The imbalance between the number of potential transplant recipients and available donors results in long waiting times and consequently a definite, albeit variable, mortality on the waiting list. In the absence of alternative therapies such as renal replacement therapy, 10% to 20% of all heart, liver, or lung transplant candidates die before an organ becomes available (1). It is therefore of utmost importance to determine, based on an evidence-based screening process, which patients with end-stage disease will benefit most from transplantation (TX).
Within this decision process, medical selection criteria are well established. There is growing awareness that psychosocial and behavioral factors are contributing to posttransplant outcomes as well. Consequently, TX guidelines state that careful pretransplant screening should not only comprise a comprehensive medical evaluation, but should also involve a thorough psychological assessment (2–17). However, these guidelines do not substantiate what is meant by a thorough assessment.
Our literature reviews testify to the relative absence of evidence-based guidelines for pretransplant psychosocial and behavioral screening (18, 19). Existing studies often use retrospective designs and focus on only one, or a limited number of pretransplant characteristics and their respective impact on outcome. Consequently, there is an urgent need for more prospective studies comprehensively assessing all relevant pretransplant factors.
The present study prospectively examined pretransplant psychosocial and behavioral predictors of posttransplant outcomes, that is, nonadherence with the immunosuppressive regimen, graft loss, late acute rejection and number of hospitalizations and hospitalization days. All relevant predictors were selected based on extensive literature reviews.
Design and Sample
This prospective study followed patients from pre-TX until 1 year posttransplant and consecutively included all heart, liver, and lung TX candidates listed for TX at the University Hospitals of Leuven (Belgium) between May 2001 and May 2003.
Patients had to be Dutch speaking, more than or equal to 18 years, on the waiting list for a first cadaveric TX, and capable to independently complete the questionnaires. Patients waiting for an urgent, living donor or multiorgan TX were excluded.
Variables and Measurement
Age, gender, marital status (i.e., married or living together vs. absence of a stable relationship), and educational level were assessed by self-report. Educational level was ranked as low (schooling <9 years), moderate (schooling 9–12 years) or high (schooling >12 years) (20).
Anxiety and Depression
The Hospital Anxiety and Depression Inventory is a valid 14-item self-report instrument assessing presence and severity of depressive (seven items) and anxiety symptoms (seven items) in nonpsychiatric medical outpatients (21). Items are scored on a four-point Likert-scale increasing in severity, with total scores ranging between 0 and 21 for each subscale. Cut-off points indicating severity of depressive or anxiety symptoms were as follows: 0 to 7 no symptoms; 8 to 10 mild; 11 to 14 moderate; and 15 to 21 severe symptoms (21).
The 60-item NEO-FFI assesses five personality traits referring to a dimensional taxonomy to understand normal personality functioning, being neuroticism (=proneness to psychological distress, excessive carvings, or urges), extraversion (=capacity for joy, need for stimulation), openness to experience (=toleration for and exploration of the unfamiliar), agreeableness (=one’s orientation along a continuum from compassion to antagonism in thoughts, feelings, and actions), and conscientiousness (=degree of organization, persistence, and motivation in goal-directed behavior; 12 items each; 22, 23). Item scores were summed and raw scores (range 12–60) for each personality trait were compared with norm data for gender and age and transformed in stanine- (or “standard-nine”) scores following a normal distribution (mean=5; SD=2).
Received Social Support
The Social Support Questionnaire is a self-report instrument adapted from previous research to assess received social support (24). Factor 1 (five items; score 5–25) was labeled as “general received practical and informational support.” Factor 2 (six items; score 6–36) was labeled “received specific support with medication taking.” Higher scores reflect more support.
Self-Reported Adherence With the Treatment Regimen
A self-report questionnaire based on the following was developed for this study.
- Adherence with medication taking: A single-item question assessed pre-TX adherence with medication taking: “In the last 14 days, how often did you not take a dose of your medication.” Patients answering “never” were considered to be adherent. All other scores (i.e., once, twice, three times, and four times or more) indicated nonadherence with medication taking. Adherence with timing of medication intake was not assessed pre-TX, as this dimension of medication taking was less important for pretransplant drugs.
- Smoking status: Patients reporting to be an active smoker were considered to be nonadherent with smoking cessation guidelines.
