Medication non-adherence after kidney transplantation: A critical appraisal and systematic review

Journal Pre-proof Medication non-adherence after kidney transplantation: A critical appraisal and systematic review

Sumit R.M. Gokoel, Kim B. Gombert-Handoko, Tom C. Zwart, Paul J.M. van der Boog, Dirk Jan A.R. Moes, Johan W. de Fijter PII:

S0955-470X(19)30049-7

DOI:

https://doi.org/10.1016/j.trre.2019.100511

Reference:

YTRRE 100511

To appear in:

Transplantation Reviews

Please cite this article as: S.R.M. Gokoel, K.B. Gombert-Handoko, T.C. Zwart, et al., Medication non-adherence after kidney transplantation: A critical appraisal and systematic review, Transplantation Reviews(2019), https://doi.org/10.1016/j.trre.2019.100511

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© 2019 Published by Elsevier.

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Medication non-adherence after kidney transplantation: a critical appraisal and systematic review Sumit R.M. Gokoel MD,1 Kim B. Gombert-Handoko PharmD PhD2 , Tom C. Zwart PharmD2 , Paul J.M. van der Boog, MD PhD1 , Dirk Jan A.R. Moes PharmD PhD2 , Johan W. de Fijter MD PhD1 1. Leiden University Medical Center / Department of Medicine / Division of Nephrology, Leiden, The Netherlands

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Address for correspondence: Albinusdreef 2 2333 ZA Leiden, The Netherlands Division of Nephrology, C7-36 Phone number: +31 70 529 98 28 E-mail address: [email protected]

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2. Leiden University Medical Center / Department of Clinical Pharmacy & Toxicology, Leiden, The Netherlands

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Summary

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Medication non-adherence is one of the most important causes for shortened graft survival subsequently leading to a reduction in kidney graft survival results. Our aim was to provide an overview of its prevalence, risk factors, diagnostic methods and interventions to improve adherence in kidney transplant recipients. Therefore, we systematically searched the databases PubMed, COCHRANE Library, Web of Science and EMBASE for studies addressing “medication adherence”, “compliance”, “adherence”, “kidney transplantation” and “life style factors”. We identified 96 studies that satisfied our inclusion criteria. A problematic lack of a uniformly accepted definition for non-adherence was found, consequently leading to a wide range in non-adherence prevalence (36-55%). Using one uniformly accepted non-adherence definition should therefore be encouraged. A wide range in diagnostic methods makes it difficult to accurately detect non-adherence. Heterogeneous results of intervention studies make it difficult to select the best adherence enhancing method, challenging the battle against medication non-adherence. Literature suggests a combination of personalized interventions, based on patient-specific non-adherent behavior, to be most successful in improvement of adherence. High quality diagnostic methods and multidisciplinary, personalized interventions with focus on relevant clinical outcome are essential in overcoming medication non-adherence in kidney transplant recipients.

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Introduction

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Over the past decades improvements in graft survival have been made in the early-post transplantation phase, mostly due to improved immunosuppressive therapy and surgical techniques.(1-3) However, long-term results have not kept pace with these advances.(2, 4) Longterm kidney graft function is affected by suboptimal exposure to immunosuppressive medication, lifestyle-associated factors, recurrence of native kidney disease and medication non-adherence.(5, 6) All these factors contribute to premature graft loss, leading to return of patients to the renal transplant waiting list. Medication non-adherence by patients tends to increase over time(7, 8), but it is of key importance in long-term kidney graft survival. One meta-analysis illustrated a sevenfold increased odds of graft failure in non-adherent patients versus adherent patients.(9)

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The aim of this critical appraisal is to explore what challenges have to be overcome in the field of medication non-adherence in kidney transplant recipients. Non-adherence prevalence rates, risk factors, as well as diagnostic and interventional methods for non-adherence will be discussed. Recommendations for an optimal set of interventions are provided, aimed at maximizing medication adherence to prolong long-term kidney graft survival.

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Materials and methods A literature search of PubMed, COCHRANE Library, Web of Science and EMBASE electronic databases was performed from the year 2000 until present. The main search terms were ‘medication adherence’, ‘compliance’, ‘adherence’, ‘kidney transplantation’ and ‘life style factors’. The specific strings used for the four search engines are provided as supplementary material. Titles were screened by SG, followed by screening of the abstract and full text focussed on the adult kidney transplant population. Studies regarding the following were excluded: focus on paediatric population, dialysis patients, psychology only, lifestyle factors only, commentary letters, donor screening criteria related, surgery related, histopathology and/or serology related, opportunistic infections post‐transplant, cardiovascular focus only, case reports, dermatology related, legal aspects regarding donation/transplantation, focus on one specific race or country, statistical unclarity, presentation of a study protocol and/or still ongoing study. From each included study we explored the following subjects: (i) definition of non-adherence, (ii) prevalence of nonadherence, (iii) risk factors for non-adherence, (iv) diagnostic methods for tracing nonadherence, (v) adherence enhancing interventions.

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Results We identified 891 studies, of which 96 were included in this review. The flow diagram of electronic database searches is provided in Figure 1.

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Definition of non-adherence In current practice, 'adherence' is preferred to the previous term 'compliance' as it suggests a therapeutic alliance or contract between patient and physician, in which the patient plays an active role.(10, 11) The World Health Organization (WHO) has defined medication adherence as "the extent to which a person’s behaviour (taking medications, following a recommended diet and/or executing lifestyle changes) corresponds with the agreed recommendations of a health care provider.(10) The Kidney Disease: Improving Global Outcomes (KDIGO) Transplant Work Group has refined this definition, stating that medication non-adherence means "deviation from the prescribed medication regimen sufficient to adversely influence the regimen’s intended effect”.(12) In daily practice, non-adherence is often interpreted in different ways regarding its underlying cause(s) and quantification. Regarding its causes, a subdivision can be made into intentional and non-intentional nonadherence.(13) Intentional non-adherence means not taking prescribed medication deliberately and also not adhering to lifestyle advice as recommended by a healthcare provider, whereas non-intentional non-adherence means non-deliberate non-adherence. One author proposed another subdivision: ‘major non-compliance’ (leading to rejection and graft loss as a consequence) versus ‘subclinical non-compliance’ (not leading to rejection or graft loss).(14) However, this subdivision does not provide a clear difference in reasons for non-adherence, which makes it difficult to tailor personalized interventions for each patient.

Journal Pre-proof Non-adherence can be quantitatively assessed with the percentage of medication intake days (taking adherence), the percentage of correct inter-dose intervals (timing adherence) and the number of drug holidays.(15, 16) Amendments to these factors can result in varying definitions of non-adherence, without changing the essence of the WHO definition. However, different definitions make it difficult to compare non-adherence between studies.

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Prevalence of non-adherence KDIGO reported medication non-adherence to be as high as 50% in developed countries, whilst non-adherence in developing countries might even be higher.(12) Review of studies from the year 2000 up to present, as reported in Table 1, showed a prevalence rate ranging from 0.6 to 93.7%.(7, 8, 13, 14, 17-54) This wide prevalence range can most likely be explained by the use of different adherence definitions (ranging from clear defined to less clear defined, such as “less than excellent adherence”, but also lack of subdivision into intentional versus non-intentional non-adherence) and by use of different diagnostic methods, which probably have varying specificity and sensitivity. Based on the studies presented in Table 1, an accurate estimation of non-adherence prevalence in kidney transplant recipients seems difficult to make.

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Risk factors for non-adherence The WHO has defined five main categories of risk factors which can influence adherence behaviour: patient-related, therapy-related, disease-related, healthcare organizational and socioeconomic.(10) Psychological and personality characteristics, age and sex are examples of patient-related factors. Therapy-related factors are for example patient perceived barriers with regard to medication intake, such as forgetting to refill prescriptions, too many pills, too many doses, side effects and the obligation to lifelong adherence.(10, 12, 34, 55, 56) Discrepancies are seen in risk factors “age” and “time post-transplant”: most studies suggest that younger age(10-12, 27, 43, 49, 56, 57) and longer time post-transplantation(7, 8, 11, 22, 27, 51, 56, 57) are risk factors for non-adherence, but two other studies illustrated more non-adherence in older patients(8, 22) and clinical transplant guidelines by KDIGO mention that more non-adherence is seen in the early post-transplantation phase.(12) However, one study did not find a significant difference in time post-transplantation.(14) Examples of disease-related, socioeconomic and healthcare organizational factors are presented in Table 2. Comparable risk factor categorization has been applied by KDIGO and other authors.(11, 12, 14, 23, 27, 56) Furthermore, risk factors for non-adherence can also be divided into modifiable and nonmodifiable risk factors, which are of key importance when trying to resolve nonadherence.(22) For example, in patient-related factors, age and sex are non-modifiable. In contrast, the behaviour of patients with negative views on the use of immunosuppressive medication can be modified by providing education on the need for such medication and medication-taking self-efficacy.(21, 22) Diagnostic methods for tracing non-adherence Several diagnostic methods for tracing non-adherence are described in literature.(10, 58) The WHO categorizes these methods as either ‘subjective’ or ‘objective’ measures(10), while others prefer the terms ‘direct’ and ‘indirect’.(11, 47, 56) For clarity, the WHO categorization is

Journal Pre-proof applied throughout this manuscript. Various diagnostic methods and their accuracy are presented in Table 3. Overall, a relatively low number of studies in the field of renal transplantation on diagnostic methods accuracy was identified.

