ArticleTransplantation direct2026
Growth Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes.
Article in Transplantation direct, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
5 authors.
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Abstract
Background: Nonadherence to immunosuppressive medication remains one of the most critical predictors of late acute rejection, hospitalization, and graft loss in pediatric organ transplant recipients. Despite its clinical importance, longitudinal patterns of adherence behavior in this population remain underexplored, particularly using objective pharmacokinetic indicators. This study aimed to examine adherence trajectories over time using the medication level variability index (MLVI), a validated biomarker of tacrolimus variability, and to assess their association with clinical and demographic factors. Methods: We applied growth mixture modeling to longitudinal MLVI data from a large single-center sample of pediatric heart, liver, and kidney transplant recipients (N = 181). MLVI values were calculated from serial tacrolimus trough levels over a multiyear posttransplant period. Results: Two adherence trajectories emerged with complete separation in MLVI (trajectory 1: range, 1.07-2.10; trajectory 2: range, 2.67-3.99). Across all years of follow-up, posttransplant hospitalization because of late acute rejection occurred in 33.4% of adherent versus 35.1% of nonadherent patients, and on a per-year basis 5.3% versus 7.7% of person-years, respectively (descriptively higher in the nonadherent trajectory). Public insurance status and patient functional status differed significantly between the 2 trajectories, suggesting social and structural determinants of adherence behavior. Conclusions: These findings underscore the dynamic and heterogeneous nature of adherence across pediatric transplant populations. MLVI-based trajectory modeling offers a promising avenue for early risk identification and could inform the development of electronic medical record-integrated alerts or personalized adherence interventions. Future research should integrate psychosocial variables and expand to multicenter samples for enhanced generalizability.
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