CLINICAL AND TRANSLATIONAL RESEARCH
`
`Predictive Patterns of Early Medication Adherence
`in Renal Transplantation
`Thomas E. Nevins,1,4 William N. Robiner,2 and William Thomas3
`
`Background. Patients’ adherence with posttransplant immunosuppression is known to affect renal transplant outcomes.
`Methods. Prospectively, individual medication adherence patterns in 195 kidney transplant recipients were quanti-
`fied with electronic medication monitors. Monitored drugs were mycophenolate mofetil, sirolimus, or azathioprine.
`T
`Monitoring began at hospital discharge and continued an average of 15
`8 months. Patient follow-up for clinical
`T
`outcomes averaged 8
`3 years. Each month’s adherence percentage was calculated as the sum of daily adherence per-
`cents, divided by the number of evaluable days.
`Results. During the first 3 months after transplantation, patients (n=44) with declining medication adherence, de-
`fined as dropping by 7% or higher (equal to missing 2 days) between months 1 and 2, later experienced lower mean
`G
`medication adherence for months 6 to 12, 73% versus 92% respectively (P
`0.0001). Compared to patients with stable
`adherence, they also had more frequent (P=0.034) and earlier (P=0.065) acute rejection episodes. This was addi-
`tionally associated with more frequent (P=0.017) and earlier (P=0.046) death-censored graft loss.
`In addition, daily medication adherence, expressed as the percentage of doses taken, decreased as the number of
`prescribed daily doses increased. During the first 3 months after transplantation, adherence with four doses per day
`averaged 84%, compared to 91% for patients on twice-daily dosing (P=0.024) and 93.5% for patients on once-daily
`dosing (P=0.008).
`Conclusions. Early declining medication nonadherence is associated with adverse clinical outcomes. This pattern
`is detectable during the first 2 months after transplantation. Early detection of nonadherence provides opportunities
`to target interventions toward patients at the highest risk for adverse behaviors and events.
`
`Keywords: Drug monitoring, Immunosuppression, Transplantation, Medication adherence.
`(Transplantation 2014;98: 878Y884)
`
`Renal transplantation is the optimal therapy for many
`
`patients with end-stage renal disease. Currently, ex-
`cept for identical twins, long-term successful transplanta-
`tion requires lifelong daily immunosuppression. Surprisingly,
`a significant number of transplant recipients fail to consis-
`tently follow their prescribed immunosuppressive regimen.
`This medication nonadherence (med-NA) ranges from acci-
`dental and rare to complete cessation of a drug. Although
`definitions of med-NA vary somewhat, individual studies
`(1Y4), database reviews (5), and meta-analyses (6, 7) have all
`demonstrated substantial med-NA rates after renal transplan-
`tation. Indeed, med-NA rates in renal transplant recipients
`are higher than those for any other solid organ transplant
`
`The authors all received salary support as faculty at the University of Minnesota
`and a grant from the National Institutes of Health (DK-13083).
`The authors declare no conflicts of interest.
`1 Division of Pediatric Nephrology, Department of Pediatrics, Univer-
`sity of Minnesota Medical School, Amplatz Children’s Hospital,
`Minneapolis, MN.
`2 Departments of Medicine and Pediatrics, University of Minnesota Med-
`ical School, Minneapolis, MN.
`3 Division of Biostatistics, School of Public Health, University of Minnesota,
`Minneapolis, MN.
`4 Address correspondence to: Thomas E. Nevins, M.D., Division of Ne-
`phrology, Department of Pediatrics, 420 Delaware St SE, 13-152 Phillips-
`Wangensteen Bldg, Minneapolis, MN 55455.
`E-mail: nevin001@umn.edu
`
`878 www.transplantjournal.com
`
`(6). Posttransplant med-NA has clearly been shown to be a
`critical factor associated with increased rates of graft dys-
`function and loss (1Y3, 7).
