Assessment and Treatment of Nonadherence in Transplant Recipients

Shneider, Caitlin; Dunphy, Claire; Shemesh, Eyal; Annunziato, Rachel A.

doi:10.1016/j.gtc.2018.07.015

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…The MLVI represents the standard deviation of all measured tacrolimus concentrations (i.e., √s 2 ). The CV is calculated by multiplying each patient’s MLVI by 100, then dividing the product by the mean tacrolimus concentration, i.e., (100*MLVI) /x̄, thus allowing comparisons between patients with different adherence target levels ( Shneider et al, 2018 ). The calculation of the time-weighted average differed from the non-time-weighted average in that, to determine it, each assay value (x i ) was multiplied by the time of exposure (t i ), i.e., half the time interval between the measurement and the value preceding it, plus half the time interval after the measurement.…”

Section: Methodsmentioning

confidence: 99%

Comparison of different methods to assess tacrolimus concentration intra-patient variability as potential marker of medication non-adherence

et al. 2022

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Background and objective: Non-adherence to tacrolimus commonly manifests as low drug concentrations and/or high intra-patient variability (IPV) of concentrations across multiple measurements. We aimed to compare several methods of tacrolimus IPV calculation and evaluate how well each reflects blood concentration variation due to medication non-adherence in kidney transplant recipients.Methods: This Czech single-center retrospective longitudinal study was conducted in 2019. All outpatients ≥18 years of age, ≥3 months post-transplant, and on tacrolimus-based regimens were approached. After collecting seven consecutive tacrolimus concentrations we asked participating patients to self-report adherence to immunosuppressants (BAASIS© scale). The IPV of tacrolimus was calculated as the medication level variability index (MLVI), the coefficient of variation (CV), the time-weighted CV, and via nonlinearly modeled dose-corrected trough levels. These patient-level variables were analyzed using regression analysis. Detected nonlinearities in the dose-response curve were controlled for by adding tacrolimus dosing and its higher-order terms as covariates, along with self-reported medication adherence levels.Results: Of 243 patients using tacrolimus, 42% (n = 102) reported medication non-adherence. Non-adherence was associated with higher CVs, higher time-weighted CVs, and lower dose-corrected nonlinearly modeled trough levels; however, it was not associated with MLVIs. All of the significant operationalizations suggested a weak association that was similar across the applied methods.Discussion and conclusion: Implementation non-adherence was reflected by higher CV or time-weighted CV and by lower blood concentrations of tacrolimus. As an additional tool for identifying patients at risk for non-adherence, simple IPV calculations incorporated into medical records should be considered in everyday clinical practice.

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Section: Methodsmentioning

confidence: 99%

Comparison of different methods to assess tacrolimus concentration intra-patient variability as potential marker of medication non-adherence

et al. 2022

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“…Moreover, the financial cost of INA increases more than $12,000 US dollars per patient over a 3-year time period 15. Despite the clinical impact of INA, there is no effective approach to improving immunosuppressant adherence (IA) in solid organ recipients 16…”

Section: Introductionmentioning

confidence: 99%

<p>Prevalence and Risk Factors of Immunosuppressant Nonadherence in Heart Transplant Recipients: A Single-Center Cross-Sectional Study</p>

Zhang¹,

Zhou²,

Nelson³

et al. 2019

PPA

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BackgroundImmunosuppressant nonadherence (INA) has been shown to affect outcomes after solid organ transplantation. The aim of the present study was to determine the prevalence of INA in heart transplant recipients and the associated risk factors of INA.MethodsAdult heart transplant recipients who firstly received heart transplantation (discharged for at least 3 months) were consecutively enrolled. Immunosuppressant adherence was assessed using the Basel Assessment of Adherence with Immunosuppressive Medication Scale (BAASIS). INA was categorized into five domains of contributing factors (socio-demographic factors, transplant-related factors, healthcare system access factors, post-transplant treatment-related factors, and patient-related psychosocial factors). These factors were compared between adherent and nonadherent patients. The risk factors of INA were investigated by logistic regression analysis.ResultsA total of 168 heart recipients were ultimately included. Among them, 69 (41.1%) recipients were revealed to be nonadherent. Logistic regression analysis indicated that INA was associated with monthly income<3000 Chinese Yuan (CNY) (OR, 3.11; 95% CI, 1.58–6.12; p=0.001), number of prescribed concomitant drugs (OR, 1.23; 95% CI, 1.12–1.50; p=0.003) and concerns about immunosuppressants (OR, 1.09; 95% CI, 1.01–1.18; p=0.031).ConclusionsHeart recipients had a high prevalence of INA. Lower income, greater number of prescribed concomitant drugs, and more concerns about immunosuppressants correlated most with timing nonadherence and taking nonadherence among heart recipients. These findings will be helpful to intervene on and prevent future INA of heart recipients.

