| 罗欣悦,邓 俊,杨梓苑,衡 鑫,张佩芸,王宋平.重症监护室机械通气患者呼吸机相关性肺炎病原菌分布及风险预测模型构建[J].,2023,(13):2518-2522 |
| 重症监护室机械通气患者呼吸机相关性肺炎病原菌分布及风险预测模型构建 |
| Pathogenic Distribution and Risk Prediction Model Building of Ventilator Associated Pneumonia in Patients with Mechanical Ventilation in Intensive Care Unit |
| 投稿时间：2023-02-03 修订日期：2023-02-28 |
| DOI：10.13241/j.cnki.pmb.2023.13.023 |
| 中文关键词: 机械通气 呼吸机相关性肺炎 危险因素 病原菌 模型构建 |
| 英文关键词: Mechanical ventilation Ventilator associated pneumonia Risk factors Pathogens Model building |
| 基金项目:四川省卫计委基金资助项目（18PJ402）；泸州市人民政府－西南医科大学科技战略合作项目（2019LZXNYDJ09） |
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| 中文摘要: |
| 摘要 目的：研究重症监护室（ICU）机械通气患者呼吸机相关性肺炎（VAP）的病原菌分布，分析其危险因素并构建风险预测模型，为VAP的预防提供理论依据。方法：选取2018年12月至2021年6月入住本院ICU的137例机械通气患者的临床资料进行回顾性分析，根据是否发生VAP分为VAP组30例和非VAP组107例。分析VAP的病原菌分布，对VAP的危险因素进行多因素Logistic回归分析，并构建风险预测模型。结果：30例VAP患者共培养出病原菌37株，革兰阳性菌8株（21.62%），革兰阴性菌28株（75.68%），真菌1株（2.70%）。VAP的发生与慢性呼吸系统疾病、糖尿病、意识障碍、气管切开、抑酸药物使用、糖皮质激素使用、贫血、低蛋白血症、住院天数、机械通气时间、年龄等因素有关（P＜0.05）。多因素Logistic回归分析结果可知：住院天数＞14 d、意识障碍、糖皮质激素使用、年龄是VAP发生的独立危险因素（P＜0.05）。VAP风险预测模型构建：PI=1.208×住院天数＞14 d+1.051×意识障碍+1.012×糖皮质激素使用+0.044×年龄-5.907。构建的风险预测模型预测ICU机械通气患者发生VAP的受试者工作特征（ROC）曲线下面积（AUC）为0.806。结论：VAP病原菌以革兰阴性菌为主，住院天数、意识障碍、糖皮质激素使用、年龄是VAP发生的独立危险因素，临床通过构建风险预测模型，有助于降低VAP的发生率。 |
| 英文摘要: |
| ABSTRACT Objective: To study the pathogenic distribution of ventilator associated pneumonia (VAP) in patients with mechanical ventilation in intensive care unit (ICU), to analyze its risk factors and build a risk prediction model, so as to provide a theoretical basis for the prevention of VAP. Methods: The clinical data of 137 patients with mechanical ventilation who were admitted to our ICU from December 2018 to June 2021 were retrospectively analyzed. They were divided into 30 cases in the VAP group and 107 cases in the non-VAP group according to whether the occurrence of VAP. The pathogenic distribution of VAP was analyzed, the risk factors of VAP were analyzed by multivariate Logistic regression, and the risk prediction model was built. Results: Among the 30 patients with VAP, 37 strains of pathogenic bacteria were cultured, included 8 strains of Gram-positive bacteria (21.62%), 28 strains of Gram-negative bacteria (75.68%), and 1 strain of fungal strain (2.70%). The occurrence of VAP was associated with chronic respiratory diseases, diabetes mellitus, disturbance of consciousness, tracheotomy, acid-inhibiting drug use, glucocorticoid use, anemia, hypoproteinemia, hospitalization days, mechanical ventilation time, age and other factors(P＜0.05). Multivariate Logistic regression analysis showed that hospitalization days>14 d, disturbance of consciousness, glucocorticoid use and age were independent risk factors for the occurrence of VAP(P＜0.05). VAP risk prediction model building: PI=1.208× hospital stay>14 d+1.051×disturbance of consciousness +1.012×glucocorticoid use +0.044×age -5.907. In the risk prediction model, the ROC area under curve(AUC) of subjects with VAP in patients with mechanical ventilation in ICU was 0.806. Conclusion: The main pathogens of VAP are Gram-negative bacteria, and the hospitalization days, disturbance of consciousness, glucocorticoid use and age are independent risk factors for the occurrence of VAP. The clinical building risk prediction model is helpful to reduce the incidence of VAP. |
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