测值。决策曲线用于评估列线图的净效益。
未经评估：SAP的发生率在训练组和验证组中分别为9%和9.7%，分别。结果显示，A2DS2评分，中风分类,老年营养风险指数，血红蛋白,快速血糖是SAP的独立预测因子。一个新颖的列线图,A2DS2-营养，是基于这五个预测因子构建的。训练组A2DS2-Nutrition的AUROC(0.820,95%CI:0.794-0.845)高于A2DS2评分(0.691,95%CI:0.660-0.722)。同样，它显示出比A2DS2评分更好的预测性能[AUROC=0.864（95％CI：0.828-0.894）与验证组AUROC=0.763(95%CI:0.720-0.801)]。这些结果在两组中都得到了很好的校准。此外，决策曲线显示,与A2DS2评分相比,A2DS2-Nutrition为2型糖尿病AIS患者提供了额外的净获益.
未经评估：A2DS2得分，中风分类,老年营养风险指数，血红蛋白,空腹血糖是2型糖尿病AIS患者SAP的独立预测因子。因此，提出的A2DS2-Nutrition可能是AIS合并T2DM患者SAP发生的简单可靠的预测模型.
