Profiles of Immunosuppressive Nonadherence and Association between Medication Adherence and Tacrolimus Intra-patient Variability Among Stable Kidney Transplant Recipients

doi:10.21203/rs.3.rs-3741590/v1

Background

Understanding the modifiable correlates and risk factors for medication non-adherence is essential to develop interventions to improve adherence and thus long-term transplantation outcomes.

Methods

This single-center cross-sectional study collected 409 validated questionnaires of renal transplant recipients followed in outpatient clinics. The incidence of nonadherence to immunosuppressive therapy (implementation phase) was calculated. Multilevel correlates associated with nonadherence were collected to analyze multilevel correlates of nonadherence in kidney transplant recipients. The relationship between nonadherence and recipient outcomes was also explore.

Results

The incidence of nonadherence to immunosuppressive drugs in renal transplant recipients was 41.6%. Multiple logistic regression analysis showed that 4 variables were positively associated with immunosuppressive nonadherence, namely without steady partner (OR: 1.722; 95%CI: 1.034–2.866), post-transplant years (OR: 1.240; 95%CI: 1.134–1.356), twice-daily immunosuppressive regimen (OR: 5.522 95%CI: 2.857–10.671), Tac IPV (OR: 1.029 95%CI: 1.007–1.052). There was a significant difference in Tac IPV between the adherent and nonadherent groups. The incidence of rejection was 4.2% (10/239) in the adherent group and 12.4% (21/170) in the nonadherent group(p = 0.002). In addition, nonadherence (OR: 2.967, 95%CI: 1.248–7.058) and Tac IPV (OR: 1.030, 95%CI: 1.001–1.060) were positively correlated with episodes of rejection.

Conclusion

The higher incidence of immunosuppression nonadherence in renal transplant recipients correlates with lacking a steady domestic partner, longer time since the transplantation, higher frequency of immunosuppressive dosing, and higher Tac IPV. In addition, nonadherence and higher tacrolimus IPV were correlated with episodes of rejection. Tacrolimus IPV can be an additional instrument for identifying recipients at risk of nonadherence.

Health sciences/Medical research

Health sciences/Nephrology

Health sciences/Risk factors

Kidney transplantation

Immunosuppressant nonadherence

BAASIS

Tacrolimus IPV

Rejection

Kidney transplantation (KT) is a primary treatment for end-stage renal diseases. Although progress has been made in short-term outcomes of KT, the loss of long-term allografts remains a key challenge¹. Nonadherence to immunosuppressive medication has long been considered a modifiable risk factor for long-term graft failure^2–4. Medication nonadherence increases the intrapatient variability (IPV) of immunosuppressive medication concentrations, which may lead to the development of de novo donor-specific human leukocyte antigen antibody(dnDSA) in recipients and increases the risk of long-term graft rejection and loss^5–7. Reports indicate that 15–60% of late acute rejections and 35–45% of graft losses are associated with medication nonadherence. Therefore, understanding the modifiable correlates and risk factors for medication nonadherence is critical for developing interventions to improve adherence and thus long-term transplant outcomes^2,8.

The Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group identifies nonadherence as “deviation from the prescribed medication regimen sufficient to adversely influence the regimen's intended effect”⁹. As defined by the Ascertaining Barriers to Compliance (ABC) taxonomy, medication adherence refers to the process by which patients take their medications as prescribed, and is further categorized into three quantifiable phases: "initiation," "implementation," and "discontinuance "¹⁰. ABC has also reported rates of nonadherence to medication as high as 50% in developed countries and even higher in developing countries⁹. A review of studies showed a nonadherence prevalence rate ranging from 36–55% in KT, higher than that for other solid organ transplant recipients (range, 7–15%)^11–14. This variability is likely due to the use of different diagnostic methods with different specificities and sensitivities¹⁴. Adherence can be assessed using direct or indirect methods, including pill counts, electronic pill dispensers, questionnaires, and immunosuppressive drug trough concentrations^3,14. The Basel Assessment of Adherence to Immunosuppressive Drugs Scale (BAASIS) is a validated self-reported medication-taking behavior scale widely used in the field of KT^4,14–17.

Medication nonadherence of kidney transplant recipients is influenced by several factors. The World Health Organization has defined five major categories of risk factors that may influence adherence behavior: social- and economic-related factors, health system/healthcare team-related factors, therapy-related factors, condition-related factors, and patient-related factors¹⁸. Studies on the multilevel correlates of immunosuppressive nonadherence after heart and renal transplantation, including BRIGHT^19–21 and STICK² BRAZIL, have been guided by Bronfenbrenner's ecological model, which assumes that individual behavior is the result of multilevel determinants, with the patient in the center (patient-level), followed by the influence of their healthcare provider/family (micro-level), the healthcare organization (meso-level), and the healthcare system and related policies (macro-level)²².

Few reported studies exist on adherence in Chinese renal transplant recipients, and information on the correlation of IPV with nonadherence is limited^23–26. Therefore, the main objectives of this study were to assess the prevalence of nonadherence to immunosuppressives therapy (implementation phase) in Chinese renal transplant recipients, explore the multilevel correlates associated with nonadherence, and investigate the relationship between tacrolimus (Tac) IPV and nonadherence. The secondary objective of this study was to explore the relationship between Tac IPV and nonadherence with the recipients’ prognoses.

