© 2017 The American Society for Blood and Marrow Transplantation There is a paucity of data evaluating acute kidney injury (AKI) incidence and its relationship with the tacrolimus–sirolimus (Tac-Sir) concentrations in the setting of reduced-intensity conditioning (RIC) after allogeneic stem cell transplantation (allo-HSCT). This multicenter retrospective study evaluated risk factors of AKI defined by 2 classification systems, Kidney Disease Improving Global Outcome (KDIGO) score and “Grade 0-3 staging,” in 186 consecutive RIC allo-HSCT recipients with Tac-Sir as graft-versus-host disease prophylaxis. Conditioning regimens consisted of fludarabine and busulfan (n = 53); melphalan (n = 83); or a combination of thiotepa, fludarabine, and busulfan (n = 50). A parametric model, with detailed Tac-Sir consecutive blood levels, describing time to AKI was developed using the NONMEM software version 7.4. Overall, 81 of 186 (44%) RIC allo-HSCT recipients developed AKI with a cumulative incidence of 42% at a median follow-up of 25 months. Time to AKI was best described using a piecewise function. AKI-predicting factors were melphalan-based conditioning regimen (HR, 1.96; P <.01), unrelated donor (HR, 1.79; P =.04), and tacrolimus concentration: The risk of AKI increased 2.3% per each 1-ng/mL increase in tacrolimus whole blood concentration (P <.01). In multivariate analysis, AKI grades 2 and 3 according to KDIGO staging were independent risk factors for 2-year nonrelapse mortality (HR, 2.8; P =.05; and HR, 6.6; P <.0001, respectively). According to the KDIGO score, overall survival decreased with the increase in severity of AKI: 78% for patients without AKI versus 68%, 50%, and 30% for grades 1, 2, and 3, respectively (P <.0001). In conclusion, AKI is frequent after Tac-Sir–based RIC allo-HSCT and has a negative impact on outcome. This study presents the first predictive model describing time to AKI as a function of tacrolimus drug concentration.
- Acute kidney injury
- Allogeneic stem cell transplantation
- Parametric modeling of time-to-event data
- Reduced intensity conditioning
- Time-to-event analysis