- Alcohol use: Amount of daily alcohol intake was explored as follows: “How many glasses of alcohol (beer, wine, and spirits) do you drinking per day.” Based on the National Institute of Health guidelines (25), following definitions of nonadherence were used: Liver transplant candidates answering more than “0” were considered to be nonadherent, as strict abstinence is required in patients with alcoholic cirrhosis and is strongly recommended in other liver TX candidates. For male heart and lung transplant candidates, all answers above “2,” and for female patients all answers above “1” were considered as nonadherence with alcohol guidelines (25).
Pre-TX Clinical Characteristics and Comorbidity
Clinical characteristics that were assessed for the three organ transplant groups separately are listed in Table 1.
Comorbidity refers to the total burden of illnesses across multiple potential conditions unrelated to the patient’s principal diagnosis. The Charlson Comorbidity Index consists of 19 categories defined as ICD-9 diagnoses assessing the burden of comorbidities (26), each associated with a weight from 1 to 6, based on 1-year mortality risk. The weighted values are summed. Higher scores indicate more severe comorbidity.
Self-Reported Nonadherence With the Immunosuppressive Regimen
Posttransplant medication taking was assessed by the same question included in the pretransplant assessment. A second question referring to regularity or timing of immunosuppressive intake was added as this aspect of medication behavior is also important to prevent poor outcomes: “During the last 14 days, how often did you not take your medication on time?” Patients answering “I always took them on time” were considered to be adherent. Patients answering “once,” “twice,” “thrice,” or “I never take them on time” were considered to be nonadherent with timing.
For all further analyses, the answers on both adherence questions were combined as evidence shows that both taking and timing adherence with immunosuppression are related to clinical outcome (27): More specifically, the following operational definition was used: patients being nonadherent with taking or timing were considered as nonadherers with immunosuppressive regimen (i.e., being labeled as nonadherence on at least one question). This admittedly stringent definition is based on the well-known underreporting of nonadherence using self-report and the evidence that already minor deviations from dosing schedule impact clinical outcome (27–29).
Graft Loss, Late Acute Rejection, and Number of Unscheduled Hospitalizations and Hospitalization Days
Graft loss, presence of acute rejection and number of unscheduled hospitalizations and unscheduled hospitalization days during the first year posttransplant were collected continuously by chart review.
Graft loss refers to a combined outcome variable of patient death, retransplantation or graft failure due to chronic rejection (e.g., patients on the waiting list for retransplantation).
Acute rejection in heart, liver, and lung TX recipients was defined using established classification systems (30–32). Only moderate or severe biopsy-proven acute rejections were considered (i.e., grade ≥II biopsies for all TX groups). For heart and lung transplantation, biopsies are obtained at fixed time points during the first year of posttransplant follow-up as part of their standardized follow-up protocol. For liver transplantation, a biopsy is only performed in case a rejection is suspected.
Both number of unscheduled hospitalizations and hospitalization days during the first year were used as proxies for resource utilization. A hospitalization for gastro-intestinal complaints, for instance, is an unscheduled hospitalization, whereas hospitalizations for routine biopsies are part of planned follow-up care.
Clinical events occurring during the first 6 months most likely are related to the transplant procedure and graft quality (33). For the purpose of this study, we were interested in events occurring late in the posttransplant period (6–12 months post-TX; 34). Outcome data are presented separately for both time periods.
Data Collection Procedure
The principal investigator contacted eligible patients by phone 2 weeks after being placed on the waiting list to prevent patient’s perception that unwillingness to participate could compromise their chances of being listed for TX. Patients were assured that no individual data would be disclosed to their treating physician and that their answers would have no impact on their pretransplant status (i.e., earlier TX or removal from the waiting list). The informed consent form and the coded questionnaires were sent to the patient’s home address, which returned the questionnaires in a prepaid envelope. The primary investigator contacted the patients again at 1 year post-TX reminding them that a new questionnaire was sent to their home. The hospital’s Ethical Review Board approved this study.
Descriptive statistics (mean, SD, median, interquartile range, and frequencies) for the demographic characteristics and outcome variables were used as appropriate. Presence of graft loss, late acute rejection, unscheduled hospitalizations, and hospitalization days are shown for the first 6 months posttransplant, and for the period of interest (i.e., events occurring between 6 months and 1 year posttransplant).