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Objective methods Electronic monitoring (EM) is a method which uses a microprocessor that records time and date of medication use. It is usually embedded in the medication container but new technologies have paved the way for electronic blistering.(58, 59) In theory EM is highly accurate in recording medication use and detecting patterns of medication intake, making it more precise than biochemical measures, analysis of pill counts/pharmacy refill records and subjective methods. Some authors consider EM to be the gold standard.(11, 15, 47, 56, 60) However, usability issues and contrasting reliability of this technique have been reported in literature.(11, 56, 58) De Bleser et al(61) compared two different EM devices in a laboratory setting for three weeks, mimicking twice daily use of immunosuppressive medication. For unknown reasons there was a discrepancy in perfect functioning (that is, total absence of missing registration and/or over-registration) between the devices. To the best of our knowledge, accuracy of other available devices (e.g. eCap, Wisepill, Medsignals) has not yet been investigated.(59) Moreover, EM also shows other disadvantages: true intake is not guaranteed, as it only records opening of the medication container. Consequently, incorrect use of EM could lead to falsely categorizing patients as non-adherent.(58) Patients may also complain about the size and appearance of the container.(58, 59) This may lead to non-usage of EM devices, falsely categorizing patients as non-adherent.

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In direct observed therapy (DOT), observation of medication ingestion by a patient is directly supervised by trained medical personnel.(56) A discrepancy appears to be present in the categorization of DOT: it can be seen as a diagnostic measurement method for nonadherence, as well as an adherence-enhancing intervention, because sitting face to face with the patient guarantees one hundred percent adherence. Disadvantages are its time consuming and costly nature and its non-practicality in certain patient groups. No studies evaluating the diagnostic accuracy of DOT were identified during literature search. Wireless observed therapy (WOT) utilizes an Ingestible Sensor System (ISS), which is embedded in pills or capsules, and registers medication intake when it comes into contact with gastric fluid. This theoretically leads to highly reliable measurements of intake and timing of intake of drugs. Disadvantages of ISS are its high costs, possible dermal and or gastro-intestinal discomfort and the fact that some patients felt anxious with this type of constant surveillance.(30) Therefore, more studies are needed to consistently prove diagnostic accuracy and clinical feasibility of WOT. Biochemical measurements, such as drug and metabolite concentrations, can be obtained rather easily but often do not provide an unequivocal measure of non-adherence. Drug concentrations fluctuate as a result of other factors influencing pharmacokinetics such as drug-drug and drug-food interactions, which may disturb their diagnostic reliability. Also, 'white coat adherence', in which patients purposely take their medication correctly only several days prior to drug concentration measurements, may occur and mask non-

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adherence.(16, 56, 58) Nonetheless, the percentage of subtherapeutic trough levels as marker for non-adherence may predict a higher chance of graft rejection. (62) Another marker for nonadherence in theory might be the parent-metabolite ratio of immunosuppressive drugs, with low parent levels together with normal metabolite levels suggesting non-adherence.(63) However, in daily clinical practice metabolites are often not measured, but patients that are suspected of non-adherence could be tested. Abbreviated area under the curve (AUC) monitoring can provide insight in the pharmacokinetic profile (64): a normal trough level with low peak and low AUC fits malabsorption, while a low trough level with high peak and normal clearance fits “white coat adherence” because pharmacokinetic levels have not reached a steady-state yet.(65) Since AUC monitoring might be laborious for patient and clinic, capillary dried blood spot technology may be used for at home AUC monitoring.(66) Finally, evidence shows that high intra-patient variability (IPV) in immunosuppressive trough levels is associated with a higher rate of graft rejection.(67-71) However, in contrast to IPV and rejection, the relationship between IPV and non-adherence is not clearly established yet(62), because IPV can also be affected by diarrhoea, gastro-intestinal metabolism and motility, drug-food and drug-drug interactions, genetic factors and immunoassay variability.(67, 70) One study illustrated that non-adherence was associated with lower tacrolimus levels and worse kidney graft function(38) and another study(71) illustrated significantly higher IPV in patients with low tacrolimus drug levels (<5ng/ml) in comparison with IPV in patients with higher tacrolimus levels (p=0.004), suggesting that non-adherence may play a significant role in higher IPV.

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Two studies evaluated the diagnostic accuracy of blood assays, reporting ranges of specificity of 17-80% and sensitivity of 30-83.3%, respectively.(18, 47) These ranges can be explained by differences in adherence definitions, as can be seen in Table 3.

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Pill count represents the number of apparent dosage units taken between two clinic visits compared to the total number of units received by the patient and provides an adherence ratio. Adherence underestimation can occur, because the dispense date is used as the denominator for the pill count calculation without considering the possibility of surplus medication. Furthermore, pill counts do not reveal an intake pattern. It is important to keep in mind that possession of medication does not necessarily mean complete adherence.(56, 58) Advantages of this methodology are its low costs and simplicity. Pharmacy refill records can provide insight in the balance between the number of prescribed refills and the number of refills actually received by the patient.(58) Discrepancies in this balance can be measured and evaluated. The main advantage of this strategy is that data is readily available in pharmacy information systems. Disadvantages are the assumption that prescription refill patterns correspond to medication adherence behaviour and that it relies on correct refill data input. Subjective methods These include self-reporting via questionnaires or interviews and healthcare provider assessment of patient adherence.(58, 72) Advantages of these methods are their low costs and ease of use in clinical practice. Disadvantages are possible underreporting of non-adherence

Journal Pre-proof due to recall bias and that its results depend on the applied adherence definition, as presented in Table 3.(11, 56, 58) Diagnostic accuracy of some self-report questionnaires was assessed in three different studies, reporting ranges of specificity of 23.3-57.1% and sensitivity of 6890%.(18, 26, 47) Answering a local self-report questionnaire during a confidential interview rendered 85.7% sensitivity, with a specificity range of 37.1-72.5%.(18) Two studies evaluated diagnostic accuracy of clinician rating on non-adherence, reporting ranges of specificity of 15-100% and sensitivity of 33.3-93.1%, depending on the applied cut-off for nonadherence.(18, 47) Independent reviewer rating on non-adherence scored 100% sensitivity, but specificity was similar to clinician rating.(18) In conclusion, a vast array of diagnostic methods for detection of non-adherence is available, with objective measures suggesting a higher accuracy than subjective measurement methods.

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Adherence enhancing interventions Many adherence enhancing interventions are available, aimed at the different categories of risk factors. Thirty-one studies were identified that evaluated the effect of such interventions, presented in Table 4.

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Clinical pharmacy services These generally comprise of direct patient care by a clinical pharmacist with focus on improvement of medication adherence. Clinical pharmacy services are usually combined with patient education and counselling services. Four out of six identified studies have illustrated a significant overall adherence enhancement. (73-76) Chisholm et al(73) also detected a significantly lower number of graft rejections. Three studies were randomized controlled trials (RCT)(74, 76, 77) , of which two illustrated a significant adherence enhancement in the intervention group, but no difference in transplant outcomes. (74, 76) Bessa et al(77) did not illustrate a significant improvement in self-reported adherence, nor a reduction in intrapatient variability in tacrolimus trough levels as another marker for adherence, with their pharmaceutical care intervention. The effect on adherence was unclear in the non-randomized controlled trial by Hlubocky et al.(78) Reduction of pill burden This method can theoretically improve adherence since it lowers the pill burden for patients, specifically unintentional (forgetfulness) non-adherent behaviour could be affected. Three out of five identified trials, which all were pre-post comparison studies, illustrated significant higher adherence after pill burden reduction.(79-81) The observational study by Fellstrom et al(82) did not find a significant improvement in self-reported adherence, nor an improvement of tacrolimus trough levels as another marker for adherence. Kuypers et al(83) illustrated a higher patient implementation (day-by-day % of patients with correct regimen dosing), but patient persistence (% of patients who remained adherent to the regimen) was not significantly different. Two trials(79, 82) did not find significant changes in transplant outcome, but another trial(80) reported 12 patients with rejection of which five patients were not adherent based on self-report questionnaire (N=4 patients missing data).

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Educational-behavioural intervention This type of intervention usually consists of educational and/or behavioura l and/or social support during home visits, by telephone calls or online website, by setting up educationalbehavioural contracts with non-adherent patients. All nine identified trials(84-92) were RCT’s, of which five demonstrated a significant adherence enhancing effect. (84-86, 91, 92) Two of these five successful RCT’s also assessed the effect of educational-behavioural intervention on transplant outcomes(86, 91) , with one study illustrating lower mean creatinine and blood urea nitrogen levels in the intervention group.(91) However, more infections occurred in the intervention group of this RCT. The other seven RCT’s did not assess the effect of intervention on transplant outcomes. Three RCT’s pre-selected non-adherent patients for trial participation(87, 88, 91) , but only one found an improvement of adherence after educationalbehavioural intervention.(91)

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Psycho-educational intervention This type of intervention usually consists of a multidisciplinary team providing patients education, assessing barriers to adherence and enabling greater competence in daily life, thus potentially increasing medication adherence. Three identified RCT’s all demonstrated a significant increase in adherence.(93-95) However, kidney graft survival measured after ten years was significantly lower in the intervention group in one RCT. (93) Another multi-centre RCT illustrated higher odds for 100% taking and timing adherence in the intervention group, but no differences in transplant outcomes were found. (95) Cukor et al(94) pre-selected nonadherent patients for trial participation and improved adherence in the intervention group. However, no difference was found in mean tacrolimus trough levels as another marker for adherence and transplant outcomes were not assessed.