`Despite the obvious importance of med-NA (8, 9),
`there are only a few studies of posttransplant med-NA with
`the more potent, contemporary immunosuppressive drugs
`(5). We showed in a previous study of once-daily azathio-
`prine (Aza) adherence that there was a significant association
`of early, declining compliance with increased rates of acute re-
`jection and death-censored graft loss (1). These early-declining
`compliance (‘‘drop2’’) patients were those with at least two
`more days of missed doses in month 2 compared to month 1
`after transplantation, that is, adherence dropped by at least
`2 days from month 1 to month 2. In the present study, we
`report prospective electronic monitoring of contemporary
`immunosuppression confirming our earlier observations and
`demonstrating that the drop2 patients remain at increased
`
`T.E.N. developed the study concept, assisted with data interpretation, and
`wrote the primary article. W.N.R. assisted with the study design and
`execution and revised the article. W.T. performed all the statistical data
`analyses, prepared the figures and tables, and revised the article.
`Received 11 December 2013. Revision requested 20 December 2013.
`Accepted 25 February 2014.
`*
`Copyright
`2014 by Lippincott Williams & Wilkins
`ISSN: 0041-1337/14/9808-878
`DOI: 10.1097/TP.0000000000000148
`
`Transplantation & Volume 98, Number 8, October 27, 2014
`
`Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
`
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`Nevins et al.
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`879
`
`risk for adverse outcomes, even when prescribed more potent
`medications.
`
`RESULTS
`From August 1998 through August 2006, 1802 patients
`received kidney or kidney-pancreas transplants at the Uni-
`versity of Minnesota Medical Center-Fairview. Of these, 868
`(48.2%) were eligible, contacted, and invited to participate in
`this drug-monitoring study; 452 patients (52.1%) consented
`to participate. Study patients were given an electronic medi-
`cation event-monitoring system cap (MEMS cap; AARDEX
`Group Ltd., 1950 Sion, Switzerland) to record adherence with
`one of their immunosuppressive medications beginning at
`discharge from their hospitalization for renal transplant.
`By study design, prospective medication adherence
`monitoring was planned to extend to at least 1 year. One
`hundred ninety-five patients (43%) provided data for all or
`part of the first study year, 192 patients had evaluable data
`for the first three consecutive months after hospital dis-
`charge. Of these, 125 patients were prescribed twice-daily
`mycophenolate mofetil (MMF), 17 Aza and 28 sirolimus
`(Rapa) patients were prescribed their medication once daily.
`Of the 195 patients, 153 (78.5%) completed electronic mon-
`itoring through the end of their first year after transplan-
`tation. The mean length recorded by the MEMS cap was
`T
`15.8
`7.8 months. Follow-up for clinical outcomes averaged
`T
`7.9
`3 years. Outcome data are available for 166 patients (85%)
`at 5 years after transplantation and for 96 patients at 8 years
`after transplantation.
`Of 195 participants, 44 patients (22.6%) demonstrated
`adherence declines of 7% or more (equivalent to missing
`
`two or more additional days in month 2 versus month 1;
`‘‘drop2’’). The remaining 151 patients had either stable or
`improving rates of adherence during their second month
`after transplantation. Although the assignment of each pa-
`tient’s immunosuppressive drug protocol was not random-
`ized, there were no significant demographic differences
`between patient groups stratified by their drug regimens other
`than donor source and transplant number. Also while non-
`adherence was higher in patients taking more than one dose
`daily, the proportion of drop2 patients did not significantly
`differ by initial dosing regimen (Table 1). The drop2 group
`had experienced significantly more cases of early (e90 days)
`acute rejection. The only demographic factor associated with
`the drop2 group was being nonwhite, with no other signifi-
`cant differences noted (Table 2).
`These early adherence patterns persisted. Longer-term
`follow-up demonstrated that during months 6 to 12 after
`transplantation, drop2 patients had mean medication ad-
`T
`herence rates of 73%
`30%, while adherence in the stable
`T
`G
`group is 93%
`14% (P
`0.0001). Drop2 patients experienced
`twice the rate of acute rejection (P=0.034) and death-
`censored graft loss (P=0.017) seen in the stable adherence
`group (Table 2). Drop2 patients’ first rejection event tended
`to appear sooner (Fig. 1A, P=0.065) than patients with
`stable adherence. Similarly, allograft losses also appeared ear-
`lier (Fig. 1B, P=0.046). There were no significant differences
`in death rates or time to death between drop2 patients and
`the stably adherent participants. Setting aside the 15 patients
`who experienced early rejections (7 in drop2 and 8 in the
`stable adherence group), both rejection (P=0.099) and graft
`loss (P=0.050) remained twice as frequent in the drop2 group.