2.1 Design and Study Population

This was a single-center, cross-sectional study. This study included kidney transplant recipients who were followed-up in the outpatient clinic between January 2022 and January 2023. The main inclusion criteria were as follows: (i) age >18 years, (ii) >1-month post-transplant, (iii) treatment with immunosuppressive drugs containing Tac at follow-up, and (iv) the ability to understand the objectives of the study and provide written informed consent. The major exclusion criteria were pediatric recipients aged <18 years, treatment with immunosuppressive drugs not containing Tac at follow-up , and multi-organ transplant recipients. A total of 409 validated adherence assessment questionnaires were collected from kidney transplant recipients who met the inclusion and exclusion criteria. The incidence of nonadherence to immunosuppressive therapy (implementation phase) was calculated. Patient-level (sociodemographic, clinical, and treatment-related factors), micro-level (family/healthcare provider), meso level (transplant center), macro-level (healthcare system) variables were collected to analyze the multilevel correlates of nonadherence in kidney transplant recipients. The relationship between nonadherence and recipient outcomes was also explored.

There were 84 beds in the clinical department of our transplant center. Our transplant center has been at the forefront in China in terms of three comprehensive indexes of transplantation programs, cumulative number of cases, and long-term survival. It is the first unit in China to have performed over 7000 renal transplants, which represents the national lead in this category, providing a relevant environment for our research on adherence in renal transplant recipients.

This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20220618).

2.2 Variables and Measurements

2.2.1 Adherence to Immunosuppressant Therapy

The validated interview version of BAASIS was used to assess the implementation stage of adherence to immunosuppressant therapy (with permission) ^27,28. This self-report questionnaire is consistent with the ABC classification of medication adherence and uses four items (taking and timing adherence, drug holidays, and dose reduction) to measure the implementation phase of nonadherence to immunosuppressants (implementation nonadherence). KT subjects who reported deviations in any item within the past four weeks were considered nonadherent. The concurrent and predictive validity of BAASIS has been previously established^2,29-31.

2.2.2 Multilevel Correlates of Immunosuppressive Nonadherence

The multilevel correlates of nonadherence were as follows: At the patient-level, factors included were sociodemographic factors, clinical factors, and treatment-related factors. Sociodemographic factors included age, gender, height, weight, body mass index (BMI), educational level (middle school and below, high school, and college), employment status (yes or no), marital status (steady partner or not), and family income (average disposable income, <¥20,000/year, ¥20,000-40,000/year, ¥40,000-80,000/year, and >¥80,000/year). Clinical factors consisted of chronic kidney disease etiology (polycystic kidney disease, nephrolith, chronic nephritis/nephropathy, others, unknown), time on the pre-KT treatment (years), pre-KT treatment modality (preemptive, peritoneal, hemodialysis, and peritoneal & hemodialysis). Treatment-related factors included post-transplant years, donor type (deceased or living donor), frequency of immunosuppressant regimens (1 or 2 doses/d), Tac IPV, adverse event episodes (self-reported medication side effects, yes/no). At the micro-level, the patients evaluated their satisfaction with the transplant team (good, fair, or poor). At the meso-level, the city of origin (same city, same province, or other provinces as the transplant center) was compiled. At the macro-level, the type of medical insurance (NRCMS, New Rural Cooperative Medical Scheme; UEBMI, Urban Employee Basic Medical Insurance, URBMI, Urban Resident Basic Medical Insurance) was documented (detailed information in Table 1).

2.2.3 Data Collection and Assessments

Kidney transplant recipients who met the inclusion and exclusion criteria and signed the written consent form were interviewed by the transplantation team. Correlates were assessed by interviewers or were collected from the medical files. For all renal transplant recipients undergoing the adherence survey, ten Tac trough concentrations were collected from each recipient at the outpatient follow-up visits before the start of the adherence assessment. At least three samples were available for calculating the IPV of Tac in renal transplant recipients. The coefficient of variation (CV) was used to quantify Tac IPV^32-35. The CV is represented by the ratio of the standard deviation (σ) to the mean (μ). Banff 2014 was used to grade the biopsy specimens.

2.3 Statistical Analysis

Data are expressed as mean ± standard deviation for normally distributed variables, median (interquartile range) for non-normally distributed variables, and number (proportion) for categorical variables. Factors potentially associated with adherence profiles were explored by comparison between latent classes, using the t-test and Mann–Whitney U-test for normally and non-normally distributed data, respectively, and the χ2 test or Fisher's exact test for categorical data. We first considered nonadherence to immunosuppressants as a binary variable and performed univariate binary logistic regression analyses using generalized estimating equations assuming Binomial family and logit link function, providing original odds ratios (ORs) as associate parameters. Multivariate binary logistic regression analysis was performed for variables with p<0.1. All statistical analyses were performed using the SPSS software version 26.0. A p-value<0.05 was considered statistically significant.

3.1 Sample Characteristics

Between January 2022 and January 2023, 431 renal transplant recipients undergoing outpatient follow-up received adherence questionnaires, and 409 (94.9%) met the inclusion criteria of the analysis. There were 301 (73.6%) males and 108 (26.4%) females. The mean age of these patients was 39.6±11.1 years, the mean BMI was 21.5±3.4 kg/m², and the median post-transplant period was 1.6 (IQR 3.3) years. A total of 317 (77.5%) recipients received kidneys from deceased donors while 92 (22.5%) received kidneys from living donors. Descriptive statistics of the multilevel variables (implementation phase) and the results of the bivariate analysis for the two groups are shown in Table 1.