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Medication reminder intervention These include interventions to remind patients to take their medication. Three out of four identified trials illustrated a significant adherence improvement in the intervention group and all these three studies were RCT’s.(96-98) However, adherence measured as a coefficient of variation in tacrolimus trough levels and mean tacrolimus trough levels measured in two of these RCT’s did not differ.(97, 98) The other successful RCT was the only to include preselected non-adherent patients and also demonstrated a significant better systolic and diastolic blood pressure in the intervention group. (96) Sustainability of the reminder intervention from this trial was assessed 12 months post-study enrolment, by retrospectively analysing patient chart documented blood pressure. (99) Again, systolic blood pressure was significantly lower in the intervention group. Only one study reported transplant outcomes, demonstrating three times more biopsy-verified rejection in the control group (N=13 out of 40; univariate analysis P=0.019, multivariate analysis P=0.054). (98) Torabi et al(100) did not find a significant adherence difference three months post-implementation of their smartphone reminder application. Remote monitoring This type of intervention consists of real-time video consultations enabling remote monitoring of outpatient clinics. Schmid et al(101) illustrated a significant higher composite adherence score in the intervention group after 12 months follow up. A reduction of

Journal Pre-proof unplanned hospital admissions and shorter in-hospital durations was seen in the intervention group, but overall no significant differences in transplant outcomes were found. Free of charge immunosuppressive medication In some countries immunosuppressive medication is not fully reimbursed, this could potentially cause non-adherence in patients with financial problems. Chisholm et al(102, 103) conducted two trials to evaluate the effect of free of charge immunosuppression. Neither study showed a significant improvement in medication adherence after 12 months (remaining adherence in both studies 48% and 42%, respectively).

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Discussion There are a number of problems that must be solved to overcome non-adherence; first of all the lack of a uniformly accepted definition makes it difficult to assess the magnitude of nonadherence, as illustrated by a wide range in prevalence range (1-90%).(7, 8, 13, 14, 17-53) Definitions varied widely between studies, especially with regard to taking adherence, timing adherence and drug holidays, factors which quantify the non-adherence definition.(15, 16) However, one meta-analysis of 147 studies revealed a non-adherence prevalence of approximately 36% in kidney transplant recipients, which was higher compared to nonadherence in other solid organ transplant recipients (range 7-15%).(28) Studies in kidney transplant recipients which reported composite non-adherence prevalence rates, measured by combining several diagnostic methods, reported 38-55% non-adherence prevalence.(7, 14, 36, 47) So, perhaps the wide non-adherence prevalence range of 1-90% might be narrowed down to 36-55%. Furthermore, it is essential to link a non-adherence definition to clinically significant outcomes, such as rejection and kidney graft survival.(104) Recently, the Non-Adherence Academic Research Consortium and European Society for Patient Adherence, Compliance and Persistence committees have published recommendations to standardize the methodological approach and scientific reporting of non-adherence for future clinical trials.(105, 106) These guidelines show that there is a need for uniformity in the field, which corresponds with our viewpoints illustrated in this review. To reduce the wide variety in heterogeneous study designs and results, and to improve uniformity in daily clinical practice, we recommend combining the definition for non-adherence as stated by KDIGO with the following: – Taking adherence: preferably daily medication intake to ensure adequate steady-state pharmacokinetics and therefore long term improvement of graft survival.(70) However, from a clinical point of view it might be expected that forgetting intake on sporadic occasions, for example once in a few months, will not lead to significant changes that result in concentrations below the therapeutic window when in steady-state pharmacokinetics. The relationship between the frequency of taking non-adherence and magnitude renal function deterioration is not yet clear and warrants thorough investigation. Thereafter, risk categories of non-adherence can be established. – Timing adherence: intake < 4 hours for immediate-release immunosuppressive medication or intake < 14 hours for prolonged-release variants based on manufacturers guidelines.(107)

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A second obstacle is identification of non-adherent patients: even though a vast array of diagnostic methods for detection of non-adherence is available, none have both a high specificity and sensitivity.(18, 26, 47) While some authors consider EM as the current gold standard, daily practical issues might lead to falsely categorizing patients as non-adherent. (11, 15, 47, 56, 60) This may be confirmed by a recently published RCT which assessed educationalbehavioural intervention illustrating 42% underutilisation of electronic monitoring devices by all participants as marker for adherence, versus 13% underutilisation of a self-report questionnaire on adherence (BAASIS). (92) Another trial even illustrated that the use of different EM devices show different non-adherence levels.(61) Some patients may experience anxiety or stress, due to the feeling of being under continuous surveillance. In addition, some immunosuppressive drug formulations such as tacrolimus need to remain stored inside the blister until actual ingestion to guarantee adequate physical, chemical and microbiological stability. The recently introduced pill-embedded Ingestible Sensor System may be an interesting option, but high costs, practical issues and questionable patient comfort need to be resolved before widespread clinical implementation of this device is advisable.(30) Most likely a combination of diagnostic methods should be used, which give higher accuracy without too much intrusion in patients’ daily life.(104, 108) The “Consensus Conference” on non-adherence recommended the use of a composite adherence score.(109) Schäfer-Keller et al(47) demonstrated a sensitivity of 72% and specificity of 45% for a composite adherence score consisting of self-report, collateral report and blood levels. Regarding blood levels several methods are available to use as a marker for non-adherence, such as the percentage of subtherapeutic trough levels and IPV of trough levels. Both seem correlated with a higher chance of kidney graft rejection(38, 71), which is also seen in other solid organ transplants. (110, 111) These methods may be feasible to implement as a screening tool for non-adherence into daily clinical practice.(62, 112) Pharmacokinetic profile monitoring (AUC) may detect “white coat adherence” and provide insight in absorption profile better than measurement of trough levels alone.(64, 65) Use of dried blood spot technology may provide more patient comfort due to the possibility of at home AUC monitoring.(66) We think that a composite adherence score consisting with one of these blood level methods, together with self-report and physician report might aid optimally in pre-selection of true non-adherent patients in daily clinical practice. However, more studies are needed to confirm this. Enhancement of medication adherence, by means of a multidisciplinary approach, warrants an improvement of long term kidney graft survival results.(9, 14, 40) Physicians play a central role, but nurse practitioners can be of useful support during assessment of non-adherent patients at busy outpatient clinics.(113) They can take an advisory role towards physicians and pharmacists regarding what personalized intervention(s) would be best for the patient, based on patient-specific risk factors for non-adherence. They can also provide follow-up support of non-adherent patients. An active patient participation should also be encouraged to promote patient-tailored care.(114) Interventions should be repeated to guarantee a long-term adherence enhancing effect.(40, 87, 100) However, a final obstacle is selecting the most optimal intervention to reduce medication non-adherence in a patient-tailored way. Due to heterogeneity in trial designs, patient sample

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size, adherence diagnostic methods, adherence definitions and follow up periods, a wide range in results is seen across several intervention studies, complicating selection of the best intervention. The performance of a meta-analysis on the effectivity of the different interventions is also hindered by the heterogeneity in trials. Furthermore, the “streetlight effect”(108) can be observed in most intervention trials: by means of convenience sampling the creation of selection bias possibly missing a significant intervention effect, because only the most motivated and already adherent tend to participate in trials. Several trials also only focussed on enhancement of adherence behaviour, because clinical transplant outcomes were not always assessed, which is consistent with the observations by Lieber et al.(104) Of the intervention trials which assessed effect on adherence and clinical transplant outcome, only two trials demonstrated an improvement of both parameters. (73, 91) Two other RCT’s which assessed effect on adherence and clinical transplant outcome demonstrated improved adherence due to EM with reminder function(98) and psycho-educational intervention(93) respectively. However, in the psycho-education study there was significant more kidney graft loss in the intervention group, as measured ten years post-study inclusion suggesting that periodically repeating an intervention may be necessary to improve long term adherence and kidney graft results.(93) One RCT with educational-behavioural intervention and one nonrandomized controlled trial with smartphone medication reminder intervention demonstrated an “inclusion effect” during the study phase: adherence increased early post-intervention implementation, but decreased reaching study end, confirming that repeat of intervention is necessary.(87, 100) The RCT by Reese et al(97) demonstrated better sustainment of adherence in the EM with reminder plus provider notification group, in comparison with the EM with reminder only group and the control group, indicating that electronic reminder alone may not be enough to sustain high adherence. Another point of notice is that only five trials(87, 88, 91, 94, 96) tried to reduce the “streetlight effect” by pre-selecting non-adherent patients for trial participation, of which three trials(91, 94, 96) demonstrated significant improvement of adherence, but clinical transplant outcomes were assessed only in one of these three trials.(91). Most prospective intervention trials used convenience sampling as recruitment method, possibly explaining the lack of a significant adherence improvement. When adherence did significantly improve, no improvement in clinical transplant outcome was found.(75, 76, 79, 81, 86, 95, 101) Some interventions may not always be successful, such as free of charge immunosuppressive medication.(102, 103) In countries where immunosuppressive medication is fully reimbursed this type of intervention most likely will not be successful, but in countries where there is no full reimbursement for this medication it might reduce “medication tradeoff” (meaning the choice to spend money on other basic necessities than immunosuppressive medication).(48) Reduction of pill burden might be successful in enhancing adherence, because three out of five identified intervention trials were effective.(79-81 ) However, in one trial the follow up period was only three weeks, possibly missing an “inclusion effect”. (79) Nonetheless, the French observational PREDICT study confirmed significant higher adherence in the group with fewer immunosuppressive drugs, indicating that reduction of pill burden might improve adherence.(29) Despite the difficulties in selecting the most optimal intervention, we think that a personalized combination of clinical pharmacy services, medication intake reminders, reduction of pill burden and educational intervention may be feasible, because most of these