`
`TABLE 1. Demographic characteristics of patients divided by initial drug and dose prescription at hospital discharge
`MMF-4a (N=22)
`MMF-2a (N=128)
`
`All (N=195)
`
`AZA (N=17)
`
`RAPA (N=28)
`
`P
`
`Female
`Age
`Donor
`DD
`LRD
`LURD
`TX number
`1
`2
`3
`4
`Kidney and pancreas
`DM at TX
`Nonwhite
`Teenaged
`Early acute rejectionb
`Drop2c
`
`43%
`T
`48
`14
`
`59%
`T
`44
`11
`
`32%
`T
`45
`14
`
`44%
`36%
`20%
`
`83%
`14%
`2%
`1%
`31%
`47%
`7%
`3%
`8%
`23%
`
`24%
`71%
`6%
`
`65%
`24%
`12%
`0
`35%
`47%
`0
`0
`6%
`24%
`
`36%
`39%
`25%
`
`93%
`7%
`0
`0
`32%
`54%
`14%
`4%
`4%
`25%
`
`40%
`T
`49
`14
`
`48%
`29%
`23%
`
`80%
`17%
`1.5%
`1.5%
`27%
`41%
`5%
`4%
`9%
`19%
`
`59%
`T
`45
`13
`
`46%
`45%
`9%
`
`100%
`0
`0
`0
`55%
`68%
`14%
`0
`5%
`41%
`
`0.115
`0.141
`
`0.024
`
`0.036
`
`0.072
`0.109
`0.096
`0.669
`0.667
`0.144
`
`a MMF-2 indicates dosing twice daily, and MMF-4 indicates four times-daily dosing.
`b Acute rejection during the first 90 days after hospital discharge after transplantation.
`c Drop2 indicates subjects whose calculated percentage of adherence declined by a total of two or more days during the second monitored month compared
`to the first month.
`T
`Values are percent or mean
`standard deviation.
`P value for comparison between four drug-dose groups by W2 test or analysis of variance F test.
`DD, deceased donor; LRD, living related donor; LURD, living unrelated donor; TX, transplant; DM, diabetes mellitus.
`
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`Transplantation & Volume 98, Number 8, October 27, 2014
`
`TABLE 2. Demographic characteristics and transplant
`outcomes Y drop2 patients versus the remaining steady
`adherence patient group
`
`Female
`Age
`Donor type
`DD
`LRD
`LURD
`TX number
`1
`2
`3
`4
`Kidney and pancreas
`Diabetes at TX
`Nonwhite
`Teenaged
`Drug-doseb
`AZA
`RAPA
`MMF Y 2 times daily
`MMF Y 4 times daily
`Corticosteroids after discharge
`Initial immunosuppression
`CSA
`Tacrolimus
`Only MMF
`G
`Early acute rejection (
`Transplant outcomes
`Acute rejectionc,d
`Loss before deathc
`Deathc
`
`90 d)
`
`Drop2a
`(n=44)
`
`Steady adherence
`(n=151)
`
`43%
`T
`46
`14
`
`42%
`T
`48
`14
`
`50%
`36%
`14%
`
`80%
`16%
`5%
`0
`20%
`34%
`18%
`2%
`
`9%
`16%
`55%
`20%
`34%
`
`66%
`32%
`2%
`16%
`
`42%
`36%
`22%
`
`83%
`14%
`1%
`1%
`34%
`50%
`3%
`3%
`
`9%
`14%
`69%
`9%
`37%
`
`62%
`35%
`3%
`5%
`
`T
`T
`T
`
`1.6
`1.2
`1.1
`
`6.4
`3.7
`3.7
`
`T
`T
`T
`
`0.5
`0.4
`0.5
`
`2.5
`1.6
`2.7
`
`P
`
`0.925
`0.356
`
`0.397
`
`0.482
`
`0.078
`0.057
`0.002
`0.726
`
`0.144
`
`0.716
`
`0.849
`
`0.020
`
`0.034
`0.017
`0.327
`
`a Drop2 indicates subjects whose calculated percentage of adherent days
`declined by a total of two or more days during the second monitored mo.
`compared to the first mo.
`b Drug-dose is initial drug and dose regimen at the time of hospital
`discharge.
`T
`c Rates per 100 patient-years
`standard error.
`d For acute rejection, rates include repeated occurrences of acute rejec-
`tion, whereas log-rank test compares product-limit curves to first rejec-
`tion (see Fig. 1A). Acute rejections during the first 90 days after transplant
`were omitted.