3.2 Prevalence of Nonadherence to Immunosuppressant Medication

The present study showed a 41.6% (170/409) nonadherence rate to immunosuppressant medication in kidney transplant recipients. The most common issue of the four examined was taking nonadherence (forgetting), which was reported by 30.6% of patients, followed by timing nonadherence (delay >2 h) at 23%; whereas drug holidays (5.9%) and dose reduction (7.1%) were less frequently reported (Table 2).

3.3 Multilevel Correlates of Immunosuppressant Nonadherence

Variables with p<0.1, including educational level, marital status, post-transplant years, donor type, frequency of immunosuppressant regimens, Tac IPV, and adverse event episodes, were included in the multiple logistic regression analysis. The results showed that four variables were positively associated with immunosuppressive nonadherence, namely without steady partner (OR: 1.722, 95% confidence interval [CI]: 1.034-2.866), post-transplant years (OR: 1.240, 95% CI: 1.134-1.356), twice-daily immunosuppressive regimen (OR: 5.522, 95% CI: 2.857-10.671), Tac IPV (OR: 1.029, 95%CI: 1.007-1.052) (Table 3).

3.4 Differences in IPV between Adherent and Nonadherent Groups

A significant difference was observed in Tac IPV between the adherent and nonadherent groups (22.8±9.3 vs. 25.5±11.7, p=0.016) (Table 4).

3.5 Clinical Outcomes

The incidence of rejection was 4.2% (10/239) in the adherent group and 12.4% (21/170) in the nonadherent group (p=0.002). In terms of different types of rejection, the incidence of antibody-mediated rejection was higher in the nonadherent group than in the adherent group (4.7% vs. 0.4%, p=0.01) (Table 5). To explore the association between nonadherence and Tac IPV with transplantation outcomes, nonadherence, Tac IPV, and variables independently associated with nonadherence were included in multivariate binary logistic regression analyses using rejection episodes as a binary variable. Results showed that nonadherence (OR: 2.967, 95%CI: 1.248-7.058) and Tac IPV (OR: 1.030, 95%CI: 1.001-1.060) were positively associated with rejection episodes (Table 6).

This study assessed the prevalence of nonadherence to immunosuppressant therapy (implementation phase) in Chinese renal transplant recipients and explored the multilevel correlates associated with nonadherence. In addition, we investigated the relationship between Tac IPV and nonadherence, as well as the relationship between Tac IPV and nonadherence with episodes of rejection.

We assessed medication adherence in kidney transplant recipients using BAASIS, which has good validity and reliability as a self-report tool for assessing medication nonadherence in transplant recipients and can be easily implemented clinically. Our results showed a high incidence of nonadherence to immunosuppressant therapy in renal transplant recipients of 41.6%, and some studies that also used BAASIS to report nonadherence showed similar results in adult^11,36–38 and pediatric³⁹ renal transplant recipients (30–54%). However, that these data may be subject to recruitment bias and/or social desirability needs to be considered.³⁸

The potential correlates of the four levels of nonadherence based on the Ecological Model were investigated in our study. The assessment of patient-level factors was more frequent, whereas that of micro-, meso-, and macro-level factors was relatively lacking. At the patient level, our study showed that lack of a steady partner, longer post-transplantation time, higher Tac IPV, and a higher frequency of immunosuppressant medication were positively associated with nonadherence. These results are consistent with previous studies in which longer post-transplant time was a risk factor for nonadherence, with an increasing proportion of nonadherent recipients over time.^14,40,41 Several interventional and observational studies have confirmed that reducing pill load and decreasing medication frequency can significantly improve adherence.^28,42,43 Although extended-release Tac (ER-Tac) allows once-daily dosing, which has the potential to improve treatment adherence, mycophenolate acid (MPA) does not allow once-daily dosing; therefore, the current mainstream immunosuppressive regimen, TAC/MPA/ prednisone, still requires twice-daily dosing. In contrast to previous studies, we achieved a true once-daily immunosuppressive regimen by using sirolimus (SRL) in combination with low-dose ER-TAC, and renal transplant recipients receiving this simplified once-daily immunosuppressive regimen showed significantly improved medication adherence. Some of the classical correlates associated with nonadherence, such as age^{9,40,41,44–46}, male sex⁴², and socioeconomic level nonadherence^14,40,42, were not detected in our study. As this was a single-center study, exploration was limited of the other three levels of correlation. We did not find a significant association between patient satisfaction with the transplant team (micro-level), city of origin (meso-level), or type of medical insurance (macro-level) with nonadherence to treatment. China has committed to universal health coverage and “Healthy China 2030”. Since this healthcare reform, out-of-pocket expenditures as a percentage of the current health expenditures in China have dropped dramatically. Health insurance reform has been achieved in terms of the breadth of coverage in the population, the comprehensiveness of the benefits packages, and increased reimbursement rates. The coverage of healthcare services has progressed greatly in terms of accessibility, equity, and quality.^47–50 However, subsequent expanded studies with more comprehensive multilevel correlations are needed.

