Liu Hongbo, Xie Liyan, Xing Cong
The Municipal Hospital of Qingdao Cadre Health Section, Qingdao, Shandong 266000, China.
Qingdao Municipal Hospital, Health Care Clinic, Qingdao, Shandong 266000, China.
Open Life Sci. 2023 Dec 13;18(1):20220756. doi: 10.1515/biol-2022-0756. eCollection 2023.
This study analyzes the distribution of pathogenic bacteria and their antimicrobial susceptibilities in elderly patients with cardiovascular diseases to identify risk factors for pulmonary infections. A risk prediction model is established, aiming to serve as a clinical tool for early prevention and management of pulmonary infections in this vulnerable population. A total of 600 patients were categorized into infected and uninfected groups. Independent risk factors such as older age, diabetes history, hypoproteinemia, invasive procedures, high cardiac function grade, and a hospital stay of ≥10 days were identified through logistic regression. A predictive model was constructed, with a Hosmer-Lemeshow goodness of fit ( = 0.236) and an area under the receiver operating characteristic curve of 0.795, demonstrating good discriminative ability. The model had 63.40% sensitivity and 82.80% specificity, with a cut-off value of 0.13. Our findings indicate that the risk score model is valid for identifying high-risk groups for pulmonary infection among elderly cardiovascular patients. The study contributes to the early prevention and control of pulmonary infections, potentially reducing infection rates in this vulnerable population.
本研究分析老年心血管疾病患者病原菌的分布及其抗菌药敏情况，以确定肺部感染的危险因素。建立了风险预测模型，旨在作为这一脆弱人群肺部感染早期预防和管理的临床工具。共600例患者被分为感染组和未感染组。通过逻辑回归确定了年龄较大、糖尿病史、低蛋白血症、侵入性操作、心功能分级较高以及住院时间≥10天等独立危险因素。构建了一个预测模型，Hosmer-Lemeshow拟合优度（=0.236），受试者工作特征曲线下面积为0.795，显示出良好的判别能力。该模型的灵敏度为63.40%，特异度为82.80%，截断值为0.13。我们的研究结果表明，风险评分模型对于识别老年心血管疾病患者肺部感染的高危人群是有效的。该研究有助于肺部感染的早期预防和控制，可能降低这一脆弱人群的感染率。
