Etiologies and Outcomes Associated With Tacrolimus Levels Out of a Typical Range That Lead to High Variability in Kidney Transplant Recipients

Improving prediction of tacrolimus concentration using a combination of population pharmacokinetic modeling and machine learning in chinese renal transplant recipients

Aims

The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.

Methods

Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.

Results

The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.

Conclusion

The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

Implementing a self-reported immunosuppression adherence questionnaire to screen for non-adherence in routine care of kidney transplant recipients

INTRODUCTION

Self-reported measures of immunosuppression adherence have been largely examined in research settings.

METHODS

In this single center study of 610 kidney transplant recipients, we examined if a voluntary, non-anonymous self-report measure could identify non-adherence in a routine clinic setting and how patients perceived such a measure. Non-adherence was measured using the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) and patient perception was elicited using a customized questionnaire.

RESULTS

Non-responders to the survey (15%) were younger, more likely to be black, and less likely to have had a pre-emptive transplant. Among complete responders (n = 485), 38% reported non-adherence with non-adherent patients being younger (54 y vs. 60 y; p = .01), less likely to have been on dialysis pre-transplant (59% vs. 68%; p = .04), further out from transplant (37 vs. 22 months; p < .001) and had more rejections in the preceding year (8% vs. 3%; p = .02). Self-reported non-adherence was associated with higher calcineurin inhibitor intra-patient variability (27.4% vs. 24.5%; p = .02), but not with donor-specific antibody detection (27.8% vs. 21.2%, p = .15). Of patients providing feedback (n = 500), the majority of patients felt comfortable reporting adherence (92%), that the survey was relevant to their visit (71%), and that the survey did not interfere with their clinic visit (88%).

CONCLUSION

In summary, a self-reported questionnaire during clinic visits identified immunosuppression non-adherence in a significant proportion of patients and was well received by patients. Integrating self-report measures into routine post-transplant care may enable early identification of non-adherence.

Impact of CYP3A5 phenotype on tacrolimus time in therapeutic range and clinical outcomes in pediatric renal and heart transplant recipients

STUDY OBJECTIVE

This study investigated the effect of CYP3A5 phenotype on time in therapeutic range (TTR) of tacrolimus post-transplant in pediatric patients.

DESIGN AND DATA SOURCE

This retrospective study assessed medical records of pediatric kidney and heart recipients with available CYP3A5 genotype for tacrolimus dosing, troughs, and the clinical events (biopsy-proven acute rejection [BPAR] and de novo donor-specific antibodies [dnDSA]).

MEASUREMENTS AND MAIN RESULTS

The primary outcome, mean TTR in the first 90 days post-transplant, was 9.0% (95% CI: -16.1, -1.9) lower in CYP3A5 expressers (p = 0.014) when adjusting for time to therapeutic concentration and organ type. There was no difference between CYP3A5 phenotypes in time to the first clinical event using TTR during the first 90 days. When applying TTR over the first year, there was a significant difference in event-free survival (EFS) which was 50.0% for CYP3A5 expressers/TTR < 35%, 45.5% for expressers/TTR ≥ 35%, 38.1% for nonexpressers/TTR < 35%, and 72.9% for nonexpressers/TTR ≥ 35% (log-rank p = 0.03). A post hoc analysis of EFS identified CYP3A5 expressers had lower EFS compared to nonexpressers in patients with TTR ≥ 35% (p = 0.04) but no difference among patients with TTR < 35% (p = 0.6).

CONCLUSIONS

The relationship between TTR and CYP3A5 phenotype suggests that achieving a TTR ≥ 35% during the first year may be a modifiable factor to attenuate the risk of BPAR and dnDSA.

Tacrolimus variability score outperforms coefficient of variation in predicting clinical outcomes of living kidney transplantation

AIMS

Intrapatient variability (IPV) was previously defined as coefficient of variation (CV) or standard deviation of tacrolimus (Tac) exposure while none of them was easily being interpreted and translated into clinical practice after kidney transplantation.

METHODS

We developed a novel Tac variability score (TVS) to evaluate IPV by calculating the frequency of clinically significant changes of Tac trough levels after kidney transplantation. Multivariate Cox proportional analyses were conducted to compare the impact of TVS and CV on transplant outcomes.

RESULTS

A total of 1343 patients were divided into high TVS (>0.30) and low TVS (<0.30) groups, and low CV (<0.30) and high CV (>0.30) groups. Univariate analyses showed that high TVS (hazard ratio [HR]: 2.323, 95% confidence interval [CI]: 1.455-3.709) and high CV (HR: 1.606, 95%CI: 1.044-2.471) were associated with inferior graft survival. However, only TVS was an independent predictor for graft failure in multivariate analyses (HR: 1.972, 95%CI: 1.2-3.24), and the correlation maintained in high CV (P = .020) and low CV (P = .037) subgroups, while CV failed to predict graft loss in neither low (P = .387) nor high TVS (P = .600) subgroups. In addition, TVS had a higher correlation with graft survival in patients with Tac exposure within the therapeutic range and the correlation was less influenced by mean Tac trough levels.

CONCLUSION

TVS is a novel measure of Tac IPV with higher correlation with graft survival and more convenience in clinical use than CV after kidney transplantation.