We calculated the Tac IPV using CV and explored the relationship between Tac IPV and medication adherence. The results showed that the mean Tac IPV was 23.9%, which was similar to previous studies.^51,52 A correlation between Tac IPV and self-reported medication adherence was also found, and recipients with high IPV were more likely to experience nonadherence. Therefore, Tac IPV may serve as a predictor of adherence. A review of Tac IPV suggested that Tac IPV could be used as a surrogate marker of adherence,³³ but some studies showed no significant correlation between Tac IPV and adherence.⁵³ Although there is no gold standard for clinical adherence monitoring, and different methods of adherence monitoring have different variabilities, Tac IPV can be used as an additional tool to identify recipients at risk of nonadherence.⁵²

The relationship between Tac IPV and recipient-reported nonadherence to rejection was explored in this study. The findings showed that Tac IPV and recipient-reported nonadherence were associated with rejection. Rejection episodes, particularly antibody-mediated rejection episodes, were more frequent in nonadherent recipients than in adherent recipients. A previous study on the impact of Tac IPV on the clinical outcomes of renal transplantation reported that high Tac IPV adversely affected graft survival, acute rejection, dnDSA, chronic immune-mediated graft injury, and histologic lesions.³³ Another study on Tac IPV and graft outcomes included more than 6600 kidney transplant recipients and concluded that Tac IPV was associated with poor outcomes after KT. Using 30% as the threshold to divide patients into high and low IPV groups, these results indicated that increased IPV significantly impairs graft survival with a 32% increased risk of graft loss at 4–6 years in recipients with an IPV of 30–44% and 66% in recipients with an IPV of ≥ 45% compared with those with a low IPV (< 30%).⁵⁴ In addition, several studies have also reported a correlation between nonadherence and adverse graft outcomes.^3,38,55 Therefore, it is particularly important to identify risk factors for nonadherence to immunosuppressive medication and to implement appropriate interventions to improve medication adherence in recipients.

Our study has several limitations. First, this was a single-center retrospective cross-sectional study, which should be interpreted with caution in terms of causality.The multilevel correlates of nonadherence, especially micro-, meso-, and macro-level correlates, were not explored comprehensively, and further multicenter prospective studies are warranted to explore the multilevel correlates of nonadherence in a more comprehensive manner. Second, a validated self-report survey was used to assess adherence, and this measure of adherence may overestimate or underestimate nonadherence because of the subjective nature of the recipients. We did not use more objective reference methods for nonadherence, such as electronic medication monitoring systems. These methods are considered the closest to the gold standard for measuring nonadherence, but they are both expensive and difficult to implement in routine clinical treatment. Third, we measured IPV only for Tac, ignoring the effects of other immunosuppressive agents (including cyclosporine and mammalian target of rapamycin [mTOR] inhibitors). Although each solid-organ transplant recipient is subjected to regular therapeutic drug monitoring with calcineurin inhibitors (cyclosporine and tacrolimus) and/or mTOR inhibitors (sirolimus and everolimus) during post-transplant follow-up, the most common assessment of immunosuppression nonadherence by therapeutic drug monitoring is the variability of Tac trough levels. Moreover, our study lacked risk factors associated with Tac IPV, such as age, sex, BMI, genetic polymorphisms in CYP3A5 and CYP3A4, drug interactions, liver function, and lifestyle choices account for the differences in IPV. Similarly, IPV is affected by adherence, gastrointestinal metabolism and motility, diarrhea, food and drug interactions, synchronicity of dose administration and blood tests, and variability in the laboratory assays. Therefore, deeper exploration is needed to further substantiate the relationship between Tac IPV and nonadherence. In addition, further studies are needed to investigate the relationship between nonadherence and IPV of immunosuppressive agents with long-term renal transplant outcomes, and to explore further prospective interventions for modifiable risk factors associated with nonadherence.

In conclusion, the incidence of nonadherence to immunosuppressives therapy in renal transplant recipients in this study was as high as 41.6%. Lacking a steady domestic partner, longer time since the transplantation, higher frequency of immunosuppressive dosing, and higher Tac IPV were positively associated with nonadherence to immunosuppressives medication. Nonadherence and higher Tac IPV were correlated with episodes of rejection. In the clinic, Tac IPV can be an additional instrument for identifying recipients at risk of nonadherence.

Acknowledgments: We thank the volunteers, investigators, and site staff who participated in this study, with special thanks to Yuanyuan Zhao for assistance with the statistical analyses of this study.

Authors’ contributions: Z.ZOU designed the study, analyzed the data and wrote the paper. W. Zhang, S. Chang, S. Chen designed the study and analyzed the data. W. Zhang had full access to all of the data and performed the analyses. All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted.

Data availability statement: Deidentified data are available upon reasonable request from the corresponding author.

Conflict of interest statement: The authors have no relevant conflicts of interest to declare. The results presented in this paper have not been published previously.