Journal Pre-proof interventions improved adherence. After detection of true non-adherent patients, it would be best to assess the reason(s) for non-adherence first and then tailor patient-specific interventions.(22) The need for specific behavioural and/or psychological intervention perhaps could be based on patient-specific risk factors for non-adherence. As mentioned earlier, at busy outpatient clinics a multidisciplinary approach might be of valuable support in the battle against non-adherence.(9, 14, 40)

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Conclusion This critical appraisal has highlighted the challenges in tackling medication non-adherence in the adult renal transplant population. The lack of a uniformly accepted definition of nonadherence is the first challenge to overcome. Development and use of high quality diagnostic methods and interventions are other challenges to overcome. We recommend using a multidisciplinary approach (nurse practitioner, pharmacist and physician) in the assessment of patients with high risk for non-adherence. Subsequently, personalized interventions consisting of a combination of clinical pharmacy services, medication intake reminders, reduction of pill burden and educational intervention should be implemented to improve medication adherence effectively. Furthermore, the focus should be on increasing adherence in true non-adherent patients, which might improve long-term clinical transplant parameters.

Disclosures

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Gokoel performed the literature search and performed the initial data acquisition, data analysis and interpretation and manuscript writing. Gombert-Handoko, Zwart, van der Boog, Moes and De Fijter assisted in the additional data acquisition and analysis and reviewed and revised the manuscript.

The authors declare no conflicts of interest. Funding No funding sources have been used.

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Journal Pre-proof Table 1. Non-adherence prevalence rates

EM

(18)

Self-report (Morisky)

(18)

Self-report (MARS) Self-report (single item regarding late intake >2h)

Butler Butler

(18)

Butler

Clinician assessment *Missing medication *Late intake >2h

(18)

Butler

Interviewer (nonclinician) assessment *Missing medication *Late intake >2h

(18)

Butler

(19)

"Very rarely to very often" *"Very rarely to very often" *"Very rarely to very often" *"Very rarely to very often"

Chisholm-Burns

(21)

Chisholm-Burns

(22)

(23)

(24)

Cukor

Denhaerynck

(25)

*64.6% to 2.7%

*15.9% (TA) and 42.1% (TiA)

58.7%

*35%

Self-report (ITAS)

ITAS score < 12

21.4%

Self-report (ITAS)

ITAS score < 12

34.5%

Jo u

(20)

Cheng

*51.3% to 0.0% *45.3% to 0.7%

*TA: missing ≥ 1 dose + TiA: ≥ 2h late *≥1% non-adherence

rn

*VAS

45.7% to 1.3%

*31.3% to 9.5%

al

(8)

Burkhalter

*12% (95% CI 4-20%) *45% (95% CI 32-58%) 9%

*"Very rarely to very often" Pharmacy refill records Failure to retrieve (manually collected by medication ≥ 1 time in 1 secretary of state) year period *Self-report (BAASIS)

Couzi

8%

Pr

Brahm

Morisky score ≥ 2 MARS score < 24

12% (95% CI 4-20%) 26% (95% CI 15-37%)

f

(18)

Butler

oo

EM

Non-adherence prevalence rates

pr

(17)

Butler

Non-adherence definition Missing ≥ 20% of days of medication Missing ≥ 10% of days of medication *TA < 80% of days *TiA > 6 hours

e-

Diagnostic adherence method

Authors

Pharmacy refill records No cut-off reported

12.9% (95% CI 5.3520.45%) 17.3% (month 3) 24.1% (month 6) 30.7% (month 12) 34.6% (month 24)

Self-report (Morisky)

Morisky score > 0

Self-report (ITAS-M = Medication Therapy Adherence Scale)

ITAS score 10-11: "nearly perfect adherence" ITAS score ≤ 9: "less than perfect adherence"

Self-report (1 item Siegal questionnaire)

Missing no single dose in 13.2% (Europe) the last 4 weeks 19.3% (United States)

30% (ITAS 10-11) 19% (ITAS ≤ 9)

Journal Pre-proof *EM: TA > 25% missing doses in 3 months

*EM (3 months)

Denhaerynck

(26)

*Self-report (Siegal scale) *Blood assay

*Collateral report

(27)

*Blood assay: nonadherent when outside hospital's accepted therapeutic range

b

1.6% (taking nonadherence only)

a

*Any clinician scoring fair or poor Calculated weighted mean non-adherence prevalence over studies 27.7% that measured nonadherence by self-report (N=10)

Literature review

pr

(28)

Pr

Dew

Based on different definitions as used by authors of the 147 studies

e-

Meta-analysis of 147 studies of kidney (N=72), heart, liver, pancreas/kidneypancreas (N=6) and lung/heart-lung recipients between 1981 and 2005

oo

f

Denhaerynck

*Siegal score ≥ 1

*Self-report (compliance evaluation test questionnaire) (29)

al

Dharancy

Jo u

rn

*Physician report (VAS)

(30)

Eisenberger

(31)

Gaynor

Germani

(32)

(33)

Goldfarb

*Self-report Non-adherence: score ≥ 1 (maximum score 6)

ISS

Clinical history by physician and transplant coordinator Self-report (local 15-item questionnaire) Self-report with local questionnaire

*VAS: score < median = non-adherent

*TA: number of ISS detected divided by the number of prescribed pills during study period *TiA: number of ISS detected (<2h intake after prescribed time) divided by number of prescribed pills Patient acknowledgement of non-adherence; blood assay and graft failure Delayed or no intake or dosage modification TA: ≥1 day no intake TiA: "late" intake

36% (kidney transplant recipients) (7-15% in other transplant recipients) Overall non-adherence *68% (self-report) *53% (physician VAS) Kidney Tx group nonadherence: *73% (self-report) *53% (physician VAS) Liver Tx group nonadherence: *60% (self-report) *51% (physician VAS)

*0.6% (TA)

*15.5% (TA)

45% death-censored graft failures due to noncompliance 18% 21% (TA) 33% (TiA)

Journal Pre-proof

(34)

Gordon

Interview with either main author or research staff member

Questions on intentional and non-intentional nonadherence

*Self-report with c MARS

*MARS score < 24

*Serum immunosuppresive levels

*Achieving target = at least 2/3 measurements reaching clinical target

*25.4% (Achieving target)

Self-report (Medication Adherence Report Scale)

Score < 24 = nonadherent

*19.7% (overall nonadherence) *47.4% (non-intentional) *15.1% (intentional)

*51.4% (MARS-Total) 62.4% (MARS-Forget) 13.8% (MARS-Intent)

(13)

Griva

(35)

Overall non-adherence with BAASIS + clinician's score + Tac variability *BAASIS: (pooled group A and B) -TiA: > 1 time deviation > after one year follow up: 2h from prescribed time 38% in last 4 weeks -TA: missed one or more *Group A: doses in last 4 weeks -8 weeks post-Tx: BAASIS TiA 5% *Clinician's score: BAASIS TA 4% physisician/nurse scored Clinician's score 3% patients adherence as Tacrolimus variability 3% non-excellent -1 year post-Tx: (suboptimal or poor) BAASIS TiA 25% BAASIS TA 7% *Tacrolimus variability: a Clinician's score 7% coefficient of variability > Tacrolimus variability 13% 30% (at 8 weeks using six and at 1 year post-Tx *Group B (cross-sectional using three Taccohort 1 year post-Tx): concentrations) BAASIS TiA 18% BAASIS TA 5% Clinician's score 12% Tacrolimus variability 11%

(36)

*Clinician's score

al

Gustavsen

Pr

e-

*Self-report (BAASIS)

pr

oo

f

Griva

12%

Israni

Lalic

Jo u

rn

*Tacrolimus level variability

(37)

(38)

(39)

Lennerling

Massey

(40)

EM

>50% non-adherence 15-50% non-adherence <15% non-adherence

12% 20% 68%

Self-report with SMAQ questionnaire

Answering "no" at first question, "yes" at following three questions and missing ≥2 doses in 28.3% prior week and/or failed to take medication >2 days in last 3 months