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“…[4][5][6] Second, many transplant recipients travel extensively for their care, 7,8 and remote technology may reduce this burden. Third, immunosuppressant nonadherence remains the leading cause of preventable graft failure, [9][10][11][12][13][14][15] and frequent remote encounters could facilitate adherence. But, there are challenges.…”

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confidence: 99%

Remote intervention engagement and outcomes in the Clinical Trials in Organ Transplantation in Children consortium multisite trial

Duncan-Park

Dunphy

Becker

et al. 2021

American Journal of Transplantation

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Remote interventions are increasingly used in transplant medicine but have rarely been rigorously evaluated. We investigated a remote intervention targeting immunosuppressant management in pediatric lung transplant recipients. Patients were recruited from a larger multisite trial if they had a Medication Level Variability Index (MLVI) ≥2.0, indicating worrisome tacrolimus level fluctuation. The manualized intervention included three weekly phone calls and regular follow-up calls. A comparison group included patients who met enrollment criteria after the subprotocol ended.Outcomes were defined before the intent-to-treat analysis. Feasibility was defined as ≥50% of participants completing the weekly calls. MLVI was compared pre-and 180 days postenrollment and between intervention and comparison groups. Of 18 eligible patients, 15 enrolled. Seven additional patients served as the comparison.Seventy-five percent of participants completed ≥3 weekly calls; average time on protocol was 257.7 days. Average intervention group MLVI was significantly lower (indicating improved blood level stability) at 180 days postenrollment (2.9 ± 1.29) compared with pre-enrollment (4.6 ± 2.10), p = .02. At 180 days, MLVI decreased by 1.6 points in the intervention group but increased by 0.6 in the comparison group DUNCAN-PARK et Al. | 3113 AJT 1 | INTRODUC TI ON Remote communications (e.g., telephone, video, or text messaging) can improve access to medical services. 1 More recently, clinicians and institutions have turned to remote communication to minimize contagion during the COVID-19 pandemic. 2Remote contacts may have particular appeal in the long-term management of transplant recipients. First, reducing exposure to pathogens is especially important in immunosuppressed individuals, 3 and indeed, transplant programs have rapidly turned to telehealth in the context of COVID-19. [4][5][6] Second, many transplant recipients travel extensively for their care, 7,8 and remote technology may reduce this burden. Third, immunosuppressant nonadherence remains the leading cause of preventable graft failure, [9][10][11][12][13][14][15] and frequent remote encounters could facilitate adherence. But, there are challenges. Remote interventions may be less effective at communicating with patients than in-person encounters, confidentiality must be maintained, and some patients may not have the means or comfort with technology to participate. 16,17 Key components of a robust evaluation of telehealth interventions include assessing predefined outcomes in prospective multisite trials. 18 Yet, despite its promise, there is a dearth of such investigations into telehealth in pediatric populations. [19][20][21]

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Assessment and Treatment of Nonadherence in Transplant Recipients

Cited by 8 publications

References 67 publications

Comparison of different methods to assess tacrolimus concentration intra-patient variability as potential marker of medication non-adherence

Comparison of different methods to assess tacrolimus concentration intra-patient variability as potential marker of medication non-adherence

<p>Prevalence and Risk Factors of Immunosuppressant Nonadherence in Heart Transplant Recipients: A Single-Center Cross-Sectional Study</p>

Remote intervention engagement and outcomes in the Clinical Trials in Organ Transplantation in Children consortium multisite trial

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