| 罗艳琳,卢豫川,贾钦尧,等.慢性阻塞性肺疾病病人呼吸机相关性肺炎 91例的病原谱及其列线图预测模型[J].安徽医药,2024,28(1):129-133. |
| 慢性阻塞性肺疾病病人呼吸机相关性肺炎 91例的病原谱及其列线图预测模型 |
| Pathogen spectrum and nomogram prediction model in 91 cases of ventilator-associated pneumonia in patients with chronic obstructive pulmonary disease |
| |
| DOI：10.3969/j.issn.1009-6469.2024.01.027 |
| 中文关键词: 肺疾病，慢性阻塞性 肺炎，呼吸机相关性 危险因素 细菌感染和真菌病 列线图 预测 |
| 英文关键词: Pulmonary disease, chronic obstructive Pneumonia, ventilator-associated Risk factors Bacterial infections and my-coses Nomogram Prediction |
| 基金项目: |
|
| 摘要点击次数: 218 |
| 全文下载次数: 101 |
| 中文摘要: |
| 目的分析慢性阻塞性肺疾病( COPD)病人呼吸机相关性肺炎( VAP)病原菌感染特点，构建列线图预测模型。方法选择 2019年 1月至 2021年 12月在南充市第二人民医院接受机械通气治疗的 193例慢性阻塞性肺疾病急性加重期( AECOPD)病人，根据有无 VAP将病人分为两组，在单因素分析基础上行多因素 logistic回归分析，构建列线图预测模型，以 ROC曲线分析模型预测价值，并以计算机模拟充分采样( bootstrap)法进行内部验证。结果该研究的 193例中共 91例( 47.15%)病人出现 VAP，91例病人中共分离出病原菌 108株，其中革兰阴性菌占 72.22%，革兰阳性菌占 15.74%，真菌占 12.04%，单一感染 62例，混合感染 29例。单因素分析基础上行多因素分析结果显示:年龄 ≥60岁、气道干预方式为气道切开、合并糖尿病、机械通气时间 ≥ 4d、使用抗菌药物联合用药、使用抑酸剂、有吸烟史及 APACHEⅡ评分≥15分为 AECOPD病人 VAP发生的危险因素(P<0.05)。根据上述因素以 R语言建立列线图预测模型，受试者操作特征(ROC)曲线下面积 0.84，95%CI为(0.78，0.90)Bootstrap法对列线图进行内部验证，平均绝对误差为 0.02，预测曲线与标准曲线基本拟合。结论 AECOPD病人 VAP发生率较高，主，要因感染革兰阴性菌所致， VAP的发生率受病人年龄、气道干预方式、合并糖尿病情况、机械通气时间、糖皮质激素使用情况、抗菌药物联合用药、抑酸剂使用情况、吸烟史及 APACHEⅡ评分的影响，以上述因素构建的列线图模型具有较高的区分度与准确度。 |
| 英文摘要: |
| Objective To analyze the characteristics of pathogen infection of ventilator-associated pneumonia (VAP) in patients withchronic obstructive pulmonary disease (COPD) and to construct a nomogram prediction model.Methods A total of 193 AECOPD pa-tients who received mechanical ventilation treatment in the Second People's Hospital of Nanchong City from January 2019 to December2021 were selected, and the patients were divided into two groups according to the presence or absence of VAP. On the basis of univari-ate analysis, multivariate logistic regression analysis was performed to construct the nomogram prediction model, and the predictive val-ue of the model was analyzed by ROC curves and internally validated by computer simulation of the full sampling (bootstrap) method.Results A total of 91 patients (47.15%) of the 193 cases in this study developed VAP, and 108 pathogenic bacteria were isolated fromthe 91 patients, of which 72.22% were gram-negative, 15.74% were gram-positive, and 12.04% were fungal, with 62 cases of single in-fections and 29 cases of mixed infections. Univariate analysis based on multifactorial analysis showed that age ≥ 60 years, airway inter-vention by airway incision, comorbid diabetes mellitus, duration of mechanical ventilation ≥ 4 d, use of antimicrobial drug combina-tions, use of acid suppressants, history of smoking and APACHE Ⅱ score ≥ 15 were risk factors for the development of VAP in patientswith AECOPD (P < 0.05). According to the above factors to establish the prediction model of the nomogram in R language, the area un-der the curve of the subject operating characteristic (ROC) curve was 0.84, and the 95%CI was (0.78, 0.90). The bootstrap method was used to internally validate the nomogram, and the results showed that the mean absolute error was 0.02, and the prediction curve wasbasically fitted to the standard curve. The incidence of VAP in AECOPD patients was high, mainly due to infection with gram-negativebacteria, and the incidence of VAP was affected by the patient's age, mode of airway intervention, comorbid diabetes mellitus, durationof mechanical ventilation, use of glucocorticoids, antimicrobial drug combinations, use of acid suppressive agents, history of smoking,and APACHE Ⅱ scores. The nomogram model constructed with the above factors had a high degree of differentiation and accuracy.Conclusions The incidence of VAP in AECOPD patients is high, mainly due to infection with gram-negative bacteria, and the inci-dence of VAP is affected by patient age, airway intervention mode, comorbid diabetes mellitus, duration of mechanical ventilation, glu-cocorticosteroid use, antimicrobial drug combinations, acid suppressant use, history of smoking, and APACHE Ⅱ scores. The columnarplot model constructed with the above factors has a high level of differentiation and accuracy. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