Multivariable binary logistic regression was used to determine the relationship between pretransplant psychosocial and behavioral predictors and posttransplant outcomes, that is, nonadherence with the immunosuppressive regimen at 1 year posttransplant (model 1), occurrence of late acute rejection (model 2), and graft loss (model 3). Analyses for graft loss were only performed in patients not having experienced graft loss in the first 6 months posttransplant. Pre-TX factors with a P less than 0.10 were entered in the multivariable models. All models were controlled for transplant type, age, and comorbidity. The models for late acute rejection and graft loss were also controlled for presence of late acute rejection in the first 6 months posttransplant, as evidence shows that this is an important predictor for developing future rejections or graft loss (35–37). Because of the relatively high correlation between anxiety and depression (rho=0.687), anxiety was omitted from the model to avoid multicollinearity, as literature shows that anxiety symptoms often co-occur with depressive symptoms (38).
Number of unscheduled hospitalization and hospitalization days were highly skewed. Because transformation of the data were not possible, we dichotomized both variables based on scores more than or equal to or less than percentile 75, and used multivariable binary logistic regression for both outcomes. Admittedly, this is an arbitrary cut-off, but allows us to capture at least those patients with a high number of unscheduled hospitalizations (i.e., ≥2 between 6 and 12 months posttransplant) and unscheduled hospitalization days (i.e., ≥6 between 6 and 12 months post-TX).
Statistical analysis was performed using SPSS for Windows version 15. Statistical significance was set at P less than 0.05.
Figure 1 provides an overview of number of excluded and included patients. One hundred eighty-six transplant candidates were available for prospective follow-up, of which 154 were transplanted. Analyses were performed in 141 patients who had completed one year follow-up, that is, 28 heart, 61 liver, and 52 lung transplant recipients.
Clinical characteristics are shown in Table 1. Demographic characteristics of the 141 patients are presented in Table 2. Age was significantly different between the three patient groups (P=0.036), with lung transplant patients tending to be younger than heart (posthoc Tukey test: P=0.098) and liver transplant patients (posthoc Tukey test: P=0.051).
Pretransplant Psychosocial and Behavioral Predictors of Posttransplant Adherence With the Immunosuppressive Regimen
For this outcome, data were available for 125 patients (two patients did not return their questionnaires at 1 year post-TX, and 14 patients died during the first post-TX year. Thus, patient survival was 96.4% in heart, 86.9% in liver and 90.6% in lung transplant recipients). Seventeen patients (13.6%) admitted having had problems with taking, whereas 45 patients (36%) reported having had problems with timing of immunosuppressive medication intake. When combining the scores of both taking and timing, 50 patients (40%) reported adherence problems.
The results of the binary logistic regression model for posttransplant nonadherence with the immunosuppressive regimen are presented in Table 3. Pre-TX medication nonadherence, a higher education, less received specific social support with medication taking and lower scores on the personality trait “conscientiousness” were all independent predictors of medication nonadherence at 1 year posttransplant.
Pretransplant Psychosocial and Behavioral Predictors of Posttransplant Clinical Outcomes
Post-TX clinical outcomes are presented in Table 4. Twelve percent of patients lost their graft and almost 35% experienced an acute rejection during the first 6 months post-TX.
With respect to the outcomes of interest, late graft loss (i.e., between 6 and 12 months post-TX) occurred in 6.4% of patients, and 10% of patients experienced an acute rejection more than 6 months posttransplant. The prevalence of late graft loss was not significantly different between the three groups, whereas a tendency towards more late acute rejections in liver and lung compared with heart transplant recipients.
A total of 40.8% of the patients were hospitalized for more than 6 months posttransplant, with a median number of hospitalizations of 0 (Q1=0; Q3=1) and a median number of hospitalization days of 0 (Q1=0; Q3=5.25). Twenty-three patients experienced more than or equal to two unscheduled hospitalizations (i.e., 17.7%≥percentile 75) and 32 patients had more than or equal to six unscheduled hospitalization days between 6 and 12 months post-TX (i.e., 24.6%≥percentile 75).
