Objectives: A psychosocial evaluation before liver transplant aims to identify risk factors for nonad-herence and poor outcomes posttransplant. Despite broad support for such evaluations, evidence justifying its components is thus far limited. We investigated whether variables assessed during the psychosocial evaluation before liver transplant predict immunosup-pressant nonadherence and graft rejection.

Materials and Methods: Our study included 248 adult liver recipients at least 1 year after transplant with at least 3 measured tacrolimus levels. Predictor variables from the pretransplant evaluation were defined a priori and included sociodemographic factors (age, race, time since transplant), psychiatric history, substance abuse history, education level, and social support. Nonadherence was determined using the medication level variability index, which is an objective measure of adherence reflective of high medication level fluctuation from nonadherence. Outcomes (medication level variability index and biopsy-confirmed rejection) were obtained 1-year posttransplant to the present.

Results: We found that 50% of patients were nonadherent to tacrolimus (medication level variability index > 2.5). The 41 patients with rejection (t = 2.71, P < .01) and black patients (F = 3.10, P = .02) had significantly higher index scores. Time since transplant was correlated with medical level variability index (r = 0.15, P = .02). However, in logistic regression, none of the predefined psychosocial variables predicted nonadherence (P = .40) or rejection (P = .19).

Conclusions: Our results confirmed a significant association between medication level variability index and rejection, validating it as an objective measure of clinically significant nonadherence. In a large sample with high rates of nonadherence, none of the pretransplant psychosocial variables commonly used in standard liver transplant evaluations predicted nonadherence or rejection. These findings call into question current selection criteria. Future prospective studies are needed to investigate more com-prehensive psychosocial variables and their ability to determine posttransplant outcomes.

Key words : Graft failure, Liver transplantation, Medication nonadherence, Transplant psychosocial evaluation

Introduction

Nonadherence to immunosuppressant therapy occurs in 15% to 40% of adult liver transplant (LT) recipients and is associated with poor outcomes.^1-4 Psychosocial assessments before LT are performed with the hope that early detection of risk can lead to better selection of candidates or inform interventions that may mitigate identified risk before or after transplant.⁵ In turn, such evaluations have become standard practice in most transplant programs.^6,7 Despite broad support for performing these evaluations,8 there is little agreement as to what components should be evaluated, perhaps because there are no rigorous randomized controlled, long-term studies to date that establish whether addressing psychosocial risk factors in fact improves post-LT adherence or outcomes. In the absence of information about inter-vention effects, transplant centers may “pick and choose” risk factors that they believe are important or modifiable in their particular setting. However, such chosen factors may reflect a particular center’s bias or beliefs rather than evidence-based criteria.

Although it has not been established whether early identification of risk factors can lead to better outcomes, studies do exist about the link between some risks and medication adherence, perhaps the most critical component of the posttransplant regimen. Risk factors that have been reported to be associated with post-LT medication nonadherence include limited health literacy, especially medication knowledge,⁹ nonwhite ethnicity,^10,11 inadequate social support,^10,12 self-reported pretransplant medication nonadherence,^12-14 negative medication beliefs and illness perceptions,¹⁵ lower level of the personality trait “conscientiousness,”¹² higher education levels,¹² and pretransplant mood disorders, including depression.^16-18 These findings are inconsistent across studies, likely because of inadequate study power and differences in methodology, including the use of subjective techniques to measure nonadherence.

Identifying predictors of medication nonad-herence is challenging, given the difficulty of enrolling nonadherent individuals into research and measuring nonadherence objectively. Studies evaluating risks for post-LT medication nonadherence have several methodologic shortcomings that may render results less generalizable than is usually assumed. First, prospective studies of risk, by design, fail to capture many of the most nonadherent patients, who are less likely to consent to research in the first place and for whom prospective collection of follow-up infor-mation may be challenging.¹⁹ Thus, prospective designs may at times display selection bias, making the results less generalizable. Second, the adherence literature has not reached a point in which there is either an agreed on “criterion standard” measu-rement of adherence or a threshold of measurement,²⁰ resulting in the use of subpar adherence measures in studies. Ultimately, the use of different methodo-logies renders it difficult to compare studies or pool them together, thus casting uncertainty on which predictors of nonadherence are meaningful and clinically significant.

