Development of a Risk Prediction Model for Carbapenem-resistant Enterobacteriaceae Infection After Liver Transplantation: A Multinational Cohort Study.
CRE carriage
CRE infection
SOT
liver transplantation
Journal
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213
Informations de publication
Date de publication:
16 08 2021
16 08 2021
Historique:
received:
06
11
2020
pubmed:
11
2
2021
medline:
23
9
2021
entrez:
10
2
2021
Statut:
ppublish
Résumé
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. 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 subdistribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. A 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/. Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.
Sections du résumé
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 subdistribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created.
RESULTS
A 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/.
CONCLUSIONS
Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.
Identifiants
pubmed: 33564840
pii: 6132093
doi: 10.1093/cid/ciab109
doi:
Substances chimiques
Anti-Bacterial Agents
0
Carbapenems
0
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e955-e966Investigateurs
Michele Bartoletti
(M)
Renato Pascale
(R)
Caterina Campoli
(C)
Simona Coladonato
(S)
Francesco Cristini
(F)
Fabio Tumietto
(F)
Antonio Siniscalchi
(A)
Cristiana Laici
(C)
Simone Ambretti
(S)
Renato Romagnoli
(R)
Francesco Giuseppe De Rosa
(FG)
Antonio Muscatello
(A)
Davide Mangioni
(D)
Andrea Gori
(A)
Barbara Antonelli
(B)
Daniele Dondossola
(D)
Giorgio Rossi
(G)
Federica Invernizzi
(F)
Maddalena Peghin
(M)
Umberto Cillo
(U)
Cristina Mussini
(C)
Fabrizio Di Benedetto
(FD)
Débora Raquel Benedita Terrabuio
(DRB)
Carolina D Bittante
(CD)
Alexandra do Rosário Toniolo
(ADR)
Elizabeth Balbi
(E)
José Huygens Parente Garcia
(JHP)
Ignacio Morrás
(I)
Antonio Ramos
(A)
Ana Fernandez Cruz
(AF)
Magdalena Salcedo
(M)
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.