Lung TX patients experienced significant more unplanned hospitalizations and hospitalization days compared with liver transplant patients, who in turn experienced more hospitalizations and hospitalization days than heart TX patients.
Binary logistic regression revealed that being unmarried or not living in a stable relationship was the only significant predictor of graft loss between 6 and 12 months posttransplant (Wald=4.34; P=0.037: odds ratio=4.88; 95% CI [1.10; 21.7]).
Pretransplant self-reported nonadherence with medication taking was a significant predictor of late acute rejection in the regression model (Wald=4.89; P=0.027; odds ratio=4.37; 95% CI [1.18; 16.16]).
No pretransplant predictor could be identified for experiencing more than or equal to two unscheduled hospitalizations during 6 and 12 months post-TX. Yet, pre-TX nonadherence with medication taking tended to be a predictor of experiencing more than or equal to six unscheduled hospitalization days between 6 and 12 months posttransplant (P=0.087).
We prospectively demonstrated, for the first time, that pre-TX nonadherence with medication taking, lower received social support with medication taking, a higher educational level, and lower scores on the personality trait “conscientiousness” were all independent predictors of nonadherence with the immunosuppressive regimen at 1 year posttransplant.
Evidence from other chronic patient populations already showed that past nonadherence is a powerful predictor of future nonadherence (39–40). Yet, when Bunzel et al. (41) hypothesized that pre-TX nonadherence may be a predictor of posttransplant nonadherence, only one study in renal TX substantiated this statement (42); The latter study used a retrospective chart review and defined nonadherence as having at least one note in the clinical chart indicating pretransplant or posttransplant nonadherence with the therapeutic regimen. Our prospective, multivariable analysis confirms that pretransplant nonadherent patients have an eightfold higher risk of being nonadherent posttransplant. Hence, our data provide evidence to statements in transplant guidelines and substantiates our intuitive knowledge that pre-TX nonadherence is indeed a risk factor that should be screened for carefully during the pretransplant evaluation process.
Our data also confirm the evidence from the meta-analysis of DiMatteo (43) in that poor received specific social support with medication taking is a critical determinant of medication nonadherence.
In contrast to other findings showing a correlation between lower education and nonadherence (44), our data suggest that patients with a higher education are more at risk for post-TX medication nonadherence. Higher education may be associated with higher employment status resulting in a busier lifestyle, a known risk factor preventing patients from regular medication intake (40, 45). Alternatively, it is possible that higher educated patients are “decisive” nonadherers who prefer independent decision-making regarding their disease and treatment, a subcategory of nonadherers that has been defined by Greenstein et al. (29). Both busyness and decisiveness require further investigation.
Part of posttransplant nonadherence seems also to be determined by personality. Personality traits refer to a dimensional taxonomy to understand normal personality functioning (23), and do not necessarily reflect psychopathology. Patients with low conscientiousness may be criticized for their carelessness, negligence, and failure to stay within the lines, while patients with high conscientiousness are disciplined, organized, goal-oriented, and have a high need for structure, i.e., all characteristics that may help people in treatment adherence. A similar relationship between low conscientiousness and medication nonadherence has been shown by the group of Christensen (46, 47) and Stilley et al. (48).
Pretransplant Psychosocial and Behavioral Predictors of Posttransplant Clinical Outcome
Lack of partnership (i.e., being unmarried or not living together in a stable relationship) was a significant predictor of late graft loss. Bunzel and Wollenek (49) already indicated that heart transplant patients with an empathic and supportive partner had better surgical and post-TX outcomes compared with patients without such an active relational involvement. On the basis of the conceptual framework of Berkman et al. (50), we can hypothesize that patients without partnership have fewer social contacts and a lower network density (i.e., the extent to which members of the social network are connected to each other), which may ultimately result in poor outcomes.
Moreover, pretransplant medication nonadherence was the only predictor of late acute rejection. Although there is evidence that posttransplant nonadherence with the immunosuppressive regimen is a risk factor for subsequent late acute rejection (51, 52–54), we have now prospectively demonstrated for the first time that pretransplant nonadherence with medication taking predicts a higher incidence of late acute rejection and more hospitalization days. These findings need, of course, to be replicated in further trials.
