Maddalena Giannella1,2, Maristela Freire3, Matteo Rinaldi1,2, Edson Abdala4, Arianna Rubin1, Alessandra Mularoni5, Salvatore Gruttadauria6, Paolo Grossi7, Nour Shbaklo8, Francesco Tandoi9, Alberto Ferrarese10, Patrizia Burra10, Ruan Fernandes11, Luis Fernando Aranha Camargo11, Angel Asensio12, Laura Alagna13, Alessandra Bandera13, Jacques Simkins14, Lilian Abbo15, Marcia Halpern16, Evelyne Santana Girao17, Maricela Valerio18, Patricia Muñoz18, Ainhoa Fernandez Yunquera19, Liran Statlender20, Dafna Yahav21, Erica Franceschini22, Elena Graziano23, Maria Cristina Morelli24, Matteo Cescon2,25, Pierluigi Viale1,2, Russell Lewis1,2. 1. Infectious Diseases Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Policlinico di Sant'Orsola, Bologna, Italy. 2. Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy. 3. Working Committee for Hospital Epidemiology and Infection Control, Hospital das Clinicas, Universidade de São Paulo, Brazil. 4. Infectious diseases department, Hospital das Clinicas, Universidade de São Paulo, Brazil. 5. Infectious Diseases, ISMETT IRCCS, Palermo, Italy. 6. Department for the Treatment and Study of Abdominal Diseases and Abdominal Transplantation, IRCCS, ISMETT-UPMC, Palermo, Italy. 7. Infectious and Tropical Diseases Department, University of Insubria, Varese, Italy. 8. Infectious Disease, Department of Medical Sciences University of Turin AOU Città della salute e della Scienza, Turin, Italy. 9. Liver Transplant Center, General Surgery Unit, Department of Surgical Sciences, A.O.U. Città della Salute e della Scienza, Molinette Hospital, University of Turin, Turin, Italy. 10. Multivisceral Transplant Unit (Gastroenterology), Department of Surgery Oncology and Gastroenterology, Surgical and Gastroenterological Sciences, Padua University Hospital, Padua, Italy. 11. Infectious Diseases Unit, Hospital Israelita Albert Einstein, São Paulo, Brazil. 12. Preventive Medicine Department, Puerta de Hierro-Majadahonda University Hospital, Majadahonda, Madrid, Spain. 13. Infectious Diseases Unit, Department of Internal Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy. 14. Transplant Infectious Diseases and Immunocompromised Host Service, Division of Infectious Diseases, University of Miami/Miami Transplant Institute, Miami, Florida, USA. 15. Department of Medicine, Division of Infectious Diseases, University of Miami, Miami, Florida, USA. 16. Liver Transplant Unit, Quinta D'Or Hospital, Rio de Janeiro, Brazil. 17. Infectious Diseases Unit and Liver Transplant Unit of Hospital Universitário Walter Cantídio, Universidade Federal do Ceará, Fortaleza- Brazil. 18. Department of Clinical Microbiology and Infectious Diseases, Instituto de Investigación Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, Madrid, Spain. 19. Department of Gastroenterology, Instituto de Investigación Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, Madrid, Spain. 20. Intensive Care Unit, Rabin Medical Center, Petah Tikva, Israel. 21. Infectious Disease Unit, Beilinson Hospital, Petah Tikva, Israel. 22. Infectious Diseases Unit, Department of Nephrology Dialysis and Transplant Unit, University Hospital of Modena, Modena, Italy. 23. Infectious Disease Clinic, ASUFC, Udine, Italy. 24. Internal Medicine Unit for the Treatment of Severe Organ Failure, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Policlinico di Sant'Orsola, Bologna, Italy. 25. Liver and Multiorgan Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Policlinico di Sant'Orsola, Bologna, Italy.
Abstract
BACKGROUND: Patients 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. METHODS: Multinational 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 sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. RESULTS: 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 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention 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 (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/. CONCLUSIONS: Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.
BACKGROUND:Patients 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. METHODS: Multinational 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 sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. RESULTS: 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 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention 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 (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/. CONCLUSIONS: Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.
Authors: Nour Shbaklo; Francesco Tandoi; Tommaso Lupia; Silvia Corcione; Renato Romagnoli; Francesco Giuseppe De Rosa Journal: Biomedicines Date: 2022-06-30
Authors: Elena Pérez-Nadales; Mario Fernández-Ruiz; Belén Gutiérrez-Gutiérrez; Álvaro Pascual; Jesús Rodríguez-Baño; Luis Martínez-Martínez; José María Aguado; Julian Torre-Cisneros Journal: Transpl Infect Dis Date: 2022-06-28