The present hypothesis-driven, retrospective study predefined psychosocial risk factors and investigated whether they predict immunosup-pressant nonadherence as measured by drug-level variability and biopsy-proven rejection outcomes. We investigated the most commonly used sociodemo-graphic and psychosocial variables considered in standard pre-LT evaluations used across most US transplant centers. In addition, we used an objective measure of nonadherence, the medication level variability index (MLVI), which represents the degree of fluctuation between individual medication blood levels. The MLVI, as described below, has been shown to be predictive of poor outcomes after adult LT independently in 2 centers^21,22 and has also been examined in several centers in pediatric recipients.^23-25 Medication level variability index values above 2 to 2.5 have been associated with rejection and are highly correlated with electronic measurements of adherence,²⁶ suggesting that it is a reliable objective measure of clinically significant nonadherence. Although MLVI remains to be validated in a large, multicenter study, it is one of the only available objective measures of nonadherence that can be easily used in clinical settings.

Although there is evidence to suggest associations between nonadherence and certain demographic or psychosocial variables, these findings are inconsistent and not well substantiated in the literature. Among the variables investigated and used in most standard pre-LT evaluations (including sex, race, marital status, employment status, social support, substance abuse history, and psychiatric history), we hypothesized that none of these variables would be predictive of outcomes in a large patient population given inconsistent and unconvincing findings in prior studies.

Materials and Methods

Study population
Adult LT recipients whose medical records were available at the Recanati Miller Transplant Institute at the Icahn School of Medicine at Mount Sinai Hospital in New York between 2009 and 2013 were identified (N = 423). Among the 423 adult LT recipients, 248 were included in this retrospective review (Figure 1). All patients included in this study had regular follow-up in the Recanati Miller Transplant Institute LT clinic and received standardized post-LT education materials, including counseling on the importance of daily immunosuppressant adherence and avoidance of using herbal medicines, sup-plements, or consumption of grapefruit juice. Inclusion criteria for the 248 patients selected for the study were as follows: underwent LT for any reason between 2009 and 2013, were prescribed tacrolimus between 2009 and 2013 for maintenance immuno-suppression, were at least 1 year post-LT, and had at least 3 tacrolimus levels since 1 year after LT. Consecutive tacrolimus levels were measured in the outpatient setting at any time interval from 1 year post-LT until the study date, excluding inpatient hospitalization levels.

A total of 175 excluded patients, 45 of whom were excluded given they were not taking tacrolimus. Instead, these individuals were taking primarily sirolimus (16 patients), cyclosporine (25 patients), or some other combination of immunosu-ppressant therapy (2 patients) as their maintenance immuno-suppression therapy. Other exclusion criteria included: received dual LT or other solid-organ transplant (17 patients), underwent repeat LT (12 patients), died within 1 year of LT (62 patients), and had no documented tacrolimus levels within the past year or had less than 3 tacrolimus levels in total after LT (39 patients).

Study procedures and data collection
At Mount Sinai Hospital, transplant patient records are stored in an electronic medical record database using the programs OTTR (2009-2014) and Epic (from 2014 to present). All pretransplant candidates undergo a standardized psychosocial evaluation conducted by a social worker. These evaluations are documented in the electronic medical records using a standardized template. In this study, a total of 5 reviewers (including 3 psychology trainees, 1 medical student, and 1 internal medicine resident), after dedicated training, conducted chart reviews of the psychosocial evaluations, demographic information documented in the medical records, tacrolimus levels, and liver biopsy results. The reliability of data collection was monitored by a senior psychologist. Because data were collected from medical records and analyzed anonymously, the institutional review board of the Icahn School of Medicine at Mount Sinai Hospital approved a waiver of informed consent.

