实用老年医学 ›› 2025, Vol. 39 ›› Issue (11): 1135-1139.doi: 10.3969/j.issn.1003-9198.2025.11.013

• 临床研究 • 上一篇    下一篇

老年病人耐碳青霉烯铜绿假单胞菌感染风险预测模型的构建与验证

吴玲, 周海辉   

  1. 210009 江苏省南京市,徐州医科大学南京鼓楼临床学院
  • 收稿日期:2025-03-31 发布日期:2025-11-26
  • 通讯作者: 周海辉,Email: zhhhnj@126.com

Development and validation of a risk prediction model for carbapenem-resistant pseudomonas aeruginosa infection in elderly patients

WU Ling, ZHOU Haihui   

  1. Nanjing Drum Tower Hospital Clinical College of Xuzhou Medical University, Nanjing 210009, China
  • Received:2025-03-31 Published:2025-11-26
  • Contact: ZHOU Haihui, Email: zhhhnj@126.com

摘要: 目的 分析老年病人耐碳青霉烯铜绿假单胞菌(CRPA)感染的危险因素并构建列线图预测模型,以提高对CRPA感染的早期识别和干预能力。方法 回顾性收集2022—2023年南京医科大学附属老年医院收治的558例铜绿假单胞菌感染的老年(≥75岁)病人临床资料,按照7∶3比例随机分成训练组391例和验证组167例,通过logistic 回归确定独立危险因素,应用R软件构建列线图预测模型,应用ROC曲线、校准曲线及Hosmer-Lemeshow 检验评价模型的预测性能。结果 训练组logistic 回归分析显示,脓毒症休克(OR=7.054,95%CI:1.708~29.133)、气管切开(OR=2.931,95%CI:1.247~6.889)、3个月内住院史(OR=22.575,95%CI:11.524~44.222)和碳青霉烯类用药史(OR=3.203,95%CI:1.420~7.226)为老年病人感染CRPA的危险因素。根据logistic回归结果构建列线图预测模型,训练组和验证组的ROC 曲线下面积分别为0.816(95%CI: 0.778~0.85)和 0.798(95%CI: 0.736~0.860),校准曲线与理想曲线拟合较好,Hosmer-Lemeshow 检验提示模型有较好的预测效能。结论 本研究所建立的列线图模型判别能力较好,可以为预测老年病人CRPA的感染风险提供参考,从而预警和控制医院CRPA感染的发生和传播。

关键词: 老年人, 耐碳青霉烯铜绿假单胞菌, 危险因素, 预测模型, 列线图

Abstract: Objective To identify the risk factors for carbapenem-resistant pseudomonas aeruginosa (CRPA) infection in the elderly patients and to develop a nomogram prediction model for early recognition and intervention. Methods A retrospective study was conducted on 558 elderly patients (≥75 years) with pseudomonas aeruginosa (PA) infection admitted to Geriatric Hospital of Nanjing Medical University from January 2022 to December 2023. The patients were randomly divided into a training cohort (n=391) and a validation cohort (n=167). Multivariate logistic regression analysis was performed to identify the independent risk factors for CRPA infection. A nomogram prediction model was constructed using R software. The model’s performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and the Hosmer-Lemeshow test. Results Multivariate logistic regression analysis in the training cohort revealed four independent risk factors for CRPA infection in the elderly patients, including septic shock (OR=7.054, 95%CI:1.708-29.133), tracheostomy (OR=2.931, 95%CI:1.247-6.889), hospitalization history within 3 months (OR=22.575, 95%CI:11.524-44.222), and carbapenem use (OR=3.203, 95%CI:1.420-7.226). The model demonstrated a good fit of observed data, with an area under the ROC curve (AUC) of 0.816 (95%CI: 0.778-0.850) and 0.798 (95%CI: 0.736-0.860) for the experimental and validation cohort, respectively. The calibration curves and Hosmer-Lemeshow test results further confirmed the model had a good predictive performance. Conclusions Our prediction model exhibits a good predictive value in identifying elderly patients at risk of CRPA infection and can be used to guide preventive and treatment measures.

Key words: aged, carbapenem-resistant pseudomonas aeruginosa, risk factors, predictive model, nomogram

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