Development of a Risk Prediction Model for Carbapenem-resistantEnterobacteriaceaeInfection After Liver Transplantation: A Multinational Cohort Study
Authors: Giannella Maddalena , Freire Maristela , Rinaldi Matteo , Abdala Edson , Rubin Arianna , Mularoni Alessandra , Gruttadauria Salvatore , Grossi Paolo , Shbaklo Nour , Tandoi Francesco , Ferrarese Alberto , Burra Patrizia , Fernandes Ruan , Aranha Camargo Luis Fernando , Asensio Angel , Alagna Laura , Bandera Alessandra , Simkins Jacques , Abbo Lilian , Halpern Marcia , Santana Girao Evelyne , Valerio Maricela , Muñoz Patricia , Fernandez Yunquera Ainhoa , Statlender Liran , Yahav Dafna , Franceschini Erica , Graziano Elena , Morelli Maria Cristina , Cescon Matteo , Viale Pierluigi , Lewis Russell ,   CRECOOLT study group, Bartoletti Michele , Pascale Renato , Campoli Caterina , Coladonato Simona , Cristini Francesco , Tumietto Fabio , Siniscalchi Antonio , Laici Cristiana , Ambretti Simone , Romagnoli Renato , De Rosa Francesco Giuseppe , Muscatello Antonio , Mangioni Davide , Gori Andrea , Antonelli Barbara , Dondossola Daniele , Rossi Giorgio , Invernizzi Federica , Peghin Maddalena , Cillo Umberto , Mussini Cristina , Benedetto Fabrizio Di , Terrabuio Débora Raquel Benedita , Bittante Carolina D , Toniolo Alexandra do Rosário , Balbi Elizabeth , Garcia José Huygens Parente , Morrás Ignacio , Ramos Antonio , Cruz Ana Fernandez , Salcedo Magdalena 
Published: February 2021

AbstractBackgroundPatients colonized with carbapenem-resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT), with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies.MethodsMultinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray subdistribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created.ResultsA total of 840 LT recipients found to be colonized with CRE before (n = 203) or after (n = 637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (interquartile range [IQR], 9–42) days after LT. Pre- and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical reintervention were retained in the prediction model. Median 30- and 60-day predicted risk was 15% (IQR, 11–24) and 21% (IQR, 15–33), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (area under the curve [AUC], 74.6; Brier index, 16.3) and bootstrapped validation dataset (AUC, 73.9; Brier index, 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/.ConclusionsOur clinical prediction tool could enable better targeting interventions for CRE infection after transplant.