`T
`Values are percent, or mean
`standard deviation, or rate per 100 patient-
`T
`years
`standard error.
`P value for comparison by W2 test or t test.
`DD, deceased donor; LRD, living related donor; LURD, living unrelated
`donor; TX, transplant; CSA, cyclosporine A; AZA, azathioprine; RAPA, sirolimus.
`
`Of the 195 recipients, 45 had their monitored drug
`(Aza or Rapa) prescribed as a single daily dose. The re-
`maining 150 patients were initially prescribed MMF at a
`frequency of twice daily (n=128) or, in an empiric effort to
`minimize side effects, four times daily (n=22). Independent
`of the specific drug monitored, the 3-month medication ad-
`herence rates varied inversely with the number of daily drug
`
`doses prescribed. During the first month after discharge,
`43% of patients taking single daily doses of a monitored
`medication missed at least one dose. This percentage in-
`creased to 49% during month 3. During the same intervals,
`73% of patients prescribed four doses per day missed at least
`one dose of medication during month 1 and 76% missed
`doses in month 3 (Fig. 2). During the first 3 months, pa-
`tients prescribed single daily doses of medication took a
`mean of 93.5% of their medication; and twice-daily doses,
`a mean of 91%. Patients prescribed medication four times
`per day took 84% of their prescribed doses. Medication
`adherence rates for once-daily (P=0.008) and twice-daily
`dosing (P=0.024) were significantly better than four-times-
`per-day dosing. Comparing adherence rates, there was no
`statistically significant difference between once-daily and twice-
`daily dosing.
`
`FIGURE 1. A, Time to first acute rejection beginning
`90 days after hospital discharge. Kaplan-Meier curves de-
`fining the rejection-free survival of patients with steady or
`declining (drop2) medication adherence; vertical dashes
`mark censoring events. The table indicates the number
`of patients at risk in 2-year intervals. There is a trend to-
`ward earlier and more frequent rejections in the drop2
`group compared to the steadily adhering group (log-rank,
`P=0.065). B, Time to deathYcensored graft loss. The Kaplan-
`Meier curves defining the death-censored allograft survival
`for patients with steady or declining (drop2) medication ad-
`herence; vertical dashes mark censoring events. The table
`indicates the number of patients at risk in 2-year intervals.
`There were more frequent and earlier graft losses in the
`drop2 group compared with the steadily adhering group
`(log-rank, P=0.046).
`
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`FIGURE 2.
`Sorted by drug and dose schedule, the stacked bar graph displays the percentage of patients missing no,
`one, two to three, and four or more doses per month during the first 3 months after transplant. There were 16 patients on
`once-daily azathioprine (Aza), 26 on once-daily sirolimus (Rapa), 124 on twice-daily mycophenolate mofetil (MMF), and 22
`on four times-daily MMF. Seven patients were excluded because they either changed drug or dose schedule during the first
`month or had less than five evaluable days in any month.
`
`Rank ordering each patient’s mean proportional ad-
`herence during the first 3 months, according to prescrip-
`tion of once or more than once daily, produces similar
`patterns (Fig. 3), indicating that at least two thirds of pa-
`tients in both groups took more than 90% of their medi-
`cation. Focusing exclusively on patients prescribed MMF
`twice a day (n=128), the mean interdose interval in months
`1 to 3 after transplant, expected to be about 12 hours, was
`T
`T
`19
`13 hours for the 24 drop2 patients and 13
`6 hours for
`the 104 stably adherent patients (P=0.0014). Longer-term
`differences in adherence persisted: mean adherence during
`T
`months 6 to 12 was 63%
`33% in the drop2 group and
`T
`G
`92%
`15% in the stable group (P
`0.0001). On overall
`follow-up, drop2 patients experienced four times the rate of
`acute rejection (P=0.021) and almost three times the rate of
`death-censored graft loss (P=0.012) observed in stably ad-
`herent patients (data not shown). Even omitting patients
`with early rejections (five patients from the drop2 group and
`seven from the stably adherent patients), the rates in the
`drop2 group remained more than twice as high as in the rates
`in the stably adherent patients for both rejection (P=0.256)
`and death-censored graft loss (P=0.030).