Measured variables and assessments
The following predictor variables were included in the medical record review (depicted here as they appear in the electronic medical record and as captured in the data collection): age at transplant (years), time since transplant (years), sex (female/male), race/ethnicity (White, Hispanic, Asian, Black, or other), education level (college or higher/no college), employment status (yes/no/retired), insurance status (Medicaid/Medicare/private), marital status (partnered/not partnered), psychiatric history (yes/no), substance abuse history (yes/no), and confirmed social support (yes/no).

The standardized psychosocial evaluation included an in-depth interview discussing medical, psychiatric, and social history. Every transplant candidate is first evaluated by a social worker. If a patient has an established psychiatric diagnosis, substance abuse history, history of behavioral problems, acute alcoholic hepatitis, fulminant hepatic failure from overdose, or acute liver failure with high risk of mortality, the patient is referred for psychiatric evaluation. These procedures differ slightly for patients with fulminant hepatic failure in that the informed consent process and psychosocial evaluation before transplant must be conducted in a more rapid fashion. Moreover, if a transplant candidate is critically ill (ie, intubated), collateral information is often obtained from family, next of kin, and the medical team, thus comprising the key data essential to the psychosocial assessment. Pretransplant predictor data were extracted from these standardized psychosocial evaluations depicted in the electronic medical records and coded uniformly for each participant.

The MLVI is an objective biomarker of adherence that has been validated in several studies of LT recipients.20,27 It is calculated for each patient as the standard deviation (SD) of a series of blood levels, obtained over time, resulting in a level reflecting the degree of variability between individual measures. A higher MLVI equals more variability between individual measures, thereby indicating less adherence. It encompasses a series of blood levels to display a pattern of medication ingestion rather than a single blood level that could be misleading.28 Medication level variability index values above 2 to 2.5 (greater than 2 SDs from the mean) have been robustly associated with rejection.²⁰

At our institution, tacrolimus trough levels are routinely obtained at least every 3 months after LT in the outpatient setting. We used all recorded levels in patient charts 1-year after LT onward to calculate the MLVI (we did not perform blood tests for research purposes). As long as a total of 3 tacrolimus levels were recorded since 1 year after LT until the study date, the patient was included in our analyses. Tacrolimus levels obtained during inpatient admis-sions were excluded because inpatient levels do not reflect patient adherence as medications are administered by a nurse or medical team. We ana-lyzed MLVI both as a continuous (higher MLVI = less adherence) and a categorical measure, using a predefined threshold of MLVI > 2.5 for the deter-mination of nonadherence, based on past analyses in different cohorts.^21,22 We used the MLVI cutoff of 2.5 instead of 2.0 as a more conservative threshold of measuring nonadherence.

Data on whether patients had an episode of biopsy-proven rejection (as determined by a pathologist) were collected and considered to be the clinical outcome most closely associated with nonadherence.²⁹ These measurements included all biopsies that were read as “definite” or “probable” rejection. For each patient, if there was at least 1 biopsy-proven episode of rejection in the study period, it was entered as a positive value (positive rejection) for the purpose of the main analysis. Rejection episodes were documented any time after 1-year post-LT. Repeated rejections in the same patient were not considered as different instances so as to avoid an overrep-resentation of a small number of patients who might have recurrent rejections in the final analyses.

Statistical analyses
All analyses were conducted using SPSS software (SPSS: An IBM Company, version 20, IBM Corporation, Armonk, NY, USA). Two-tailed P ≤ 0.05 was chosen as the level of significance. Descriptive statistics were used to characterize the sample. Preliminary analyses utilized independent sample t tests, analysis of variance, and chi-square tests to examine univariable differences in adherence measured continuously (MLVI) or categorically (MLVI threshold of 2.5) for the preidentified predictor variables. Multivariable analyses, specifically multiple regression for MLVI and logistical regression for MLVI threshold and rejection, were used to examine whether the set of variables obtained from medical record review predicted these outcomes. On the basis of the available 248 patients and 11 predictors, our study was powered to detect small to medium effects.³⁰

Results

Table 1 displays characteristics of the 248 patients included in the study. Mean (SD) recipient age at transplant was 56.1 (10.54) years, with 36% being female. Patients were racially diverse. The primary diagnoses reflected common causes of end-stage liver disease seen in adult LT patients. Surprisingly, 50% of transplanted patients had MLVI > 2.5, suggestive of clinically significant immunosup-pressant nonadherence, and 17% (41 patients) had biopsy-proven rejection. Patients with a rejection episode had higher mean (SD) MLVI levels than patients with no rejection: 3.42 (1.52) versus 2.70 (1.44) (t = 2.90, P < .01). The percentage of patients with MLVI values above 2.5 was also higher among those who had rejection at 71% versus 29% (chi-square = 8.29, P < .01).

