Exploring risk factors of non-adherence to immunosuppressive medication in kidney transplant recipients : improving methodology reorienting research goals

摘要

8.1. Background and aim of the research programudNon-adherence to the immunosuppressive therapy is an important issue in kidneyudtransplant patients. About 20% of the kidney transplant patients are non-adherent toudthe immunosuppressive regimen. Non-adherence contributes to 20% of late acuteudrejection episodes and 16% of the graft losses, and results in a decreased number ofudquality adjusted life years. A strategy to increase long-term successful outcome afterudtransplantation is to identify patients at risk for non-adherence and to target them forudpreventive and adherence enhancing interventions. Comprehensive research on riskudfactors of non-adherence addressing socio-economic, patient-, condition-, therapy-,udand health care system/worker-related factors is lacking. Especially health care-relatedudrisk factors are understudied. Moreover, existing studies are hampered by a number ofudmethodological shortcomings. An important shortcoming is that accurate measurementudmethods for detecting non-adherence, such as electronic monitoring (EM) are rarelyudadopted. EM, currently the most sensitive adherence assessment method, usesudmicrochip technology to register date and time of openings of a pill bottle. AlthoughudEM’s superior sensitivity to detect non-adherence makes it a potential gold standard ofudadherence assessment, the lack of thorough validation as well as the lack of use ofudappropriate statistical methods for multivariable and/or longitudinal data analysis of EMuddata, hinder progress in the field.udThe main purpose of this research program was to determine prevalence and riskudfactors of non-adherence to immunosuppressive medication in kidney transplantudpatients. As an additional purpose, we aimed to improve the validity of EMudmeasurement by mapping assumptions underlying correct EM measurement. Weudtested these assumptions on adherence data of kidney transplant patients.ud8.2. Methodsud8.2.1. Prevalence and risk factors of non-adherenceudTo study prevalence and risk factors of non-adherence to immunosuppressives, weudconducted a prospective study, in which we measured adherence by EM over a 3-udmonth period in 250 adult renal transplant patients sampled from two outpatientudtransplant centers in Switzerland. We calculated period prevalences of adherence andudexpressed them as the percentage of prescribed doses taken (taking adherence), theudpercentage of days with correct dosing (dosing adherence), the percentage of interdoseudintervals not exceeding 25% of the prescribed interval (timing adherence), andudthe number of drug holidays per 100 days (>48h no intake if once; >24h if twice dailyudintake).udSelected risk factors were socio-economic, therapy related (e.g. number ofudtransplantations, use of medication aids, symptom occurrence and distress), patientudrelated (e.g. self-efficacy, health beliefs, coping styles, busyness, health behaviors),udcondition related (e.g. depression, substance use), and health care system/teamudrelated (e.g. regularity of follow up). Testing of the risk factors occurred by simpleudmixed logistic regression analysis, using a sequence of daily binary adherence data.udFactors significant after correction for multiple testing were entered into a multipleudmixed logistic regression model.ududBecause the EM-study was not designed to extensively investigate health care systemudor health care team-related factors, we performed an additional meta-analysis to lookudwhether non-adherence prevalences differed between continents/countries. This metaanalysisudon individual patient data pooled data from three studies in adult kidneyudtransplant patients from the US (n=1563), the Netherlands (n=85), Belgium (n=187)udand Switzerland (n=342). Adherence was measured by the Siegal scale, a self-reportudinstrument for measuring non-adherence to immunosuppressives. Patients wereudcategorized as non-adherent if they reported to have missed a dose ofudimmunosuppression in the last 4 weeks. Data were analysed using multiple mixedudlogistic regression with center as a random effect and continent/country as fixedudeffects, while controlling for several demographical and clinical characteristics of theudincluded samples.ud8.2.2. Validation of EM assessmentudTo study the validity of the EM measurement, we summarized existing evidence onudprocesses that may bias non-adherence assessment. Unbiased EM assessment requiresudfulfillment of four validity assumptions, being (1) correctly functioning EM equipment,ud(2) correspondence of EM-bottle openings to the actual intake of the prescribed dose,ud(3) absence of influence of EM on a patient’s normal adherence behavior, and (4)udsample representativeness.udWe examined these four validity assumptions using the above mentioned sample ofud250 kidney transplant patients whose adherence was measured by EM. Moreudspecifically, we (1) determined the prevalence of non-functioning EM systems, (2)udexamined the impact of patient-reported discrepancies between cap openings andudactual drug intakes on period prevalence, (3) explored whether non-adherenceudincreased over time after patients started EM, and (4) screened for differencesudbetween participating patients and patients who refused to participate or who droppedudout of the study.ud8.3. ResultsudMean taking, dosing, timing adherence and drug holidays per 100 days were 98%,ud96%, 93%, and 1.1 days, respectively. Variables associated with EM measured nonadherenceudwere: higher self-reported non-adherence (OR= 3.08; 95%CI: 1.69-5.61),udno usage of a pillbox (OR= 0.31; 95%CI: 0.16-0.61), male gender (OR= 0.46; 95%CI:ud0.26-0.81), and lower self-efficacy (OR= 0.49; 95%CI: 0.22-1.07). Furthermore, audgradually declining adherence could be observed between Monday and Sunday (OR=ud1.04; 95%CI: 1.02-1.07).udThe results of the meta-analysis examining self-reported non-adherence differencesudbetween continents/countries showed that the prevalence of non-adherence toudimmunosuppressives in the U.S. and Europe was 19.3% and 13.2.%, respectively Theudhigher prevalence of non-adherence in US patients was confirmed in the multipleudlogistic regression analysis (OR=1.78; 95%CI: 1.10-2.89). Moreover, non-adherenceuddiffered between Belgium (16%) and the Netherlands (14.1%) (OR=0.27; 95% CI:ud0.09-0.80) and between Belgium and Switzerland (11.4%) (OR=0.17; 95% CI: 0.0-ud0.42).udThe validation study of EM showed that not all assumptions underlying EMudmeasurement were fulfilled: (1) one cap malfunctioned, (2) mismatches between bottleududopenings and actual drug intake occurred in 62% of the patients (n=155), and (3)udnon-adherence increased during the initial period of the monitoring, primarily duringudthe first 5 weeks, indicating EM had an intervention effect. The bias caused by this 5-udweek intervention effect was minimal. The effect of mismatches between bottleudopenings and actual drug intake on the measured adherence prevalence was larger,udbut could be minimized by correcting the downloaded EM data using patient selfreportsud(i.e., self-reported adherence to the EM guidelines and notes made by theudpatient to correct mismatches between openings and ingestions).ud8.4. ConclusionsudThis study program aimed to study risk factors of non-adherence in kidney transplantudpatients. Its contribution to the literature lies in the fact that a comprehensive numberudof non-adherence risk factors, including the currently neglected health care systemudfactors, have been explored, and in the fact that improvements of the methodologicaludapproach for adherence studies have been proposed.udThe profile of risk factor appearing in the final results suggest that forgetfulness was audmajor driver of non-adherence. Moreover, system factors might also have an impact onudindividual adherence behavior, as suggested by the found differences in prevalence ofudnon-adherence between European and US patients and among European patients.udThese findings may change the focus of adherence research in the transplantudpopulation.udMethodological improvements put forward throughout this study program primarilyudconcern the measurement of adherence behavior using EM. Novel statistical techniquesudare proposed that allow multivariate analysis of EM data and inclusion of time-varyingudvariables into the statistical regression models. Besides, we showed that, althoughudassumptions underlying valid EM measurement may be violated, bias can to a certainudextent be prevented by correcting incorrect data or omitting them from the analysis.