`
`DISCUSSION
`Data in this study highlight two important early pat-
`terns in med-NA. First, this prospective patient cohort con-
`firms that med-NA appears early after transplant and that
`the pattern of early declining adherence is associated with
`significantly poorer late allograft outcomes. Second, the com-
`plexity (i.e., doses per day) of the immunosuppressant medi-
`cation regimen directly affects adherence rates.
`Quantitative medication adherence has been reported
`in a variety of chronic clinical conditions, including sei-
`zures (10, 11), glaucoma (12, 13), human immunodeficiency
`(14Y17), hypertension (18, 19), chronic anticoagulation
`(20, 21), and congestive heart failure (22). Although most
`studies were of short duration and used differing adherence
`
`definitions, they all observed that med-NA 1) was detectable
`in each study and 2) was regularly associated with adverse
`outcomes. Med-NA occurs commonly in asymptomatic med-
`ical conditions requiring chronic medication. In a wide vari-
`ety of chronic diseases, 15% to 25% of patients have been
`reported to rapidly reduce or discontinue their prescribed
`drug shortly after the initial prescription (11, 12, 17, 18, 20).
`Individually, adherence rates vary, perhaps reflecting each pa-
`tient’s perception of the clinical importance of the condition
`being treated (23) and the anticipated risks associated with
`
`FIGURE 3. Two distributions of mean proportional ad-
`herence per patient during the first 3 months for patients
`taking one versus multiple daily medication doses. Patients
`taking MMF two or more times a day (n=150) are repre-
`sented by black symbols, whereas the 45 patients taking
`medication once daily (Aza, n=17; Rapa, n=28) are repre-
`sented by gray symbols. In each subgroup, drop2 patients
`are represented by triangles and steadily adhering patients
`are represented by circular symbols. Vertical lines divide
`subjects into tertiles. Note that drop2 patients are not lim-
`ited to the lowest tertile. Note the highly similar distribution
`curves indicating that the proportional definition of adher-
`ence identifies a similar adherence distribution in either
`single- or multiple-dose patients.
`
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`missing medication. In this regard, solid organ transplant re-
`cipients consistently demonstrate better overall rates of ad-
`herence with their medications compared to patients with
`asymptomatic conditions such as hypercholesterolemia (23)
`or hypertension (18).
`Remarkably although solid organ transplant recipients
`are regularly reminded that immunosuppressive NA may
`result in graft loss or even death, med-NA seems ubiquitous
`(6). With improving transplant protocols, decreasing rates
`of early rejection, and patient care advances, med-NA has
`emerged as a critical barrier to achieving optimal long-term
`transplant outcomes (1Y3, 24, 25).
`We previously reported that significant posttransplant
`med-NA could be detected during the first few weeks after
`hospital discharge (1, 2). In that analysis of a natural history
`cohort, a 7% decline (e.g., two missed doses over 30 days) in
`Aza adherence during the second month after transplanta-
`tion identified patients who experienced significantly earlier
`and more frequent episodes of acute rejection as well as
`increased rates of allograft loss. Now analyzing twice-daily
`MMF using a proportional adherence model, the distribu-
`tion of adherence is virtually identical to that seen with
`once-daily Aza or Rapa (Fig. 3) (1). Despite historically lower
`rejection rates (26), the present prospective study confirms
`our earlier finding that early declining adherence was asso-
`ciated with significantly more frequent and earlier episodes
`of rejection (Fig. 1A). Using contemporary immunosuppres-
`sion, acute rejection rates are 250% higher in patients with
`early declining adherence compared to stably adherent pa-
`tients, demonstrating that even today’s potent immuno-
`suppressive drugs are ineffective at preventing rejection if
`taken inconsistently. Clearly, med-NA will remain a concern
`during the development and study of future immunosup-
`pressant drugs.
`Declining medication adherence is further associated
`with both earlier and higher rates of death-censored graft loss
`(Fig. 1B; P=0.046). The drop2 group exhibits a 200% increase
`in graft loss when compared to stably adherent allograft re-
`cipients at 5 years after transplantation.
