Harry M Gallagher1, Ghulam Sarwar2, Tracy Tse2, Timothy M Sladden3, Esmond Hii3, Stephanie T Yerkovich4, Peter M Hopkins4, Daniel C Chambers5. 1. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Queensland, Australia; Respiratory Medicine, Waikato Hospital, Hamilton, New Zealand. 2. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Queensland, Australia. 3. School of Medicine, The University of Queensland, Brisbane, Queensland, Australia. 4. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Queensland, Australia; School of Medicine, The University of Queensland, Brisbane, Queensland, Australia. 5. Queensland Lung Transplant Service, The Prince Charles Hospital, Brisbane, Queensland, Australia; School of Medicine, The University of Queensland, Brisbane, Queensland, Australia. Electronic address: daniel.chambers@health.qld.gov.au.
Abstract
BACKGROUND: Erratic tacrolimus blood levels are associated with liver and kidney graft failure. We hypothesized that erratic tacrolimus exposure would similarly compromise lung transplant outcomes. This study assessed the effect of tacrolimus mean and standard deviation (SD) levels on the risk of chronic lung allograft dysfunction (CLAD) and death after lung transplantation. METHODS: We retrospectively reviewed 110 lung transplant recipients who received tacrolimus-based immunosuppression. Cox proportional hazard modeling was used to investigate the effect of tacrolimus mean and SD levels on survival and CLAD. At census, 48 patients (44%) had developed CLAD and 37 (34%) had died. RESULTS: Tacrolimus SD was highest for the first 6 post-transplant months (median, 4.01; interquartile range [IQR], 3.04-4.98 months) before stabilizing at 2.84 μg/liter (IQR, 2.16-4.13 μg/liter) between 6 and 12 months. The SD then remained the same (median, 2.85; IQR, 2.00-3.77 μg/liter) between 12 and 24 months. A high mean tacrolimus level 6 to 12 months post-transplant independently reduced the risk of CLAD (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.63-0.86; p < 0.001) but not death (HR, 0.96; 95% CI, 0.83-1.12; p = 0.65). In contrast, a high tacrolimus SD between 6 and 12 months independently increased the risk of CLAD (HR, 1.46; 95% CI, 1.23-1.73; p < 0.001) and death (HR, 1.27; 95% CI, 1.08-1.51; p = 0.005). CONCLUSIONS: Erratic tacrolimus levels are a risk factor for poor lung transplant outcomes. Identifying and modifying factors that contribute to this variability may significantly improve outcomes.
BACKGROUND: Erratic tacrolimus blood levels are associated with liver and kidney graft failure. We hypothesized that erratic tacrolimus exposure would similarly compromise lung transplant outcomes. This study assessed the effect of tacrolimus mean and standard deviation (SD) levels on the risk of chronic lung allograft dysfunction (CLAD) and death after lung transplantation. METHODS: We retrospectively reviewed 110 lung transplant recipients who received tacrolimus-based immunosuppression. Cox proportional hazard modeling was used to investigate the effect of tacrolimus mean and SD levels on survival and CLAD. At census, 48 patients (44%) had developed CLAD and 37 (34%) had died. RESULTS:Tacrolimus SD was highest for the first 6 post-transplant months (median, 4.01; interquartile range [IQR], 3.04-4.98 months) before stabilizing at 2.84 μg/liter (IQR, 2.16-4.13 μg/liter) between 6 and 12 months. The SD then remained the same (median, 2.85; IQR, 2.00-3.77 μg/liter) between 12 and 24 months. A high mean tacrolimus level 6 to 12 months post-transplant independently reduced the risk of CLAD (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.63-0.86; p < 0.001) but not death (HR, 0.96; 95% CI, 0.83-1.12; p = 0.65). In contrast, a high tacrolimus SD between 6 and 12 months independently increased the risk of CLAD (HR, 1.46; 95% CI, 1.23-1.73; p < 0.001) and death (HR, 1.27; 95% CI, 1.08-1.51; p = 0.005). CONCLUSIONS: Erratic tacrolimus levels are a risk factor for poor lung transplant outcomes. Identifying and modifying factors that contribute to this variability may significantly improve outcomes.
Authors: Nicholas A Kolaitis; Daniel R Calabrese; Patrick Ahearn; Aida Venado; Rebecca Florez; Huey-Ling Lei; Karolina Isaak; Erik Henricksen; Emily Martinez; Tiffany Chong; Rupal J Shah; Lorriana E Leard; Mary Ellen Kleinhenz; Jeffrey Golden; Teresa De Marco; John R Greenland; Jasleen Kukreja; Steven R Hays; Paul D Blanc; Jonathan P Singer Journal: Am J Health Syst Pharm Date: 2019-12-02 Impact factor: 2.637
Authors: David R Darley; Lilibeth Carlos; Stefanie Hennig; Zhixin Liu; Richard Day; Allan R Glanville Journal: Eur J Clin Pharmacol Date: 2019-03-12 Impact factor: 2.953
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: Michelle Liu; Ciara M Shaver; Kelly A Birdwell; Stephanie A Heeney; Christian M Shaffer; Sara L Van Driest Journal: Pharmacogenet Genomics Date: 2022-04-07 Impact factor: 2.000
Authors: Sarah Duncan-Park; Claire Dunphy; Jacqueline Becker; Christine D'Urso; Rachel Annunziato; Joshua Blatter; Carol Conrad; Samuel B Goldfarb; Don Hayes; Ernestina Melicoff; Marc Schecter; Gary Visner; Brian Armstrong; Hyunsook Chin; Karen Kesler; Nikki M Williams; Jonah N Odim; Stuart C Sweet; Lara Danziger-Isakov; Eyal Shemesh Journal: Am J Transplant Date: 2021-04-12 Impact factor: 8.086
Authors: Kasia A Sablik; Marian C Clahsen-van Groningen; Dennis A Hesselink; Teun van Gelder; Michiel G H Betjes Journal: PLoS One Date: 2018-05-10 Impact factor: 3.240
Authors: Maaike A Sikma; Claudine C Hunault; Alwin D R Huitema; Dylan W De Lange; Erik M Van Maarseveen Journal: Clin Pharmacokinet Date: 2020-04 Impact factor: 6.447