Self-report with BAASIS

Score ≥ 1

54%

Self-report with BAASIS

Scoring an affirmative answer on the first 3 questions of part 1 of BAASIS = non-adherent

17% (6 weeks post-Tx) 27% (6 months post-Tx)

Journal Pre-proof

(41)

(42)

Ortega

EM

Monthly non-adherence rise ≥6.7% = nonadherent

*Abnormal immunosuppressant blood levels

*Abnormal according to hospital protocol

*31.1%

*Not specified

*29.1%

*Physician report

*49.5% (< 50th percentile) 26.5% (50-75th percentile)

f

oo

(43)

(14)

*Self-report (local 5point scale)

*Physician's report

*Self-report score < excellent is considered non-adherent *Physician's report: idem dito

Pr

Rosenberger

pr

Pinsky

*Year 1: <50th percentile = poor to fair adherence 50-75th percentile = Pharmacy refill records good adherence (MPR calculation) *Year 3: < "excellent" adherence (meaning "low" to "normal" adherence)

e-

Nevins

22.6% gained a ≥6.7% non-adherence rise at month 2 versus month 1 -> this group also displayed more non-adherence for months 6-12 (27% vs 8% for adherent group, p<0.0001)

*93.7% *54% (combined nonadherence score) *31.7% (self-report) *41% (physician's report)

0 = non-adherent

(44)

EM

Jo u

Russell

rn

al

0.25 = partial taking adherence (twice-daily medication), but not timing-adherent (>3 hours late, but intake <12 hours)

Russell

Russell

(45)

(46)

EM (older patients)

EM

0.50 = partial taking adherence and timingadherence (<3 hours)

24% (elderly patients with negative/neutral attitude towards EM) 30% (elderly patients with positive attitude towards EM) p=0.22

1 = adherent Counting cumulative electronic cap openings Same as previous study (Russell 2009)

86% 61% (non-adherence score > 0.1) 41% (non-adherence score > 0.2)

Journal Pre-proof *EM: <98% taking adherence and/or at least one drug holiday

*EM *Blood assay (trough levels of cyclosporine, tacrolimus, mycophenolat-mofetil) *Self-report (Siegal scale) *Clinician report

*Self-report: any reported non-adherence on any of the four items e

*Blood assay combined: 33% (cyclosporine 25.8%) (tacrolimus 35.1%) (mycophenolat-mofetil 40.2%) *Self-report: 12.4%

*CAS 1: see mentioned descriptions at selfreport and clinician report

*Clinician report: 24.9% *CAS combined: 38.9%

pr

*CAS 2 (self-report and/or clinician report and/or non-therapeutic blood assay levels)

*EM: 17.3%

f

*CAS 1 (self-report and/or clinician report)

*Clinician report : ≥1 clinician rating "poor" adherence

oo

(47)

Schäfer-Keller

*Different cut-off values applied for several immunosuppressive d medications

e-

*CAS 2: see mentioned descriptions at selfreport, clinician report and blood assay

Pr

Self-report of having missed a dose of any of immunosuppressive or chronic disease medication in the past 4 days

Self-report (Patient Medication Adherence Questionnaire)

(48)

Pharmacy refill records MPR < 100% = non(MPR calculation) adherent

(49)

(50)

Tielen

Tsapepas

(51)

Jo u

Spivey

rn

al

Serper

(52)

Weng

*medication non-trade-off group: 11% p < 0.01 56% (mean MPR score)

Self-report (4-item Siegal questionnaire)

All other answers than "never" indicate nonadherence

30.8%

Self-report (ITAS)

ITAS < 12

49.8%

*Missing one or more doses of either immunosuppressive or non-immunosuppressive medication

*55% overall nonadherence (self-report + trough levels)

*Self-report (local questionnaire)

Vasquez

Mean non-adherence: *medication trade-off group (choosing to spend money on other expenses than on medication): 23%

(7)

*Tacrolimus or cyclosporine trough levels

*Three successive cyclosporine trough levels < 50ng/mL or tacrolimus trough levels < 5 ng/ml

Electronic monitoring

Average daily nonadherence <50% Average daily nonadherence <80 % Average daily non-

Of the non-adherent group: -69% non-adherent based on self-report alone -15% non-adherent based on trough levels alone -15% non-adherent based on both 13.7% 26.6% 59%

Journal Pre-proof adherence <95 %

(53)

Weng

(54)

ITAS < 12

40.9%

Self-report (BAASIS)

One of the following = non-adherent -TA: ≥1 day no intake in last 4 weeks -DH: ≥2 doses of immunosuppressive medication skipped in a row in last 4 weeks -TiA: ≥ once a timing 42.7% deviation of > 2 hours behore or after prescribed dosing time -Reduction of dose: alteration of immunosuppressive medication dose in last 4 weeks without physician consultation

e-

pr

oo

f

Kobayashi

Self-report (ITAS)

Jo u

rn

al

Pr

BAASIS, Basel Assessment of Adherence to Immunosuppressive Medications Scale; CI, confidence interval; DH, drug holiday; CAS, composite adherence score; EM, electronic monitoring; ISS, Ingestible Sensor System; ITAS, Immunosuppressant Therapy Adherence Scale; MPR, Medication Possession Ratio; SMAQ, Simplified Medication Adherence Questionnaire; TA, taking adherence; TiA, timing adherence; VAS, visual analog scale a

Collateral report by 7 physicians, 4 nurses and two medical assistants (number of medical centers not reported) b Denhaerynck study: follow up duration five years, but measurement of adherence only once performed at study baseline c MARS: Medication Adherence Report Scale MARS-Total: total adherence score MARS-Intent: score for intentional non-adherence (not to be confused with other prevalence rates in the table, which are adherence rates) MARS-Forget: score for unintentional non-adherence (not to be confused with other prevalence rates in the table, which are adherence rates) d

Different cut-off values have been applied for several immunosuppresive medications: <52% variability in levels of ALL immunosuppresives

Journal Pre-proof <69% variability in levels of ALL immunosuppressives <29% non-therapeutic tacrolimus levels <29% supra-therapeutic mycophenolic acid levels <70% non-therapeutic cyclosporine levels e

Jo u

rn

al

Pr

e-

pr

oo

f

Schäfer-Keller study: clinician report performed by 7 physicians, 4 nurses and 2 medical assistants at two medical centers

Journal Pre-proof Table 2. Risk factors for non-adherence Patient related factors (8,10Age

Therapy related factors Drug schedule (10,12,55) complexity

12,22,27,43,49,55,56)

Disease related factors More (11,33,55 comorbidity

Socioeconomically related factors Poor socioeconomic (10,55) status

)

Drug side (10,12,14,27,54,55) effects More frequent dosing (10,11,27,29,54) (more pills)

(7,54,55)

Living kidney (11,27,33,49,56) donation

Recurrence of underlying renal (12) disease

al rn Jo u

Patient lack of knowledge about potential risks of (10,55) non-adherence

12,55,56)

(10,11,55

f

)

Full time (33) ≥ 1 reemployment (54) transplantation High cost of (10,54,55) Less HLA medication (56) mismatch

Pr

Low self(27,54) efficacy Negative treatment beliefs and (11,27,55) satisfaction

Dialysis prior to (54) transplantation

Longer time post(7,8,11,12,22,27,51,55,5 transplant

Previous treatment (55) failure

(10-

Education level

Unemployment

Lack of medication (7,53) knowledge

6)

Lack of adequate social (11,12,14,27,55) support

oo

Psychological factors: for example (8,11,27,54,56) depression (11,27) , anxiety , negative (11,12) personality , psychiatric (12,55) illness , (27) substance abuse

Pre-transplant non-adherence (12,27) behaviour

e-

(51,56)

Black race

Forgetfulness

Lower self-rated (14) health

pr

Sex (males more non(11,12,49,54) adherent)

Healthcare related factors Lack of time for adequate patient information at busy outpatient (10,54,55) clinics Absence of medical staff for consultation after forgetting (54) drug intake Lack of adequate healthcare insurance (10,27,55) system Lack of a multidisciplinar y approach towards non(10) adherence Lack of possibilities to actively involve patients in own (10) healthcare

Journal Pre-proof Table 3. Accuracy of diagnostic adherence measurement methods in the kidney transplant setting

Sensitivit y

Diagnostic measurement method

Butler(18)

Blood assay (drug level)

83.3%

Butler(18)

Blood assay (drug level)

66.7%

Blood assay (drug level)

e-

SchaferKeller(47)

pr

oo

f

Author

Pr

Clinician report** 1. Missed medication 2. Late intake

69% 80% b 70% c d 61% 78% e

100% 100%

33.3% 47.1%

rn Jo u

Clinician report***

57.9% (≥1) 35% (≥2) Schafer(47) Keller

SchaferKeller(47)

15% (≥3)

Composite adherence score (self-report and/or clinician report)

a

42% 30%b 58%c d 60% 40%e

al

Butler(18)

a

Adherenc Specificit e y definition Range between lowest and highest of last 6 cyclosporin e levels ≤ 27.7% 65 ng/ml Lowest of last 6 cyclosporin e levels ≥ 17.0% 150 ng/ml

62.8%

See description below* Never missing medication Never late intake (<2 hours) All clinicians rating "good" adherence = adherent patient