Next, we examined whether the pre-LT predictor variables were associated with MLVI measured continuously or categorically. Table 2 displays the results of the univariable analyses. There was a small but significant correlation between time since transplant and MLVI (r = 0.15, P = .02). Regarding race and ethnicity, significant differences in MLVI were detected when MLVI was measured continuously and as a threshold (Table 3). Specifically, one-way analysis of variance (F = 3.10, P = .02) with Tukey post-hoc tests to identify significant group differences showed that patients who described themselves as Black had higher MLVI than Hispanic or Asian patients. Similarly, chi-square tests indicated omnibus differences in frequency of above-threshold MLVI by race/ethnicity (chi-square = 10.57, P = .03). Follow-up chi-square tests revealed the same pattern of results; the rates of nonadherence for Black recipients were significantly higher than those for Hispanic (chi-square = 7.39, P < .01) or Asian (chi-square = 4.51, P = .03) patients. Rates were also higher for White patients than for Hispanic patients (chi-square = 5.19, P = .02).

All predictors were then entered into linear regression and logistic regression models to determine whether the set of predictors together predicted outcomes; time since transplant was entered as a covariate. The linear regression to predict MLVI level was not significant (P = .36). Similarly, the logistic regression to predict above/below threshold MLVI was not significant (P = .40). Finally, the logistic regression to predict rejection was also not significant (P = .19). Together, we found that our predefined set of variables did not significantly predict any of our predefined outcomes in the study cohort.

Discussion

The present study aimed to evaluate the utility of pretransplant psychosocial measurements by investigating whether standard components are predictive of key posttransplant outcomes. To date, although such psychosocial evaluations are standard practice, their content and the emphasis placed on certain variables largely lack an empirical basis. Ultimately, we found no significant associations between standard psychosocial risk factors and post-LT nonadherence and biopsy-proven graft rejection, despite our inclusion of a large patient population with high rates of immunosuppressant nonad-herence. In univariable analyses, time since transplant and Black race were significantly associated with nonadherence. However, these associations did not hold true in logistic regression analyses. Other psychosocial risk factors, including history of psychiatric illness, substance abuse, lack of social support, among others, did not predict post-LT nonadherence or graft failure. Our negative findings challenge the adequacy of current practices that call for the careful assessment of such variables during the pre-LT evaluation. Of significance, high MLVI was again shown to be associated with graft rejection, thereby reconfirming this index as an important measure of clinically significant nonad-herence and highlighting its potential utility in future research and clinical endeavors. The results of this study support the validity of MLVI as an objective marker of clinically significant immunosuppressant nonadherence.

The high rate of nonadherence detected in this patient population likely improved our ability to examine its risk factors. In our sample, using an objective measure of adherence, about 50% of transplanted patients had MLVI greater than 2.5 (our predefined threshold for nonadherence). Immuno-suppressant nonadherence was higher in our cohort versus the reported rates of nonadherence, which range from 15% to 40% in the LT population.^1-3 One possible explanation for this result is that our study cohort comprised a more nonadherent patient population. This retrospective analysis did not require patient consent, thus avoiding the usual selection bias against participation of nonadherent patients in research.¹⁹ Another possibility is that nonadherence is underreported and inaccurately captured in other studies. It could also be that our predefined threshold was too low and that we should have used another (higher) threshold in this cohort; future studies could look at this issue to prospectively define a robust threshold. However, as long as rejection is associated with the threshold, as determined here, a sensitive rather than a too specific threshold is probably preferred when clinical consi-derations require targeting a “high-risk” population. Our data show that many patients (but not all) should be considered to be “at risk,” and a robust program that addresses nonadherence clinically will likely need to monitor a large number of patients.

