老年重症患者导尿管相关尿路感染风险预测模型的构建

Establishment of a risk prediction model for catheter-associated urinary tract infection in elderly critically ill patients

  • 摘要:
    目的 分析老年重症患者导尿管相关尿路感染(CAUTI)的危险因素,并构建相关风险预测模型。
    方法 回顾性收集2014年1月—2024年6月江苏省人民医院老年ICU病房行导尿管置入术的8 905例患者的临床资料,根据CAUTI发生情况分为感染组(n=114)和非感染组(n=8 791)。采用多因素Logistic回归模型分析发生CAUTI的危险因素,并构建列线图预测模型。采用受试者工作特征(ROC)曲线、校准曲线、Hosmer-Lemeshow拟合优度检验进行内部验证,评估预测模型的区分度和校准度。
    结果 本研究中,老年重症患者CAUTI发生率为2.55‰。多因素Logistic回归分析表明,年龄、意识状态、肾功能障碍、导尿管留置时间、导尿管插入次数是老年重症患者CAUTI发生的独立影响因素。按回归分析结果绘制列线图模型, Bootstrap法内部验证ROC曲线的曲线下面积(AUC)为0.802(95%CI: 0.796~0.809); 校正曲线接近于标准曲线,预测值与实际值基本一致,证明模型具有良好的预测性能。
    结论 年龄、肾功能障碍、意识状态、导尿管留置时间、导尿管插管次数是老年重症患者CAUTI发生的独立影响因素,据此建立的列线图预测模型简便易行,具有可靠的预测效能。

     

    Abstract:
    Objective To analyze the risk factors for catheter-associated urinary tract infection (CAUTI) in elderly critically ill patients and construct a related risk prediction model.
    Methods Clinical materials of 8 905 patients with catheterization in the geriatric ICU ward of Jiangsu Provincial People's Hospital from January 2014 to June 2024 were retrospectively collected. The patients were divided into infection group (n=114) and non-infection group (n=8 791) based on the occurrence of CAUTI. Multivariate Logistic regression analysis was used to identify the risk factors for CAUTI, and a Nomogram prediction model was constructed. Internal validation was performed by the receiver operating characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow goodness-of-fit test to assess the discrimination and calibration of the prediction model.
    Results In this study, the incidence of CAUTI in elderly critically ill patients was 2.55‰. Multivariate Logistic regression analysis showed that age, consciousness status, renal dysfunction, duration of catheterization, and the number of catheter insertions were independent influencing factors for CAUTI in elderly critically ill patients. A Nomogram model was constructed based on the results of the regression analysis. Area under the curve (AUC) of ROC curve for internal validation by the Bootstrap method was 0.802 (95%CI, 0.796 to 0.809). The calibration curve was close to the standard curve, and the predicted values were generally consistent with the actual values, demonstrating good predictive performance of the model.
    Conclusion Age, renal dysfunction, consciousness status, duration of catheterization, and the number of catheter insertions are independent influencing factors for CAUTI in elderly critically ill patients. Nomogram prediction model established based on these factors is simple and feasible, with reliable predictive performance.

     

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