Funding: NO

Lentine, K. L. et al. OPTN/SRTR 2020 Annual Data Report: Kidney. Am J Transplant 22 Suppl 2, 21-136, doi:10.1111/ajt.16982 (2022).
Sanders-Pinheiro, H. et al. Multilevel Correlates of Immunosuppressive Nonadherence in Kidney Transplant Patients: The Multicenter ADHERE BRAZIL Study. Transplantation 105, 255-266, doi:10.1097/tp.0000000000003214 (2021).
Villeneuve, C. et al. Adherence profiles in kidney transplant patients: Causes and consequences. Patient Educ Couns 103, 189-198, doi:10.1016/j.pec.2019.08.002 (2020).
Gandolfini, I., Palmisano, A., Fiaccadori, E., Cravedi, P. & Maggiore, U. Detecting, preventing and treating non-adherence to immunosuppression after kidney transplantation. Clin Kidney J 15, 1253-1274, doi:10.1093/ckj/sfac017 (2022).
Rodrigo, E. et al. Within-Patient Variability in Tacrolimus Blood Levels Predicts Kidney Graft Loss and Donor-Specific Antibody Development. Transplantation 100, 2479-2485, doi:10.1097/tp.0000000000001040 (2016).
Sapir-Pichhadze, R., Wang, Y., Famure, O., Li, Y. & Kim, S. J. Time-dependent variability in tacrolimus trough blood levels is a risk factor for late kidney transplant failure. Kidney Int 85, doi:10.1038/ki.2013.465 (2014).
Cherukuri, A. et al. Post-transplant donor specific antibody is associated with poor kidney transplant outcomes only when combined with both T-cell-mediated rejection and non-adherence. Kidney Int 96, 202-213, doi:10.1016/j.kint.2019.01.033 (2019).
Nevins, T. E., Nickerson, P. W. & Dew, M. A. Understanding Medication Nonadherence after Kidney Transplant. J Am Soc Nephrol 28, 2290-2301, doi:10.1681/asn.2017020216 (2017).
KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant 9 Suppl 3, S1-155, doi:10.1111/j.1600-6143.2009.02834.x (2009).
Vrijens, B. et al. A new taxonomy for describing and defining adherence to medications. British journal of clinical pharmacology 73, 691-705, doi:10.1111/j.1365-2125.2012.04167.x (2012).
Dew, M. A. et al. Rates and risk factors for nonadherence to the medical regimen after adult solid organ transplantation. Transplantation 83, 858-873, doi:10.1097/01.tp.0000258599.65257.a6 (2007).
Gustavsen, M. T. et al. Evaluation of tools for annual capture of adherence to immunosuppressive medications after renal transplantation - a single-centre open prospective trial. Transplant international : official journal of the European Society for Organ Transplantation 32, 614-625, doi:10.1111/tri.13412 (2019).
Schäfer-Keller, P., Steiger, J., Bock, A., Denhaerynck, K. & De Geest, S. Diagnostic accuracy of measurement methods to assess non-adherence to immunosuppressive drugs in kidney transplant recipients. Am J Transplant 8, 616-626, doi:10.1111/j.1600-6143.2007.02127.x (2008).
Gokoel, S. R. M. et al. Medication non-adherence after kidney transplantation: A critical appraisal and systematic review. Transplant Rev (Orlando) 34, 100511, doi:10.1016/j.trre.2019.100511 (2020).
Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS©) – EXPLANATION, <https://baasis.nursing.unibas.ch/data/2020-BAASIS-explanation.pdf> (
Denhaerynck, K., Dobbels, F., Košťálová, B. & De Geest, S. Psychometric Properties of the BAASIS: A Meta-analysis of Individual Participant Data. Transplantation 107, 1795-1809, doi:10.1097/tp.0000000000004574 (2023).
Burkhart, P. V. & Sabaté, E. Adherence to long-term therapies: evidence for action. J Nurs Scholarsh 35, 207 (2003).
Denhaerynck, K. et al. Multilevel factors are associated with immunosuppressant nonadherence in heart transplant recipients: The international BRIGHT study. Am J Transplant 18, 1447-1460, doi:10.1111/ajt.14611 (2018).
Schönfeld, S. et al. Prevalence and Correlates of Cost-Related Medication Nonadherence to Immunosuppressive Drugs After Heart Transplantation: The International Multicenter Cross-sectional Bright Study. J Cardiovasc Nurs 35, 519-529, doi:10.1097/jcn.0000000000000683 (2020).
Marston, M. T., Berben, L., Dobbels, F., Russell, C. L. & de Geest, S. Prevalence and Patient-Level Correlates of Intentional Non-Adherence to Immunosuppressive Medication After Heart-Transplantation-Findings From the International BRIGHT Study. Transplant international : official journal of the European Society for Organ Transplantation 36, 11308, doi:10.3389/ti.2023.11308 (2023).
Berben, L., Dobbels, F., Engberg, S., Hill, M. N. & De Geest, S. An ecological perspective on medication adherence. West J Nurs Res 34, 635-653, doi:10.1177/0193945911434518 (2012).
Teng, S. et al. Symptom Experience Associated With Immunosuppressive Medications in Chinese Kidney Transplant Recipients. J Nurs Scholarsh 47, 425-434, doi:10.1111/jnu.12157 (2015).
Zhao, L., Yan, J., Yang, G. L. & Liu, Y. A Study on Adherence to Follow-up, Quality of Life, and Associated Factors Among Renal Transplant Recipients in China. Transplantation proceedings 49, 1285-1290, doi:10.1016/j.transproceed.2017.03.086 (2017).
Chen, T. et al. Follow-Up Factors Contribute to Immunosuppressant Adherence in Kidney Transplant Recipients. Patient Prefer Adherence 16, 2811-2819, doi:10.2147/ppa.S383243 (2022).
Yang, L. et al. Exploration of Adherence to the Immunosuppressive Medication in Kidney Transplant Recipients Based on Theory of Planned Behavior. Clin Nurs Res 31, 1189-1198, doi:10.1177/10547738221096550 (2022).
Sanders-Pinheiro, H., Colugnati, F. A. B., Marsicano, E. O., De Geest, S. & Medina, J. O. P. Prevalence and correlates of non-adherence to immunosuppressants and to health behaviours in patients after kidney transplantation in Brazil - the ADHERE BRAZIL multicentre study: a cross-sectional study protocol. BMC Nephrol 19, 41, doi:10.1186/s12882-018-0840-6 (2018).
van Zanten, R., de Weerd, A., Betjes, M., Boer-Verschragen, M. & Massey, E. K. Is simplification of immunosuppressive medication a way to promote medication adherence of kidney transplant recipients? Findings from a randomized controlled trial. Transplant international : official journal of the European Society for Organ Transplantation 34, 1703-1711, doi:10.1111/tri.13993 (2021).