First, although the selection of pre-TX risk factors was based on empirical and theoretical evidence (18, 19), it is possible that other psychosocial and behavioral factors may predict outcome as well, such as patient motivation, the presence of psychiatric disorders (e.g., personality disorders), or coping styles.
Second, we used self-report, with its known underreporting, to assess medication nonadherence, because electronic monitoring was not possible for budgetary reasons. Yet, several steps were taken to increase the likelihood of truthful answers, described in detail elsewhere (55). Despite the known underreport of nonadherence, 40% of our patients admitted to be nonadherent to their immunosuppressive therapy, which is high compared with the 20% to 37% rate found in the literature (56). Yet, in contrast to the literature, we assessed both taking and timing, and the self-reported problems in regularity of medication intake were mainly responsible for this high prevalence. These problems are seldom reported in review articles, as they mainly focus on taking adherence (56). Taking nonadherence was 13.6% in our study that is comparable with published prevalences.
The fact that only patients already being placed on the waiting list for TX were included may be another shortcoming. It is indeed possible that patients with severe psychosocial or behavioral problems were already excluded before becoming eligible for this study. However, literature shows that the number of patients excluded on mere psychosocial or behavioral grounds is low and approximates 5% of all patients screened for TX (15, 16). Candidates for heart transplantation were more likely to be rejected for these reasons than renal or liver transplant candidates (15). To check for a potential selection bias, we reviewed the medical files of all patients newly admitted to the heart TX unit for pretransplant screening between July 1 and December 31, 2007 (unpublished pilot data). We were particularly interested in the number of patients excluded from TX based on psychosocial or behavioral contraindications. Two of 32 patients screened (6.3%) were did not report heart TX because of active smoking at the time of assessment. Another patient (3.1%) had several relative medical contraindications together with poor social support, known nonadherence, and poor understanding of his disease status; this combination was deemed too compromising. These numbers are in line with those published in the literature, so selection bias has probably not heavily influenced our results.
Next, we excluded patients who were on the high urgency list at the time of eligibility, which might have caused selection bias. Given the criticality of their medical condition; however, a large portion of HU patients are usually not able to complete the self-report questionnaires. Future studies could use collateral report from the family or primary care physician, and chart review to evaluate which psychosocial problems predictive for poor posttransplant outcomes were present before having been place on the waiting list.
Similarly, patients on the waiting list for multiorgan transplantation were excluded, which may affect the external validity of our study. Further research is needed including patients on the high urgency list, and those in need of multiple organs.
Finally, the sample size is rather small. Posthoc power calculation, based on the hypothesis that patients showing nonadherence with medication taking before TX, have a twice as high risk to be nonadherent with posttransplant immunosuppression, revealed that 125 patients with completed follow-up are needed to achieve a power of 80% at P less than 0.05 (two-tailed Fischer Exact test for independent population proportions).
This prospective study is the first to provide an evidence base for pre-TX psychosocial and behavioral screening. Its results can be embedded in clinical practice by focusing on the identified risk factors for posttransplant nonadherence with immunosuppression and clinical outcome. More specifically, candidates for heart, liver and lung TX should be screened for conscientiousness, presence of received specific support with medication taking and level of pretransplant adherence with the therapeutic regimen. Patients with a higher educational level or with poor social networks should also be considered at risk. This evidence should be used in the first place to develop pretransplant interventions targeting on modifiable risk factors. Maximal interventional efforts should be implemented before not reporting transplantation to a patient. Yet, randomized controlled clinical trials testing the effectiveness of interventions are currently lacking and merit further investigation.
Altogether, the confirmation of these results in larger samples, other transplant types and transplant centers from different health care systems will stimulate the development of standardized psychosocial and behavioral screening protocols and will help selecting TX candidates in an ethically justified way.
1. Smits JM, Deng MC, Hummel M, et al. A prognostic model for predicting waiting-list mortality for a total national cohort of adult heart-transplant candidates. Transplantation
2003; 76: 1185.
2. Yu AS, Keeffe EB. Patient selection criteria for liver transplantation. Minerva Chir
2003; 58: 635.
3. Yu AD, Garrity ER. Lung transplantation. Recipient selection. Chest Surg Clin N Am
2003; 13: 405.