Cut-off for nonadherence: ≥1 clinician rating "poor" adherence ≥2 clinicians rating 55% (≥1) "poor" adherence 80% (≥2) ≥3 clinicians rating 93.1% "poor" (≥3) adherence See mentioned description s at selfreport and clinician 89.8% report

Journal Pre-proof

Composite adherence score (self-report and/or clinician report and/or non-therapeutic blood assay levels)

72.1%

45%

Butler(18)

Independent interviewer report 1. Missed medication 2. Late intake

100% 100%

33.3% 47.1%

Butler(18)

Self-report (Morisky questionnaire)

57.1%

68%

Butler(18)

Self-report 1. MARS questionnaire 2. Single item regarding late intake

57.1% 42.9%

70.6% 68.6%

25%

90%

23.3%

89.8%

85.7% 85.7%

37.1% 72.5%

Denhaerynck(2

oo

Self-report (Siegal Scale)

pr

SchaferKeller(47)

f

SchaferKeller(47)

Self-report (Siegal Scale)

e-

6)

Pr

Local self-report at confidential interview 1. Missed medication 2. Late intake

al

Butler(18)

See mentioned description s at selfreport and clinician report Never missing medication Never late intake (<2 hours) Morisky score = 0 MARS score = 25 Never late intake (2< hours) Scoring 0 on the 4 assessed items Scoring 0 on the 4 assessed items Never missing medication Never late intake (<2 hours)

*Different cut-off values have been applied for several immunosuppresive medications:

rn

a. <52% variability in levels of ALL immunosuppresives

b. <69% variability in levels of ALL immunosuppressives

Jo u

c. <29% non-therapeutic tacrolimus levels

d. <29% supra-therapeutic mycophenolic acid levels e. <70% non-therapeutic cyclosporine levels

**Butler: clinician report by one nephrologist ***Schäfer-Keller: clinician report performed by 7 physicians, 4 nurses and 2 medical assistants at two medical centers All clinicians score "good" adherence = score 0 = adherent patient

Notification: both Butler and Schäfer-Keller used electronic monitoring as a reference standard For adherence prevalence rates and definitions used for adherence: see table 1

MARS, Medication Adherence Rating Scale MPR, Medication Possession Ratio

Journal Pre-proof

Auth or

Interve ntion

Trial Total desig numb n er of patie nts (inter venti on group )

Follo w-up durat ion

End poi nt

Diagnos tic method

Adhere Post Post nce follo follo definiti w-up w-up on result resul s ts inter contr venti ol on grou grou p p

Pvalue

Repo rt of clinic al outc omes ?

Chishol m (73)

Cl i nical pha rmac y s ervi ces

Prepos t comp a ri son cohor t s tudy

1 yea r retros pecti v e a nd 1 yea r pros p ecti ve

Medi ca ti o n a dhe renc e

Serum i mmunos uppressan t concentra ti ons

Cycl os po ri ne ta rget ra nge 100-400 ng/mL

p=0.0 07 (chang e in cycl os porine l evels)

e-

Table 4. Intervention studies

Ta crol im us ta rget ra nge 517 ng/mL

Yes , s i gnific a ntl y l ess gra ft rejecti ons a nd i mpro ved ca rdi o va s cul air pa ra m eters in i nterv ention group (p<0.0 1) No

Cl i nical pha rmac y s ervi ces

RCT

Ts chi d a (75)

Cl i nical pha rmac y s ervi ces

Retros pecti v e ma tch ed ca s econtr ol s tudy

N=24 (N=12)

1 yea r

N=103 8 (N=519 )

1 yea r (retros pecti v e)

Jo u

Chishol m (74)

rn

Medi ca ti o n a dhe renc e Medi ca ti o n a dhe renc e

Cycl os porine preenroll ment: mea n 178.77 ng/mL ±61.4

Ta croli mus pos tenroll ment: mea n 10.17 ng/mL ±1.17

Ta croli mus preenroll ment: mea n 8.67 ng/ml ±3.5

f

Cycl os pori ne pos tenroll ment: mea n 214.7 ng/mL ±44.14

oo

pr

al

Pr

N=36

p=0.3 43 (chang e in ta croli mus l evels)

Pha rma cy refi ll records

Monthl y mea sure d a dheren ce > 80%

96.1% ± 4.7%

81.6% ± 11.5%

<0.00 1

5 di fferent methods: s ee decri ption bel ow*

Onl y MG & DC s pecified *

18.67 (pres ci pti ons) 0.87 (MPR) 29 (MG) 39 (DC) 65 (ei ther MG or DC)

17.90 (presci pti ons ) 0.83 (MPR) 53 (MG) 104 (DC) 142 (either MG or DC)

p<0.0 5 (pres c ri ption s) p<0.0 001 (MPR) p=0.0 06 (MG) p<0.0 001 (DC) p<0.0 001 (either MG or DC)

Yes , but no s i gnific a nt di ffere nces i l lustra ted

Journal Pre-proof Joos t(76

Cl i nical pha rmac y s ervi ces

RCT

N=67 (N=35)

1 yea r

Medi ca ti o n a dhe renc e

El ectronic moni torin g Pi l l count Mori s ky questionn a i re Loca l selfreport

EM: -DA ≥80% -TA ≥90 a nd ≤110% -Ti A (wi thin 6h i nterval a round s ta ndard i nta ke ti me): ≥80% -DH (>48h no i nta ke): 0

91% (DA; 95% CI 90.5291.94)

75% (DA; 95% CI 74.5776.09)

95% (TA)

82% (TA)

95% (Ti A; 95% CI 92.497.8%)

94% (Ti A; 95% CI 91.295.6% )

81% (no DH)

oo

f

)

Bes sa(7 7)

Hl uboc ky(78)

Cl i nical pha rmac y s ervi ces

Cl i nical pha rmac y s ervi ces

Jo u

rn

al

Pr

e-

pr

PC: ≥90 a nd ≤110%

RCT

Contr ol l ed, nonra ndo mi zed

N=128 (N=64)

N=290

90 da ys

1 yea r (retros pecti v e)

Medi ca ti o n a dhe renc e

Medi ca ti o n a dhe renc e

CV (%) i n ta crol imu s trough l evels

Mori s ky: a nswerin g all 4 question s wi th "no" Loca l s elfreport: no mi s sed i nta ke duri ng l a st 2 weeks CV: no cut-off s pecified

97% (PC) 74% (Mori s ky) 88% (l ocal s elfreport)

CV 31.4 ± 12.3%

Sel freport (BAASIS)

BAASIS: all 4 question s a ns were d wi th "never"

Adher ence: 73%

Continuou s mea sures of medi catio n

CMA ≥ 1 s i gnifies owi ng s ufficien t qua ntitie

Unclea r

p=0.0 14 (DA) p=0.0 06 (TA) p=0.1 42 (Ti A) p=0.0 01 (no DH)

43% (no DH)

p=0.0 08 (PC)

90% (PC)

p=0.5 24 (Moris ky)

79% (Moris ky) 97% (l ocal s elfreport )

CV 32.5% ± 16.1%

Yes , but no s i gnific a nt di ffere nces i l lustra ted

p=0.1 93 (l ocal s elfreport )

p=0.6 73 (CV)

Adher ence: 75%

p=0.4 57 (a dher ence)

Uncl e ar

Uncl e ar

Yes , but no s i gnific a nt di ffere nces i l lustra ted

Yes , but no s i gnific a nt di ffere nces

Journal Pre-proof

Prepos t comp a ri son cohor t s tudy

N=110 6

Reductio n of pi ll burden (extende d-release ta crol im us , s i roliumu s a nd corti cost eroi ds)

Prepos t comp a ri son cohor t s tudy

N=160

3 weeks

Medi ca ti o n a dhe renc e

Sel freport by l oca l questionn a i re

6 month s

Medi ca ti o n a dhe renc e

Sel freport (Gi rerd questionn a i re)

al

rn

6 month s

Jo u

Oh (81)

Medi ca ti o n a dhe renc e

94.6%

Sel freport (ITBS)

79.7% p<0.0 01

Ans weri ng a l l six question s wi th "no"

At s tudy end: 25.5% ("good a dhere nce") 68.0% ("mi no r nona dhere nce) 6.5% ("nona dhere nt")

At ba s eli ne: 20.9% ("good a dher ence") 72.0% ("mino r nona dher ence") 7.1% ("nona dher ent")

p=0.0 168 (a ny cha ng e in a dher ence)

No cl ear cut-off defi ned, but hi gher s core i ndicates more pa ti ent perceive d ba rri ers to medi cati on a dheren ce (ra nge 13-65)

Pos tconver s i on ITBS: 16.6 ± 3.6

Preconver s i on ITBS: 19.5 ± 4.0

p<0.0 01

Pr

(OSIRIS s tudy)

N=75

i l lustra ted

oo

Ca s s ut o (80)

Prepos t comp a ri son cohor t s tudy

pr

79)

Reductio n of pi ll burden (compl et e regi men s i mplific a ti on) Reductio n of pi ll burden (onceda i ly ta crol im us i ni tiation pos t-Tx)

e-

Va n Boekel(

s of medi cati on to a l low for perfect a dheren ce duri ng trea tme nt Sel freported a dheren ce

f

a dherenc e (CMA)