Marsicano Ede, O. et al. Transcultural adaptation and initial validation of Brazilian-Portuguese version of the Basel assessment of adherence to immunosuppressive medications scale (BAASIS) in kidney transplants. BMC Nephrol 14, 108, doi:10.1186/1471-2369-14-108 (2013).
Elias, C. & Cherukuri, A. BAASIS for Monitoring Therapy Nonadherence in Clinical Transplantation: Are We There Yet? Transplantation, doi:10.1097/tp.0000000000004575 (2023).
Dobbels, F. et al. The psychometric properties and practicability of self-report instruments to identify medication nonadherence in adult transplant patients: a systematic review. Transplantation 90, 205-219, doi:10.1097/TP.0b013e3181e346cd (2010).
Shuker, N., Gelder, T. & Hesselink, D. A. Intra-patient variability in tacrolimus exposure: causes, consequences for clinical management. Transplant Rev 9, doi:10.1016/j.trre.2015.01.002 (2015).
Gonzales, H. M. et al. A comprehensive review of the impact of tacrolimus intrapatient variability on clinical outcomes in kidney transplantation. Am J Transplant 20, 1969-1983, doi:10.1111/ajt.16002 (2020).
Rahamimov, R. et al. Reduction of exposure to tacrolimus trough level variability is associated with better graft survival after kidney transplantation. European journal of clinical pharmacology 75, 951-958, doi:10.1007/s00228-019-02643-y (2019).
Leino, A. D. et al. Assessment of tacrolimus intrapatient variability in stable adherent transplant recipients: Establishing baseline values. Am J Transplant 19, 1410-1420, doi:10.1111/ajt.15199 (2019).
Pabst, S., Bertram, A., Zimmermann, T., Schiffer, M. & de Zwaan, M. Physician reported adherence to immunosuppressants in renal transplant patients: Prevalence, agreement, and correlates. J Psychosom Res 79, 364-371, doi:10.1016/j.jpsychores.2015.09.001 (2015).
Lennerling, A. & Forsberg, A. Self-reported non-adherence and beliefs about medication in a Swedish kidney transplant population. Open Nurs J 6, 41-46, doi:10.2174/1874434601206010041 (2012).
Scheel, J. et al. Patient-reported non-adherence and immunosuppressant trough levels are associated with rejection after renal transplantation. BMC Nephrol 18, 107, doi:10.1186/s12882-017-0517-6 (2017).
Dobbels, F. et al. Adherence to the immunosuppressive regimen in pediatric kidney transplant recipients: a systematic review. Pediatric transplantation 14, 603-613, doi:10.1111/j.1399-3046.2010.01299.x (2010).
Nerini, E., Bruno, F., Citterio, F. & Schena, F. P. Nonadherence to immunosuppressive therapy in kidney transplant recipients: can technology help? J Nephrol 29, 627-636, doi:10.1007/s40620-016-0273-x (2016).
Belaiche, S. et al. Factors relevant to medication non-adherence in kidney transplant: a systematic review. Int J Clin Pharm 39, 582-593, doi:10.1007/s11096-017-0436-4 (2017).
Kobayashi, S. et al. Medication nonadherence after kidney transplantation: an internet-based survey in Japan. Psychol Health Med 25, 91-101, doi:10.1080/13548506.2019.1622745 (2020).
Oh, C. K. et al. Improvement of medication adherence with simplified once-daily immunosuppressive regimen in stable kidney transplant recipients: A prospective cohort study. Asian J Surg 43, 660-667, doi:10.1016/j.asjsur.2019.07.011 (2020).
Spivey, C. A., Chisholm-Burns, M. A., Damadzadeh, B. & Billheimer, D. Determining the effect of immunosuppressant adherence on graft failure risk among renal transplant recipients. Clinical transplantation 28, 96-104, doi:10.1111/ctr.12283 (2014).
Kindem, I. A. et al. Kidney-transplanted Adolescents-Nonadherence and Graft Outcomes During the Transition Phase: A Nationwide Analysis, 2000-2020. Transplantation 107, 1206-1212, doi:10.1097/tp.0000000000004431 (2023).
Varnell, C. D., Jr., Rich, K. L., Modi, A. C., Hooper, D. K. & Eckman, M. H. A Cost-effectiveness Analysis of Adherence Promotion Strategies to Improve Rejection Rates in Adolescent Kidney Transplant Recipients. Am J Kidney Dis 80, 330-340, doi:10.1053/j.ajkd.2021.12.013 (2022).
Fang, H., Eggleston, K., Hanson, K. & Wu, M. Enhancing financial protection under China's social health insurance to achieve universal health coverage. Bmj 365, l2378, doi:10.1136/bmj.l2378 (2019).
Tao, W. et al. Towards universal health coverage: achievements and challenges of 10 years of healthcare reform in China. BMJ Glob Health 5, e002087, doi:10.1136/bmjgh-2019-002087 (2020).
Zhang, H., Zhang, C., Zhu, S., Ye, H. & Zhang, D. Direct medical costs of end-stage kidney disease and renal replacement therapy: a cohort study in Guangzhou City, southern China. BMC Health Serv Res 20, 122, doi:10.1186/s12913-020-4960-x (2020).
Liu, X., Wang, Z., Zhang, H. & Meng, Q. Measuring and evaluating progress towards Universal Health Coverage in China. J Glob Health 11, 08005, doi:10.7189/jogh.11.08005 (2021).
Schumacher, L., Leino, A. D. & Park, J. M. Tacrolimus intrapatient variability in solid organ transplantation: A multiorgan perspective. Pharmacotherapy 41, 103-118, doi:10.1002/phar.2480 (2021).
Kostalova, B. et al. Comparison of different methods to assess tacrolimus concentration intra-patient variability as potential marker of medication non-adherence. Frontiers in pharmacology 13, 973564, doi:10.3389/fphar.2022.973564 (2022).
Ko, H. et al. Association between medication adherence and intrapatient variability in tacrolimus concentration among stable kidney transplant recipients. Scientific reports 11, 5397, doi:10.1038/s41598-021-84868-5 (2021).
Süsal, C. & Döhler, B. Late intra-patient tacrolimus trough level variability as a major problem in kidney transplantation: A Collaborative Transplant Study Report. Am J Transplant 19, 2805-2813, doi:10.1111/ajt.15346 (2019).
Prendergast, M. B. & Gaston, R. S. Optimizing medication adherence: an ongoing opportunity to improve outcomes after kidney transplantation. Clin J Am Soc Nephrol 5, doi:10.2215/CJN.07241009 (2010).

