Freire Maristela Pinheiro, Rinaldi Matteo, Terrabuio Debora Raquel Benedita, Furtado Mariane, Pasquini Zeno, Bartoletti Michele, de Oliveira Tiago Almeida, Nunes Nathalia Neves, Lemos Gabriela Takeshigue, Maccaro Angelo, Siniscalchi Antonio, Laici Cristiana, Cescon Matteo, D Albuquerque Luiz Augusto Carneiro, Morelli Maria Cristina, Song Alice T W, Abdala Edson, Viale Pierluigi, Filho Alexandre Dias Porto Chiavegatto, Giannella Maddalena
Hospital Epidemiology and Infection Control, University of São Paulo School of Medicine Hospital das Clínicas, São Paulo, São Paulo, Brazil.
Infectious Diseases Unit, Department of Medical and Surgical Sciences, Policlinico Sant'Orsola Malpighi, University of Bologna, Bologna, Italy.
Transpl Infect Dis. 2022 Dec;24(6):e13920. doi: 10.1111/tid.13920. Epub 2022 Aug 9.
Carbapenem-resistant Enterobacterales (CRE) colonisation at liver transplantation (LT) increases the risk of CRE infection after LT, which impacts on recipients' survival. Colonization status usually becomes evident only near LT. Thus, predictive models can be useful to guide antibiotic prophylaxis in endemic centres.
This study aimed to identify risk factors for CRE colonisation at LT in order to build a predictive model.
Retrospective multicentre study including consecutive adult patients who underwent LT, from 2010 to 2019, at two large teaching hospitals. We excluded patients who had CRE infections within 90 days before LT. CRE screening was performed in all patients on the day of LT. Exposure variables were considered within 90 days before LT and included cirrhosis complications, underlying disease, time on the waiting list, MELD and CLIF-SOFA scores, antibiotic use, intensive care unit and hospital stay, and infections. A machine learning model was trained to detect the probability of a patient being colonized with CRE at LT.
A total of 1544 patients were analyzed, 116 (7.5%) patients were colonized by CRE at LT. The median time from CRE isolation to LT was 5 days. Use of antibiotics, hepato-renal syndrome, worst CLIF sofa score, and use of beta-lactam/beta-lactamase inhibitor increased the probability of a patient having pre-LT CRE. The proposed algorithm had a sensitivity of 66% and a specificity of 83% with a negative predictive value of 97%.
We created a model able to predict CRE colonization at LT based on easy-to-obtain features that could guide antibiotic prophylaxis.
肝移植（LT）时耐碳青霉烯类肠杆菌科细菌（CRE）定植会增加LT后CRE感染的风险，这会影响受者的生存。定植状态通常仅在LT临近时才变得明显。因此，预测模型有助于指导流行地区中心的抗生素预防。
本研究旨在确定LT时CRE定植的危险因素，以建立预测模型。
回顾性多中心研究，纳入2010年至2019年在两家大型教学医院接受LT的连续成年患者。我们排除了LT前90天内发生CRE感染的患者。所有患者在LT当天进行CRE筛查。暴露变量考虑LT前90天内的情况，包括肝硬化并发症、基础疾病、等待名单上的时间、终末期肝病模型（MELD）和终末期肝病功能障碍评分（CLIF-SOFA）、抗生素使用、重症监护病房和住院时间以及感染情况。训练一个机器学习模型来检测患者在LT时被CRE定植的概率。
共分析了1544例患者，116例（7.5%）患者在LT时被CRE定植。从CRE分离到LT的中位时间为5天。使用抗生素、肝肾综合征、最差CLIF沙发评分以及使用β-内酰胺/β-内酰胺酶抑制剂会增加患者LT前CRE定植的概率。所提出的算法敏感性为66%，特异性为83%，阴性预测值为97%。
我们创建了一个能够基于易于获得的特征预测LT时CRE定植的模型，该模型可指导抗生素预防。