Our study had the following relatively unique features: predefined hypotheses (rather than a descriptive design), a robust objective measure of adherence, and a sample reflective of “real-world” patients. Our sample size was adequately powered to detect roughly medium effect sizes; it is possible that a larger study would have identified potential relations between individual variables and nonadherence not seen here, but these relations in turn would be expected to be in the realm of small effect sizes and therefore may not be of clinical importance. Considering the complexity of nonadherence as a behavior, it is possible that a cumulative effect of several risks is the important predictor rather than individual risk factors. A “scoring” approach may thus be preferred.¹¹ Although this is an intriguing proposition, studies are needed to prove or refute this supposition. At present, individual risks are mostly (clinically) evaluated separately, and our results call that practice into question.

Although some transplant centers use structured approaches such as the Transplant Evaluation Rating Scale³¹ or the Psychosocial Assessment of Candidacy for Transplant,³² there is limited agreement regarding their clinical utility.¹⁰ Both scales have demonstrated comparable interrater reliability; however, their predictive validity has yet to be established despite over a decade or more of usage.^33,34 More recently, a new pretransplant screening tool, the Stanford Integrated Psychosocial Assessment for Trans-plantation (SIPAT), was developed with the objective of being a superior predictor of patient adherence and graft survival than other available measures or unstructured approaches.^35,36 Compared with the Transplant Evaluation Rating Scale and Psychosocial Assessment of Candidacy for Transplant, SIPAT is designed to minimize selection bias and improve the predictive validity of the psychosocial evaluation. It is meant to be a more standardized, objective, and evidence-based measure of factors that are shown to be closely associated with poor outcomes post-transplant (eg, social support and substance abuse). A recent evaluation of SIPAT showed that it was not a significant predictor of posttransplant adherence and was unable to predict the primary outcome measures of graft survival, but it may be able to predict rejection episodes and other secondary measures. Therefore, more research is needed before any of those proposed “cumulative” measures could be considered to be evidence based.

Limitations to our study include its retrospective design and a predefined set of predictors. We were unable to look at other more complex psychiatric metrics such as demoralization, optimism, and other psychologic constructs given the retrospective nature of the study and the fact that these constructs are not consistently recorded. We also did not investigate pre-LT evidence of nonadherence, either self-reported or identified more objectively. Although prospective studies are generally considered to be more robust, as discussed previously, they often result in a biased sample given the exclusion of the most nonadherent patients who are least likely to consent to research and adhere to follow-up. For the purpose of adherence research, a retrospective design may allow for a less biased examination of LT recipients. Still, since we only selected patients after transplant, our sample is “biased” toward those patients who were offered a transplant. It is possible that the patients with the most severe risk were simply not transplanted or did not even present to our center. Despite these issues, however, the fact that many patients did turn out to be nonadherent and did present with rejection suggests that our center’s selection criteria, as expected, did not result in an overly risk-free sample.

Medication nonadherence clearly affects post-LT outcomes. However, it remains unclear which risk factors influence nonadherence and whether any such risks should be targeted in the pre- and post-LT periods. Our findings suggest that previously reported relations between psychosocial predictors, nonadherence, and poor outcomes in LT recipients are not as robust as would be expected. These results call into question current pre-LT procedures, including the importance placed on certain aspects of the psychosocial evaluation. Continuous calcu-lation of the MLVI adherence assessment variable could play an important role in future studies aimed at identifying modifiable risk factors for nonadherence and graft failure. Additional studies are needed, including larger prospective studies investigating a comprehensive set of psychosocial variables and their predictive value in identifying nonadherence and poor post-LT outcomes. Ideally, these variables should be used to comprise a psychosocial “risk score” accounting for a cumulative effect of several risks identified with high-risk LT recipients. Until then, assessing individual psychosocial risk factors may be inadequate in identifying individuals at risk for posttransplant nonadherence and graft failure.

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