■吸入性肺炎(AP)是急性缺血性中风(AIS)患者死亡的主要原因。及早发现,诊断和有效的预防措施对改善患者预后至关重要。然而,缺乏预测AIS后AP发生的研究。本研究旨在识别风险因素并开发列线图模型以确定AIS后发生AP的概率。
■共纳入2016年1月1日至2022年8月20日复旦大学金山医院收治的3258例AIS患者。其中,307例患者诊断为AP(AP组),2951例患者组成对照组(NAP组)。进行单因素和多因素logistic回归分析以确定AIS后AP的相关危险因素。这些因素用于建立评分系统并使用R软件建立列线图模型。
■单因素分析显示20个因素与AIS后AP的发生发展显著相关(P<0.05)。这些因素进行了多因素logistic回归分析,确定年龄(老年人),美国国立卫生研究院卒中量表(NIHSS)评分,吞咽困难,心房颤动,心功能不全,肾功能不全,肝功能不全,空腹血糖(FBG)升高,C反应蛋白(CRP)升高,中性粒细胞百分比升高(NEUT%),和前白蛋白降低为独立危险因素。构建了包含这11个风险因素的列线图模型,C指数为0.872(95%CI:0.845-0.899),指示精度高。校准和临床决策分析证明了模型的可靠性和临床价值。
■包含年龄的列线图模型,NIHSS得分,吞咽困难,心房颤动,心功能不全,肾功能不全,肝功能不全,FBG,CRP,NEUT%,前白蛋白可有效预测AIS患者的AP风险。该模型为早期干预策略提供了指导,能够识别高风险个体,以便及时采取预防措施。
UNASSIGNED: Aspiration Pneumonia (AP) is a leading cause of death in patients with Acute Ischemic Stroke (AIS). Early detection, diagnosis and effective prevention measures are crucial for improving patient prognosis. However, there is a lack of research predicting AP occurrence after AIS. This study aimed to identify risk factors and develop a nomogram model to determine the probability of developing AP after AIS.
UNASSIGNED: A total of 3258 AIS patients admitted to Jinshan Hospital of Fudan University between January 1, 2016, and August 20, 2022, were included. Among them, 307 patients were diagnosed with AP (AP group), while 2951 patients formed the control group (NAP group). Univariate and multivariate logistic regression analyses were conducted to identify relevant risk factors for AP after AIS. These factors were used to establish a scoring system and develop a nomogram model using R software.
UNASSIGNED: Univariate analysis revealed 20 factors significantly associated (P < 0.05) with the development of AP after AIS. These factors underwent multivariate logistic regression analysis, which identified age (elderly), National Institute of Health Stroke Scale (NIHSS) score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, elevated Fasting Blood Glucose (FBG), elevated C-Reactive Protein (CRP), elevated Neutrophil percentage (NEUT%), and decreased prealbumin as independent risk factors. A nomogram model incorporating these 11 risk factors was constructed, with a C-index of 0.872 (95 % CI: 0.845-0.899), indicating high accuracy. Calibration and clinical decision analyses demonstrated the model\'s reliability and clinical value.
UNASSIGNED: A nomogram model incorporating age, NIHSS score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, FBG, CRP, NEUT%, and prealbumin effectively predicts AP risk in AIS patients. This model provides guidance for early intervention strategies, enabling the identification of high-risk individuals for timely preventive measures.