`Recognition of early (first 2Y3 months) declining ad-
`herence consistently identifies patient groups at risk for early
`discontinuation or significant med-NA to their therapeutic
`regimen (9). These dynamic patterns are only demonstrable
`with quantitative data such as those provided by MEMS tech-
`nology (11, 22). Clinically, this drop2 measure of dynamic
`declining adherence is available immediately for each patient
`because it is derived from the patient’s own records without
`reference to any outside group or norm. The pivotal impor-
`tance of this observation is that early recognition of med-NA
`permits targeting adherence-promoting interventions to a
`defined subset of patients at high risk for adverse behaviors
`and outcomes. Newer generations of electronic medication
`monitors provide adherence data in ‘‘real time.’’ Ideally, effec-
`tive and sustained interventions will provide enduring im-
`provements in adherence and subsequent clinical benefits for
`both renal transplant recipients and other patient populations
`(11, 13, 18, 22).
`It has long been recognized that the complexity of
`a medication regimen affects adherence. Our data demon-
`strate that after transplantation, the more times per day a
`patient is expected to take a medication, the more likely
`
`he or she is to miss doses. A previous review of quantita-
`tive medication adherence by Claxton et al. (27) linked the
`prescribed number of daily doses to the electronically docu-
`mented adherence rates in 76 separate studies across diverse
`medical conditions. They demonstrated that, on average, a
`single daily dose yields the highest adherence rate at 79%.
`More frequent doses resulted in less adherence; twice-daily
`dosing yielded 69%, three doses per day produced 65%, and
`four doses per day resulted in adherence declining to 51%.
`Our patients’ adherence patterns are strikingly similar. How-
`ever, perhaps because of the importance of a renal transplant,
`the mean adherence rates are all proportionately higher. Simi-
`lar to Claxton et al., our data do not show statistical differ-
`ences in adherence between once-daily and twice-daily dose
`schedules. Clinically, any expected benefit from more fre-
`quent medication dosing must be balanced against the like-
`lihood that patients will not take all of the prescribed doses.
`Certainly, medication costs present yet another barrier
`to adherence. In this cohort of renal transplants, medication
`costs were covered by Medicare and supplemented by ad-
`ditional third party insurance. This was critically true during
`those first 2 to 3 months after transplantation when the drop2
`pattern was detected. Unfortunately, Medicare prescription
`coverage abruptly ends 3 years after transplantation and thus
`becomes an added barrier to individual medication adher-
`ence (28) and successful transplantation.
`This study has some limitations related to both sam-
`pling bias and technology. We could only measure adher-
`ence in those patients who consented to be observed. This
`may limit the generalizability of our findings. But since we
`may have sampled a group of patients likely biased to be
`more adherent, med-NA in the entire transplant population
`is perhaps even more prevalent than we observed. Even after
`consenting, patients sometimes dropped out or failed to re-
`turn their monitor cap, further limiting our assessment. Al-
`though the MEMS technology is an excellent tool to measure
`adherence (9), there is no certain proof that a patient remov-
`ing the monitor cap actually takes the prescribed dose of
`medication at that time. Also, because all patients were in-
`formed that their medication taking was being monitored,
`this may have masked some early med-NA. Finally, the extent
`to which our renal transplant data accurately characterize ad-
`herence for other solid organ transplants including liver or
`heart is not known (6).
`In conclusion, med-NA is a major clinical problem in
`renal transplantation. We demonstrated that it is possible
`to prospectively identify patients at increased risk for ad-
`verse events including acute rejection and graft loss based
`on their adherence patterns observed during the first 2 to
`3 months after transplantation. The sign of early declining
`adherence deserves more careful attention because it pre-
`dicts an increased risk of chronic med-NA as well as later
`adverse outcomes (2, 24). Also it should now be possible to
`focus behavioral intervention efforts on these vulnerable pa-
`tients early when their med-NA pattern is first recognized.
`Similarly, the observation that medication regimens consist-
`ing of more frequent daily doses are less likely to be precisely
`followed has management implications because simpler drug
`regimens (i.e., fewer doses per day) should promote better
`adherence. The consistency of our findings in two prospective
`renal transplant patient cohorts as well as the findings of other
`
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`
`Nevins et al.
`
`883
`
`investigators underscores the need for additional research
`to identify and better understand medication adherence
`patterns while also developing strategies to improve medica-
`tion adherence.