Yes , but no s i gnific a nt di ffere nces i l lustra ted Yes , 12 pa ti en ts with rejecti on: -4 mi nor nona dher ence -1 nona dher ent -4 "mi s si ng" Yes , but no s i gnific a nt di ffere nces i l lustra ted

Journal Pre-proof Fel lstr om (82)

Reductio n of pi ll burden

Obs er va ti on al

N=233 (175)

12 month s

Medi ca ti o n a dhe renc e

Sel freport questionn a i re(BAAS IS) BAASIS VAS

BAASIS VAS: vi s ual a dheren ce s ca le 0-100% (s elfreport) Trough l evels: no cl ear cut-off s ta ted Pa ti ent persisten ce**

57.0%

69.4%

94.3 ± 11.1%

95.3 ± 7.6%

6.0 ± 1.7 ng/mL

5.5 ± 1.9 ng/mL

Intergroup pva l ue not report ed for BAASI S s el freport /VAS, nor for trough l evels

Yes , but no s i gnific a nt di ffere nces i l lustra ted

p=0.0 824

No

Medi ca ti o n a dhe renc e

El ectronic moni torin g

pr

RCT

6 month s

e-

Educa tio n onl y

N=219 (N=145 )

RCT

N=150 (N=76)

1 yea r

Medi ca ti o n a dhe renc e

Pha rma cy refi ll records

Medi ca ti o n a dhe renc e

Number of pa ti ent obs erva ti ons (di ary)

Jo u

rn

4)

RCT

Pr

Chishol mBurns (8

Reductio n of pi ll burden (onceda i ly vs twi ceda i ly ta crol im us ) Educa tio nbehavi or al

al

Kuyper (83) s

oo

f

Ta crol imu s trough l evels

BAASIS: all 4 question s a ns were d wi th "no"

Urs ta d( 85)

N=159 (N=77)

6 month s (a dher ence eva lua ted onl y a fter 8 weeks pos tTx)

Pa ti ent i mpleme nta ti on* ** Tota l number of da ys between refi lls ≤ tota l da ys ' s upply of i mmuno s uppresi ve medi cati on --> a dheren ce ra te = 100% Da i ly report in di a ry of vi ta l s i gns mea sure d pos tTx

81.5%

71.9%

88.2%

78.8% p=0.0 009

89%

79%

Adher ence di ffere nce: p=0.0 076

No

Unclea r: "mea s ured numbe r of pa tient s obs erv a ti ons l ogged i n the di a ry, s i gnific a ntl y hi gher

Uncl e ar

p=0.0 0

No

Journal Pre-proof i n the experi mental group"

Educa tio nbehavi or al

RCT

N=111 (N=55)

3 month s

Medi ca ti o n a dhe renc e

Sel freport (ITAS)

ITAS s core = 12

86.5%

53.6%

p=0.0 01

De Geest(8

Educa tio nbehavi or al

RCT

N=18 pres electe d nona dhere nt pa ti ent s (N=6)

9 month s

Medi ca ti o n a dhe renc e

El ectronic moni torin g

Log odds on nona dhere nce:

Logg odds on nona dher ence:

Adher ence di ffere nce:

-3.5 (3 month s) -2.5 (9 month s)

-2.4 (3 month s) -1.7 (9 month s)

N=15 pres electe d nona dhere nt pa ti ent s (N=8)

6 month s

>98% ta ki ng a dheren ce a nd no drug hol idays a nd ti mi ng a dheren ce (<6h for once da i ly, <3h for twi ceda ily and <2h for threeda i ly regi men) Pres election EM a dheren ce s core >85%

0.8458

0.8153

Not menti oned

No

11.7 ± 0.6 (ITAS)

11.3 ± 2.0 (ITAS)

No

98.7 ± 2.6 (VAS)

98.9 ± 2.0 (VAS)

p=0.2 (group x ti me i ntera cti on)

26% "i mpro ved" 35% "no

20% "i mpr oved" 40% "no

Not menti oned

No

Pr

e-

(SMAR T tri a l)

oo

7)

pr

86)

f

Ga rci a(

RCT

Medi ca ti o n a dhe renc e

El ectronic moni torin g

Jo u

(TIMEli nk tri a l)

Educa tio nbehavi or al

al

(88)

rn

Russell

Cote (89)

Ha rdst a ff (90)

Webba s ed educatio n

Educa tio na l feedback

RCT

RCT

N=70 (N=35)

N=75 -> a vailabl e da ta N=48

6 month s

12 month s

Medi ca ti o n a dhe renc e

Sel freport (ITAS questionn a i re + VAS)

Medi ca ti o n a dhe renc

El ectronic moni torin g

Pos tenrollme nt no cut-off, but ca l culate d EM a dheren ce s core (0-1) No cl ear cut-off defi ned (ITAS ra nge 012, VAS ra nge 0100) Ta ki ng a dheren ce: mi s sing no doses

Yes , but no s i gnific a nt di ffere nces i l lustra ted No

P=0.0 6 (3 month s) P=0.5 8 (9 month s)

Journal Pre-proof (N=23)

Russell (91)

RCT

N=89 pres electe d nona dhere nt pa ti ent s (N=45)

12 month s

N=71 (N=35)

12 month s

a nd ta ki ng no extra dos es

Medi ca ti o n a dhe renc e

El ectronic moni torin g

Pres election EM a dheren ce s core ≥85% Pos tenrollme nt no cut-off, but ca l culate d EM ta ki ng a dheren ce s core (0-1)

di ffere nce" 39% "worse ned" At s tudy end: -Mea n TA 0.65 (Sd 0.37) Medi a n TA 0.77 (IQR 0.560.94)

di ffere nce" 40% "wors ened" At s tudy end: -Mean TA 0.53 (Sd 0.29) Media n TA 0.60 (IQR 0.440.73)

EM: Medi a n TA%: 78.3 (IQR 13.597.8) Medi a n Ti A%: 82.8 (IQR 39.494.4)

EM: Media n TA%: 90.2 (IQR 16.597.6) Media n Ti A%: 77.2 (IQR 7.497.3)

p=0.0 04

Pr

e-

pr

oo

f

(MAGI C tri a l )

Educa tio na l behavi or al

e

Medi ca ti o n a dhe renc e

al

RCT

rn

(IMAK T tri a l)

Educa tio na l behavi or a l wi th one ti me medi cati on revi ew

Jo u

Low (92)

El ectronic moni torin g

EM: -TA & Ti A ≥97%

Sel freport (BAASIS)

BAASIS: s core = 4 = a dherent

Sta ndard devi ation of ta crol imu s trough l evels (Ta cSd) Medi catio n pos sessio n ra ti o (ta crolimu s)

Ta cSd ≤ 2.5

MPR ta crol im us : no cut-off defi ned

BAASIS : a pprox i ma tel y 65% of pa tient s a dhere nt Ta cSd: 67% of

BAASI S: a ppro xi mate l y 45% of pa tien ts a dher ent Ta cSd:

EM: no pva l ue menti oned BAASI S: p<0.0 01 (rega r di ng i ntra group decre a s e of a dher ent pa tien ts , grea te r decre ase in contro l group) Ta cSd a nd

Yes , i nterv ention group s howe d l ower mea n creatin i ne a nd bl ood urea ni trog en l evels, but more i nfecti ons vers us contro l group (no pva l ue menti oned). No

Journal Pre-proof pa tient s Medi a n MPR ta crol i mus : 100% (IQR 74.0100)

N=110 (N=55)

Cukor(9

Ps ychoeducatio na l (cogni tiv ebehavi or al)

RCT

N=33 pres electe d nona dhere nt pa ti ent s (N=18)

Medi ca ti o n a dhe renc e a fter 6 mon ths

Sel freport (l ocal questionn a i re)

rn

2 weeks

Jo u

4)

al

Pr

e-

(93)

10 yea rs

Medi ca ti o n a dhe renc e

Adheren ce cutoff ra te defi ned a s the median s core of both groups a t week 8 evaluatio n (ba sed on trough l evels: 85.4%)

74.5% (6 month s a dhere nce)

47.5% (6 month s a dher ence)

No Pva l ue menti oned for a dher ence

Pres elected pa ti ents: <98% ca l culate d a dheren ce based on tel ephon e pi ll counts

Tel eph one pi l l count: 98% (ba seli ne 92%)

Teleph one pi l l count: 94% (baseli ne 94%)

p=0.0 03 (group cha ng e compa ri s on)

Ta cro trough l evels: mea n 6.4 ±3.5 (baseli ne 7.3 ±3.5)

p>0.0 5 (group cha ng e compa ri s on)

f

RCT

oo

Ps ychoeducatio na l

Media n MPR ta croli mus : 100% (IQR 87.5100)

MPR ta croli mus : no pva l ue menti oned

pr

BreuDejean

61% of pa tien ts

Tel ephon e pi ll count

Ta crol imu s trough l evels

Pos tenrollme nt: no cl ea r cut-off menti on ed

Ta cro trough l evels: mea n 5.7 ±1.8 (ba seli ne 5.4 ±2.8)

