Pietro E Cippà1, Marc Schiesser2, Henrik Ekberg3, Teun van Gelder4, Nicolas J Mueller5, Claude A Cao6, Thomas Fehr7, Corrado Bernasconi8. 1. Divisions of Nephrology. 2. Visceral and Transplantation Surgery, and. 3. Department of Nephrology and Transplantation, Skåne University Hospital, Malmö, Sweden; 4. Departments of Hospital Pharmacy and Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands; 5. Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland; 6. Praxis Riesbach, Zurich, Switzerland; 7. Divisions of Nephrology, Department of Internal Medicine, Cantonal Hospital Graubünden, Graubünden, Switzerland; and thomas.fehr@uzh.ch. 8. Limites Medical Research, Vacallo, Switzerland.
Abstract
BACKGROUND AND OBJECTIVES: Definition of individual risk profile is the first step to implement strategies to keep the delicate balance between under- and overimmunosuppression after kidney transplantation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used data from the Efficacy Limiting Toxicity Elimination Symphony Study (1190 patients between 2002 and 2004) to model risk of rejection and infection in the first year after kidney transplantation. External validation was performed in a study population from the Fixed-Dose Concentration-Controlled Trial (630 patients between 2003 and 2006). RESULTS: Despite different temporal dynamics, rejections and severe infections had similar overall incidences in the first year after transplantation (23.4% and 25.5%, respectively), and infections were the principal cause of death (43.2% of all deaths). Recipient older age, deceased donor, higher number of HLA mismatches, and high risk for cytomegalovirus disease were associated with infection; deceased donor, higher number of HLA mismatches, and immunosuppressive therapy including cyclosporin A (compared with tacrolimus), with rejection. These factors were integrated into a two-dimensional risk stratification model, which defined four risk groups: low risk for infection and rejection (30.8%), isolated risk for rejection (36.1%), isolated risk for infection (7.0%), and high risk for infection and rejection (26.1%). In internal validation, this model significantly discriminated the subgroups in terms of composite end point (low risk for infection/rejection, 24.4%; isolated risk for rejection and isolated risk for infection, 31.3%; high risk for infection/rejection, 54.4%; P<0.001), rejection episodes (isolated risk for infection and low risk for infection/rejection, 13.0%; isolated risk for rejection and high risk for infection/rejection, 24.2%; P=0.001), and infection episodes (low risk for infection/rejection and isolated risk for rejection, 12.0%; isolated risk for infection and high risk for infection/rejection, 37.6%; P<0.001). External validation confirmed the applicability of the model to an independent cohort. CONCLUSIONS: We propose a two-dimensional risk stratification model able to disentangle the individual risk for rejection and infection in the first year after kidney transplantation. This concept can be applied to implement a personalized immunosuppressive and antimicrobial treatment approach.
BACKGROUND AND OBJECTIVES: Definition of individual risk profile is the first step to implement strategies to keep the delicate balance between under- and overimmunosuppression after kidney transplantation. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We used data from the Efficacy Limiting Toxicity Elimination Symphony Study (1190 patients between 2002 and 2004) to model risk of rejection and infection in the first year after kidney transplantation. External validation was performed in a study population from the Fixed-Dose Concentration-Controlled Trial (630 patients between 2003 and 2006). RESULTS: Despite different temporal dynamics, rejections and severe infections had similar overall incidences in the first year after transplantation (23.4% and 25.5%, respectively), and infections were the principal cause of death (43.2% of all deaths). Recipient older age, deceased donor, higher number of HLA mismatches, and high risk for cytomegalovirus disease were associated with infection; deceased donor, higher number of HLA mismatches, and immunosuppressive therapy including cyclosporin A (compared with tacrolimus), with rejection. These factors were integrated into a two-dimensional risk stratification model, which defined four risk groups: low risk for infection and rejection (30.8%), isolated risk for rejection (36.1%), isolated risk for infection (7.0%), and high risk for infection and rejection (26.1%). In internal validation, this model significantly discriminated the subgroups in terms of composite end point (low risk for infection/rejection, 24.4%; isolated risk for rejection and isolated risk for infection, 31.3%; high risk for infection/rejection, 54.4%; P<0.001), rejection episodes (isolated risk for infection and low risk for infection/rejection, 13.0%; isolated risk for rejection and high risk for infection/rejection, 24.2%; P=0.001), and infection episodes (low risk for infection/rejection and isolated risk for rejection, 12.0%; isolated risk for infection and high risk for infection/rejection, 37.6%; P<0.001). External validation confirmed the applicability of the model to an independent cohort. CONCLUSIONS: We propose a two-dimensional risk stratification model able to disentangle the individual risk for rejection and infection in the first year after kidney transplantation. This concept can be applied to implement a personalized immunosuppressive and antimicrobial treatment approach.
Authors: Manuel Pascual; Tom Theruvath; Tatsuo Kawai; Nina Tolkoff-Rubin; A Benedict Cosimi Journal: N Engl J Med Date: 2002-02-21 Impact factor: 91.245
Authors: Michael T Koller; Christian van Delden; Nicolas J Müller; Philippe Baumann; Christian Lovis; Hans-Peter Marti; Thomas Fehr; Isabelle Binet; Sabina De Geest; Heiner C Bucher; Pascal Meylan; Manuel Pascual; Jürg Steiger Journal: Eur J Epidemiol Date: 2013-04-02 Impact factor: 8.082
Authors: Hélio Tedesco Silva; Harold C Yang; Herwig-Ulf Meier-Kriesche; Richard Croy; John Holman; William E Fitzsimmons; M Roy First Journal: Transplantation Date: 2014-03-27 Impact factor: 4.939
Authors: N Le Berre; M Ladrière; A Corbel; T Remen; L Durin; L Frimat; N Thilly; C Pulcini Journal: Eur J Clin Microbiol Infect Dis Date: 2020-01-04 Impact factor: 3.267
Authors: Todd A Miano; Judd D Flesch; Rui Feng; Caitlin M Forker; Melanie Brown; Michelle Oyster; Laurel Kalman; Melanie Rushefski; Edward Cantu; Mary Porteus; Wei Yang; A Russel Localio; Joshua M Diamond; Jason D Christie; Michael G S Shashaty Journal: Clin Pharmacol Ther Date: 2019-10-20 Impact factor: 6.875
Authors: Marion Hemmersbach-Miller; Barbara D Alexander; Debra L Sudan; Carl Pieper; Kenneth E Schmader Journal: Eur J Clin Microbiol Infect Dis Date: 2018-10-23 Impact factor: 3.267
Authors: Paula Rebello Bicalho; Lúcio R Requião-Moura; Érika Ferraz Arruda; Rogerio Chinen; Luciana Mello; Ana Paula F Bertocchi; Erika Lamkowski Naka; Eduardo José Tonato; Alvaro Pacheco-Silva Journal: Biomed Res Int Date: 2019-04-02 Impact factor: 3.411
Authors: Dulce M Barrios; Mytrang H Do; Gregory S Phillips; Michael A Postow; Tomoko Akaike; Paul Nghiem; Mario E Lacouture Journal: J Am Acad Dermatol Date: 2020-05-24 Impact factor: 11.527