`
`MATERIALS AND METHODS
`Medication adherence in outpatients after renal transplant was moni-
`tored using an electronic medication event monitoring system (MEMS;
`AARDEX Group, Ltd., 1950 Sion, Switzerland) to quantify adherence. Re-
`cipients were eligible for this study if they were discharged with a function-
`ing renal allograft, were able to speak and read English, and were directly
`responsible for taking their own medication. All patients received initial in-
`duction therapy with an anti-lymphocyte antibody. For this study, the choice
`of immunosuppressive medications was not randomly assigned but was
`based on the clinical assessment of each patient. Most adult patients were
`treated with rapid discontinuation of all corticosteroids (29) and received
`either cyclosporine or tacrolimus (Table 2). The monitored drug was MMF,
`Rapa, or Aza.
`Details of the medication monitoring protocol have been previously
`published (1, 2). Briefly, each time the monitor cap was removed from the
`medication vial, the date and time of that event were recorded in the cap
`memory and presumed to represent a medication dose taken. To minimize
`confusion about dose times, each patient’s daily medication record began at
`3:00 A.M. and ended the next day at 2:59 A.M.. Beginning the first day after
`the initial hospital discharge, each monitored day was evaluated for medi-
`cation adherence. Using proprietary software, continuous dosing records
`were compiled for each patient and analyzed.
`Every patient’s chart was reviewed, and all hospitalizations, drug doses,
`or schedule changes were noted. When a medication was temporarily dis-
`continued, the absence of a cap opening on that day was considered ‘‘ad-
`herent.’’ When a patient was hospitalized or the cap data were not available
`for technical reasons, those days were considered as ‘‘missing’’; all other
`days were evaluable. No data are missing because of cap technical failures.
`Proportional adherence was expressed as the proportion or percentage
`of prescribed doses taken each day. Thus, for once-daily dosing, each day
`was either 100% or 0% adherent. For a drug prescribed twice daily, each day
`could be 100%, 50%, or 0% adherent, based on taking 2 doses, 1 dose, or
`no dose, respectively. The individual’s monthly adherence percentage was
`calculated as the sum of daily adherence percents divided by the number
`of evaluable days. The number of ‘‘missed dose days’’ in a month was the
`number of evaluable days minus the sum of daily adherence percents. The
`drop2 subgroup patients (1, 2) were those with month 2 ‘‘missed dose days’’
`at least 2 days larger than in month 1. For each ‘‘month’’ of 30 days, drop2
`corresponds to an increase in monthly percent med-NA (Q6.7% from
`month 1 to month 2).
`All patients were followed up for the clear clinical end points of acute
`rejection, allograft survival, and death through December 1, 2011. By design
`(1, 2), early acute rejection (e90 days) was analyzed separately to evaluate
`its effect on later outcomes. Acute rejection was diagnosed in kidney biopsy
`or nephrectomy specimens (1). When a tissue diagnosis was not available,
`the clinical diagnosis of acute rejection was based on an otherwise unex-
`plained elevation of creatinine, coupled with appropriate physical signs (in-
`cluding fever, hypertension, or oliguria), resulting in the clinical decision to
`treat the patient for acute rejection. Renal transplants were considered lost
`when patients received a new transplant or returned to regular dialysis. We
`compared the rates of these outcomes for drop2 patients versus all the re-
`maining patients with more stable adherence. We also determined the overall
`rates of adherence during the first 3 months as a function of the monitored
`drug and its daily dosage schedule.
`
`Statistics
`Demographic factors and outcomes were compared using W2 or Fisher
`exact test; continuous demographic variables were compared with anal-
`ysis of variance. Event rates were compared using Poisson regression that
`can accommodate repeated occurrences of acute rejection, and Kaplan-
`
`Meier estimates of time to event were compared with the log-rank test.
`T
`Values reported are percents or mean
`standard deviation. Computations
`were performed using SAS Version 9.3 (SAS Institute, Inc., Cary, NC).
`Figures were drawn in R (R Foundation for Statistical Computing, 2012,
`http://www.R-project.org).
`The University of Minnesota Institutional Review Board approved this
`study (no. 9611M11943) and reviews it annually. Participants were specifi-
`cally informed that their medication taking behavior was being monitored
`from the beginning of the study.
`
`ACKNOWLEDGMENTS
`The authors gratefully acknowledge encouragement, sup-
`port, and critical review from Drs. Arthur Matas and Michael
`Mauer. Also deeply appreciated are the daily efforts and per-
`severance of research coordinators: Nancy Flaherty, Christine
`Jacox, Trudy Strand, Judith Graziano, and Linda Kruse.
`
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