Table 1. Descriptive statistics of the multilevel variables [adherent/nonadherent (implementation phase)] and results of bivariate analysis

	Adherent (n=239)	Nonadherent (n=170)	Odds ratio (95% CI), p-value
Patient-level: sociodemographic, clinical, treatment-related factors
Sociodemographic factors
Age (year)	39.8±11.1	39.3±11.1	0.996(0.978-1.014), 0.647
Male recipient n (%)	171（71.5）	130（76.5）	Reference
Female recipient n (%)	68 (28.5)	40 (23.5)	0.774(0.492-1.216), 0.266
Height (cm)	168.5±7.3	167.9±7.8	0.989(0.963-1.015), 0.407
Weight (kg)	60.8±11.8	61.8±11.4	1.008(0.991-1.025), 0.382
Body mass index (kg/m ²)	21.3±3.5	21.8±3.2	1.046(0.987-1.109), 0.129
Educational level n (%)
Middle school and below	43(18)	44（25.9）	Reference
High school	94（39.3）	60（35.3）	0.624(0.367-1.060), 0.081
College	102（42.7）	66（38.8）	0.632(0.375-1.066), 0.085
Employment status (%)
Actively employed	125(52.3)	85(50.0)	Reference
Not actively employed	114(47.7)	85(50.0)	1.096(0.740-1.625), 0.646
Marital status n (%)
Steady partner	188(78.7)	120(70.6)	Reference
Without steady partner	51(21.3)	50(29.4)	1.536(0.977-2.414), 0.063
Familiar income n (%)
<¥20,000/year	56(23.4)	49(28.8)	Reference
¥20,000-40,000/year	78(32.6)	57(33.5)	0.835(0.500-1.396), 0.492
¥40,000-80,000/year	57(23.8)	34(20.0)	0.682(0.385-1.208), 0.189
>¥80,000/year	48(20.1)	30(17.6)	0.714(0.394-1.296), 0.268
Clinical factors
Chronic kidney disease etiology n (%)
Polycystic kidney disease	3(1.3)	3(1.8)	Reference
Nephrolith	7(2.9)	2(1.2)	0.286(0.030-2.692), 0.274
Chronic nephritis/nephropathy	133(55.6)	88(51.8)	0.662(0.131-3.353), 0.662
Others	3(1.3)	1（0.6）	0.333(0.021-5.329), 0.437
Unknown	93(38.9)	76(44.7)	0.817(0.160-4.166), 0.808
Time on the pre-KT treatment n (%)
<1 year	111(46.4)	91(53.5)	Reference
1-3year	90(37.7)	55(32.4)	0.745(0.482-1.152), 0.186
>3year	38(15.9)	24(14.1)	0.770(0.431-1.378), 0.379
Pre-KT treatment modality n (%)
Preemptive	10(4.2)	4(2.4)	Reference
Peritoneal dialysis	27(11.3)	19(11.2)	1.759(0.480-6.453), 0.394
Hemodialysis	195(81.6)	143(84.1)	1.833(0.564-5.963), 0.314
Peritoneal & Hemodialysis	7(2.9)	4(2.4)	1.429(0.264-7.737), 0.679
Treatment-related factors
Post-transplant years*(IQR)	1.0 (2.4)	3.0 (4.4)	1.239(1.142-1.345), <0.001
Type of donor n (%)
Deceased donor	193(80.8)	124(72.9)	Reference
Living donor	46(19.2)	46(27.1)	1.556(0.976-2.482), 0.063
Frequency of immunosuppressive regimens n (%)
1 dose/d (QD)	77(32.2)	13(7.6)	Reference
2 doses/d (Q12)	162(67.8)	157(92.4)	5.740(3.065-10.749), <0.001
Tac IPV (%)	22.8±9.3	25.5±11.7	1.024(1.005-1.044), 0.014
Adverse event episodes n (%)	91(38.1)	84(49.4)	1.589(1.067-2.366), 0.023
Micro-level: family/healthcare provider
Patient satisfaction with the transplant team n (%)
Good	227(95)	155(91.2)	Reference
Fair	11(4.6)	13(7.6)	1.731(0.756-3.963), 0.194
Poor	1(0.4)	2(1.2)	2.929(0.263-32.583), 0.382
Meso level: transplant center
City of origin n (%)
Other provinces of transplant center	5(2.1)	4(2.4)	Reference
Same province of transplant center	168(70.3)	111(65.3)	0.826(0.217-3.143), 0.779
Same city of transplant center	66(27.6)	55(32.4)	1.042(0.267-4.069), 0.953
Macro-level: healthcare system
Type of medical insurance n (%)
NRCMS	34(14.2)	26(15.3)	Reference
UEBMI	77(32.3)	57(33.5)	0.968(0.523-1.790), 0.918
URBMI	128(53.6)	87(51.2)	0.889(0.498-1.585), 0.690