Yes , a l logra ft functi on wa s s i gnific a ntl y wors e i n the i nterv ention group a fter 10 yea rs (69.3% vs 87.3%, p=0.02 ) No

Journal Pre-proof Fos ter( 95)

(TAKEIT tri al)

Ps ychoeducatio na l (cogni tiv ebehavi or al)

RCT

N=169 (N=81)

12 month s

Medi ca ti o n a dhe renc e

El ectronic moni torin g Sta ndard devi ation of ta crol imu s trough l evels

No cutoff s pecified for a l l three di agnosti c methods

Odds ra ti o for 100% TA a nd Ti A not menti oned

0.945 (SE 0.037, 95% CI 0.8651.025)

0.574 (SE 0.036, 95% CI 0.4980.650)

122 mmHg (SBP) 81 mmHg (DBP)

139 mmHg (SBP) 79 mmHg (DBP)

SBP: P=0.0 09 DBP: P=0.0 06

EM: 78% (remi n der onl y) 88% (remi n der pl us provi d er noti fic a ti on)

EM: 55%

EM: Comp a ri son of ea ch i nterv ention vs contro l: p<0.0 01

Odds ra ti o for 100% Ti A: 1.74 (95% CI 1.212.50)

oo pr 3 month s

7)

(9

El ectroni c moni tori ng wi th remi nder , reminder pl us provi der a l ert vs no remi nder

Jo u

Reese

RCT

N=120 (1:1:1 ra ndo mi za tio n)

e-

N=21 pres electe d nona dhere nt pa ti ent s (N=11)

Medi ca ti o n a dhe renc e

El ectronic moni torin g

Pr

RCT

rn

96)

El ectroni c moni tori ng wi th reminder vs no remi nder

Bl oo d press ure (SBP & DBP)

al

McGi lli cuddy(

6 month s

Medi ca ti o n a dhe renc e

El ectronic moni torin g

Sel freport (BAASIS)

Ta crol imu s trough l evels (CV)

Pres elected pa ti ents: EM a dheren ce s core < 0.85 Pos tenrollme nt a dheren ce defi nitio n: i nta ke ti mi ng < 3hours Adheren ce <90% mea sure d twoweekly

BAASIS: no cl ear cut-off s ta ted

Ta crol im us trough l evels:

TA: p=0.0 06 Ti A: p=0.0 03 (No s tatisti ca l di ffere nce i n s tanda rd deviati on of ta croli mus trough l evels a nd s elfreport ed a dher ence) Medi c a ti on a dher ence: P<0.0 01

f

Sel freport (MAMMM)

Odds ra ti o for 100% TA: 1.66 (95% CI 1.152.39)

BAASIS : 78% (remi n der onl y)

BAASI S: 82%

CV ta croli mus : 0.24 ± 0.15

BAASI S: compa ri s on betwe en group

Yes , but no s i gnific a nt di ffere nces i l lustra ted

Except for bl ood pressu re, no other cl inical pa ra m eters eva lua ted

Not formal ly a s sess ed, but one pa rti ci pa nt develo ped transpl a nt fa i lure a nd one di ed duri ng the tri a l

Journal Pre-proof 74% (remi n der pl us provi d er noti fic a ti on) CV ta crol i mus : 0.23 ± 0.18 (remi n der onl y) 0.21 ± 0.15 (remi n der pl us provi d er noti fic a ti on) 97.8%

s p=0.8

(da ta cens or ed)

CV ta croli mus : compa ri s on betwe en group s p=0.3

N=80 (N=40)

12 month s

Medi ca ti o n a dhe renc e

El ectronic moni torin g

al

Pr

RCT

rn

El ectroni c moni tori ng wi th remi nder

Jo u

Henri k s s on(98)

e-

pr

oo

f

l oca l cl i nical protocol

Ta crol imu s trough l evels

Not cl ea rly defi ned, mos t probably ca l culati on of ta ki ng a dheren ce

Trough l evels: no cl ear cut-off s ta ted

7.32 ng/ml

Not moni t ored, beca u s e no EM device dispen s ed

7.22 ng/ml

Not a va ila bl e for EM a dher ence

pva l ue not menti oned

Yes , three ti mes more bi opsy veri fi e d rejecti on a mong contro ls (N=13) , thoug h not s ta tisti ca l ly s i gnific a nt (P=0.0 54, mul tiv a ri ate a nalysi s ). No di ffere nce i n pl a sm a creatin i ne

Journal Pre-proof N=18 (N=9)

12 month s pos ts tudy enroll ment

Tora bi(

Sma rtph one medi cati on reminder a pp

Contr ol l ed, nonra ndo mi zed

N=67 (N=18)

3 month s

Schmid

Telemedi ca l ly s upporte d ca s e ma nage ment

RCT

N=46 (N=23)

12 month s

Coef fi ci e nt of va ri a bi l ity (CV %) of s eru m ta cro l i mu s l evel s Medi ca ti o n a dhe renc e

Retros pec ti ve pa ti ent cha rt a na lysis

Ta crol imu s trough l evels

Ta rget SBP = KDIGO protocol = <131 mmHg

Overall una dju s ted mea n SBP 131 mmHg (N=8)

Overal l una dj us ted mea n SBP 155 mmHg

p=0.0 04 (overa ll una dj us teds SBP di ffere nce)

50% rea chi ng ta rget KDIGO SBP

11.1% rea chi ng ta rget KDIGO SBP

p=0.1 3 (rea ch i ng KDIGO SBP ta rget ) P=0.0 14 (CV 1 month ) P=0.6 3 (CV 3 month s)

No cl ear cut-off defi ned for CV

27.7 (CV 1 month ) 33.6 (CV 3 month s)

37.0 (CV 1 month ) 35.4 (CV 3 month s)

100% (media n CAS)

93% (medi an CAS)

al

rn Jo u

(101)

Pr

e-

100)

Bl oo d press ure

f

Retros pecti v e cohor t s tudy

oo

99)

Eva l uatio n of s us taina bi l ity of pri or medi cati on reminder i ntervent i on (71)

pr

McGi lli cuddy(

Adherenc e percentag e gra ding (CAS)

Ful ly a dherent mea ning : Col l atera l report s core ma xi mu m of 1.5 out of 5 Ta crol im us trough l evel: 4.5-9.2 ng/mL -Sel f report (BAASIS): a dheren ce i n every a s pect (l a cking memoriz a ti on of regi men dos es a cceptab

p<0.0 01

Except for bl ood pressu re, no other cl inical pa ra m eters eva lua ted

Serum creatin i ne wa s measu red duri ng tri a l, but not report ed

Yes , but no s i gnific a nt di ffere nces i l lustra ted

Journal Pre-proof l e)

Free of cha rge i mmunos uppressi ve medi cati on

Obs er va ti on al

N=18

12 month s

Medi ca ti o n a dhe renc e

Pha rma cy refi ll records

Monthl y a dheren ce ra te > 80%

Chishol m (103)

Free of cha rge i mmunos uppressi ve medi cati on

Retros pecti v e cohor t s tudy

N=33

12 month s

Medi ca ti o n a dhe renc e

Pha rma cy refi ll records

Adheren ce ra te > 80%

oo

pr

ePr al rn

Jo u

CAS, composite adherence s core; CI, confi dence interval; CV, coefficient of va riation; DBP, di astolic blood pressure; DH, drug holiday; ITAS, Immunosuppressant Therapy Adherence Scale; ITBS, Immunosuppressant Therapy Ba rrier Scale; IQR, i nterquartile range; BAASIS, Basel Assessment of Adherence to Immunosuppressive Medication Scale; KDIGO, Ki dney Disease: Improving Global Outcomes guinelines MAM-MM, Medi cal Adherence Measure Medication Module; EM, el ectronic monitoring; DA, da i ly a dherence; TA, ta king a dherence; Ti A, ti ming a dherence; PC, pi l l count; SBP, s ys tolic blood pressure; Sd, s ta ndard deviation; SE, s ta ndard error; VAS, vi s ual analog scale *= 5 di fferent methods for a dherence measurement:

12 month s pos tenroll ment: 48% a dhere nt 12 month s pos tenroll ment: 42% a dhere nt

f

Chishol m (102)

Ba s eli ne: 100% a dher ent

Ba s eli ne: Appro xi mate l y 94% a dher ent

Not menti oned

No

Yes , 12% (N=3) pa ti en ts i n cycl os pori ne group wi th rejecti on and 12.5% (N=1) i n the ta crol i mus group

Journal Pre-proof 1. the number of presciptions filled; 2. wei ghted medication possession ra tio (MPR); 3. medication gaps (MG) = a gap of at l east 60 da ys between run -out date of a prescription a nd fill date of subsequent pres cription; 4. di s continuation (DC) = a period of a t least 60 da ys between run-out date of l ast prescription and end of follow-up period; 5. ei ther a n MG or DC **Pa ti ents’ persistence with the medication, described as the percentage of patients who remained engaged with the regi men over the study period ***Pa ti ents’ implementation of the dosing regimen i s analyzed by evaluating the day-by-day percentage of patients with correct dos ing of each regimen over the study period

Jo u

rn

al

Pr

e-

pr

oo

f

Figure 1. Article tree

Figure 1