Song Xiaodong, He Yang, Bai Jie, Zhang Jun
Department of Neurology, Peking University People's Hospital, Beijing, China.
Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Nutr. 2022 Oct 13;9:1009041. doi: 10.3389/fnut.2022.1009041. eCollection 2022.
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 ADS score with available nutrition-related parameters.
A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training ( = 887) and validation groups ( = 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.
The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the ADS score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, ADS-Nutrition, was constructed based on these five predictors. The AUROC for ADS-Nutrition (0.820, 95% CI: 0.794-0.845) was higher than the ADS score (0.691, 95% CI: 0.660-0.722) in the training group. Similarly, it showed a better predictive performance than the ADS 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 ADS-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the ADS score in both groups.
The ADS 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 ADS-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.
卒中相关性肺炎（SAP）是急性缺血性卒中（AIS）常见的并发症，会显著恶化预后。2型糖尿病（T2DM）可能导致营养不良，损害先天免疫功能，并增加AIS患者发生SAP的概率。我们旨在确定T2DM合并AIS患者发生SAP的早期预测因素，并通过将ADS评分与可用的营养相关参数相结合，构建一个专门用于预测该人群SAP的列线图。
回顾性纳入1330例连续的T2DM合并AIS患者。将患者随机分为训练组（n = 887）和验证组（n = 443）。采用单因素和多因素二元逻辑回归分析确定训练组中SAP的预测因素。根据确定的预测因素建立列线图。绘制受试者工作特征曲线（AUROC）下面积和校准图，以评估列线图的预测价值。应用决策曲线评估列线图的净效益。
训练组和验证组中SAP的发生率分别为9%和9.7%。结果显示，ADS评分、卒中分类、老年营养风险指数、血红蛋白和空腹血糖是SAP的独立预测因素。基于这五个预测因素构建了一个新的列线图ADS-Nutrition。训练组中ADS-Nutrition的AUROC（0.820，95%CI：0.794 - 0.845）高于ADS评分（0.691，95%CI：0.660 - 0.722）。同样，在验证组中，它显示出比ADS评分更好的预测性能[AUROC = 0.864（95%CI：0.828 - 0.894）vs. AUROC = 0.763（95%CI：0.720 - 0.801）]。这些结果在两组中校准良好。此外，决策曲线显示，与两组中的ADS评分相比，ADS-Nutrition为T2DM合并AIS患者提供了额外的净效益。
ADS评分、卒中分类、老年营养风险指数、血红蛋白和空腹血糖是T2DM合并AIS患者发生SAP的独立预测因素。因此，所提出的ADS-Nutrition可能是T2DM合并AIS患者发生SAP的一个简单可靠的预测模型。