*Median (interquartile range) for non-normally distributed variables.

NRCMS, New Rural Cooperative Medical Scheme; UEBMI, Urban Employee Basic Medical Insurance; URBMI, Urban Resident Basic Medical Insurance.

Table 2. Medication adherence in kidney transplant recipients

	never	once a month	every two weeks	every week	more than once a week	every day
Taking nonadherence (forgetting) n (%)	284(69.4)	73(17.8)	42(10.3)	5(1.2)	4(1.0)	1(0.2)
Drug holidays n (%)	385(94.1)	7(1.7)	14(3.4)	1(0.2)	2(0.5)	0(0)
Timing nonadherence (delay >2 h) n (%)	315(77)	46(11.2)	34(8.3)	7(1.7)	7(1.7)	0(0)
Dose reduction n (%)	380(92.9)	18(4.4)	7(1.7)	0(0)	4(1.0)	0(0)

Table 3. Multiple logistic regression analysis of nonadherence to immunosuppressives

Nonadherence to immunosuppressives (BAASIS 4 questions)	Odds ratio	95% confidence interval	p-value
Educational level (High school)	0.776	0.428-1.408	0.404
Educational level (College)	0.850	0.472-1.528	0.586
Marital status (Without steady partner)	1.722	1.034-2.866	0.037
Post-transplant years	1.240	1.134-1.356	<0.001
Type of donor (Living donor)	0.982	0.579-1.666	0.948
Frequency of immunosuppressive regimens (2 doses/d)	5.522	2.857-10.671	<0.001
Tac IPV	1.029	1.007-1.052	0.011
Adverse event episodes	1.445	0.924-2.262	0.107

Table 4. Intra-patient variability of exposure to immunosuppressive drugs

Tac trough concentration	Population	Mean (ng/ml)	SD (ng/ml)	Coefficient of variation (%)	Sample size (n)	p-value
Tacrolimus	Total sample	6.3±1.7	1.5±0.9	23.9±10.4	409	0.184^a/0.123^b
	Adherents	6.4±1.9	1.4±0.7	22.8±9.3	239	0.016^c
	Non-adherence	6.3±1.4	1.6±1.0	25.5±11.7	170

^a Adherents VS Total sample

^b Non-adherence VS Total sample

^c Adherents VS Non-adherence

Table 5. Comparison of the incidence of rejection episodes between the two groups

	Adherent (n=239)	Nonadherent (n=170)	p-value
Rejection episodes n (%)	10(4.2)	21(12.4)	0.002
Type of rejection episodes
Cellular n (%)	8(3.3)	9(5.3)	0.331
Humoral n (%)	1(0.4)	8(4.7)	0.010
Combined n (%)	1(0.4)	4(2.4)	0.194

Table 6. Multiple logistic regression analysis of rejection episodes

Rejection episodes	Odds ratio	95% confidence interval	p-value
Post-transplant years	1.045	0.951-1.149	0.360
2 doses/d (Q12)	0.749	0.271-2.068	0.576
Nonadherence to immunosuppressives	2.967	1.248-7.058	0.014
IPV	1.030	1.001-1.060	0.044

No competing interests reported.

Appendixtable.docx

Profiles of Immunosuppressive Nonadherence and Association between Medication Adherence and Tacrolimus Intra-patient Variability Among Stable Kidney Transplant Recipients

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

1. INTRODUCTION

2. MATERIALS AND METHODS

3. RESULTS

4. DISCUSSION

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1