背景:随着消化内科(GEDs)中危重患者数量的增加,与耐碳青霉烯类革兰氏阴性菌(CR-GNB)相关的感染在GED中备受关注.然而,尚未建立GED患者的CR-GNB血流感染(BSI)风险预测模型。几乎普遍地,CR-GNB定殖先于CR-GNBSI或与之同时发生。这项研究的目的是开发一个列线图,可以预测作为CR-GNB携带者的GED患者获得继发性CR-GNBBSI的风险。
方法:我们于2020年1月至2022年3月进行了单中心回顾性病例对照研究。采用单因素和多因素logistic回归分析确定消化内科CR-GNB携带者继发CR-GNB血流感染的独立危险因素。根据多变量回归模型构建列线图。评估了已建立的预测列线图的各个方面,包括歧视,校准,和临床效用。我们使用自举评估内部验证。
结果:预测列线图包括以下预测因子:高ECOGPS,重症急性胰腺炎,糖尿病,中性粒细胞减少症,在医院呆了很长时间,和肠外营养。该模型表现出良好的辨别力和良好的校准。
结论:使用本研究中开发的列线图对个体风险进行估计,临床医生和护士可以及早发现继发性CR-GNBBSI高危患者.
BACKGROUND: With the number of critically ill patients increasing in gastroenterology departments (GEDs), infections associated with Carbapenem-resistant Gram-negative bacteria (CR-GNB) are of great concern in GED. However, no CR-GNB bloodstream infection (BSI) risk prediction model has been established for GED patients. Almost universally, CR-GNB colonization precedes or occurs concurrently with CR-GNB BSI. The objective of this study was to develop a nomogram that could predict the risk of acquiring secondary CR-GNB BSI in GED patients who are carriers of CR-GNB.
METHODS: We conducted a single-center retrospective
case-control study from January 2020 to March 2022. Univariate and multivariable logistic regression analysis was used to identify independent risk factors of secondary CR-GNB bloodstream infections among CR-GNB carriers in the gastroenterology department. A nomogram was constructed according to a multivariable regression model. Various aspects of the established predicting nomogram were evaluated, including discrimination, calibration, and clinical utility. We assessed internal validation using bootstrapping.
RESULTS: The prediction nomogram includes the following predictors: high ECOG PS, severe acute pancreatitis, diabetes mellitus, neutropenia, a long stay in hospital, and parenteral nutrition. The model demonstrated good discrimination and good calibration.
CONCLUSIONS: With an estimate of individual risk using the nomogram developed in this study, clinicians and nurses can identify patients with a high risk of secondary CR-GNB BSI early.