Prediction models for carbapenem‐resistant Enterobacterales carriage at liver transplantation: A multicenter retrospective study

• DOI: 10.1111/tid.13920
• Publication Date: 2022-08-09T09:50:11Z
• Authors: Maristela Pinheir...; Matteo Rinaldi; Debora Raquel Ben...; Mariane Furtado; Zeno Pasquini; Michele Bartoletti; Tiago Almeida de ...; Nathalia Neves Nunes; Gabriela Takeshig...; Angelo Maccaro; Antonio Siniscalchi; Cristiana Laici; Matteo Cescon; Luiz Augusto Carn...; Maria Cristina Mo...; Alice T. W. Song; Edson Abdala; Pierluigi Viale; Alexandre Dias Po...; Maddalena Giannella
• Keywords: Carbapenem-resistant enterobacteriaceae; Carriage

Abstract Background 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. Aims This study aimed identify factors for LT order build a model. Methods Retrospective multicentre including consecutive adult patients who underwent from 2010 2019, two large teaching hospitals. We excluded had infections within 90 days before screening was performed all day Exposure variables were considered and included cirrhosis complications, underlying disease, time waiting list, MELD CLIF‐SOFA scores, use, intensive care unit hospital stay, infections. A machine learning model trained detect probability patient being colonized with Results total 1544 analyzed, 116 (7.5%) by The median isolation 5 days. Use antibiotics, hepato‐renal syndrome, worst CLIF sofa score, use beta‐lactam/beta‐lactamase inhibitor increased having pre‐LT CRE. proposed algorithm sensitivity 66% specificity 83% negative value 97%. Conclusions created able predict colonization based easy‐to‐obtain features that could image