4. Desai NM, Mange KC, Crawford MD, et al. Predicting outcome after liver transplantation: Utility of the model for end-stage liver disease and a newly derived discrimination function. Transplantation
2004; 77: 99.
5. Botero RC, Lucey MR. Organ allocation: Model for end-stage liver disease, Child-Turcotte-Pugh, Mayo risk score, or something else. Clin Liver Dis
2003; 7: 715.
6. Thuluvath PJ, Yoo HY, Thompson RE. A model to predict survival at one month, one year, and five years after liver transplantation based on pretransplant clinical characteristics. Liver Transpl
2003; 9: 527.
7. van den Hout WB, Smits JM, Deng MC, et al. The heart-allocation simulation model: A tool for comparison of transplantation allocation policies. Transplantation
2003; 76: 1492.
8. Johnson FL. Heart transplantation. An update and review. Minerva Cardioangiol
2003; 51: 245.
9. Cimato TR, Jessup M. Recipient selection in cardiac transplantation: Contraindications and risk factors for mortality. J Heart Lung Transplant
2002; 21: 1161.
10. Deng MC. Cardiac transplantation. Heart
2002; 87: 177.
11. Glanville AR, Estenne M. Indications, patient selection and timing of referral for lung transplantation. Eur Respir J
2003; 22: 845.
12. Ochoa LL, Richardson GW. The current status of lung transplantation: A nursing perspective. AACN Clin Issues
1999; 10: 229.
13. Steinman TI, Becker BN, Frost AE, et al. Guidelines for the referral and management of patients eligible for solid organ transplantation. Transplantation
2001; 71: 1189.
14. Maurer JR, Frost AE, Estenne M, et al. International guidelines for the selection of lung transplant candidates. The International Society for Heart and Lung Transplantation, the American Thoracic Society, the American Society of Transplant Physicians, the European Respiratory Society. Transplantation
1998; 66: 951.
15. Corley MC, Westerberg N, Elswick RK Jr, et al. Rationing organs using psychosocial
and lifestyle criteria. Res Nurs Health
1998; 21: 327.
16. Levenson JL, Olbrisch ME. Psychosocial
evaluation of organ transplant candidates. A comparative survey of process, criteria, and outcomes in heart, liver, and kidney transplantation. Psychosomatics
1993; 34: 314.
17. Olbrisch ME, Levenson JL. Psychosocial
assessment of organ transplant candidates. Current status of methodological and philosophical issues. Psychosomatics
1995; 36: 236.
18. Dobbels F, De Geest S, Cleemput I, et al. Psychosocial
and behavioral selection criteria for solid organ transplantation. Prog Transplant
2001; 11: 121.
19. Dobbels F, Verleden G, Dupont L, et al. To transplant or not? The importance of psychosocial
and behavioral factors before lung transplantation. Chron Respir Dis
2006; 3: 39.
20. Appel SJ, Harrell JS, Deng S. Racial and socioeconomic differences in risk factors for cardiovascular disease among Southern rural women. Nurs Res
2002; 51: 140.
21. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand
1983; 67: 361.
22. Hoekstra HA, Ormel J, de Fruyt F. Neo Persoonlijkheidsvragenlijsten: NEO-PI-R en NEO-FFI (handleiding) [Personality Questionnaires: NEO-PI-R and NEO-FFI: manual]. Swets Test Services. Swets and Zeitlinger BV, Lisse, The Netherlands, 1996.
23. Costa PT, Widiger TA. Personality disorders and the five-factor model of personality. Washington, DC, American Psychological Association 1994.
24. Deschamps AE, De Graeve VD, van Wijngaerden E, et al. Prevalence and correlates of nonadherence to antiretroviral therapy in a population of HIV patients using medication event monitoring system. AIDS Patient Care STDS
2004; 18: 644.
25. World Health Organization. The World Health Report: 2002: Reducing risks, promoting healthy life. Geneva, World Health Organization 2002.
26. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis
1987; 40: 373.
27. De Geest S, Abraham I, Moons P, et al. Late acute rejection and subclinical noncompliance with cyclosporine therapy in heart transplant recipients. J Heart Lung Transplant
1998; 17: 854.
28. De Geest S, Abraham I, Dunbar-Jacob J. Measuring transplant patients’ compliance with immunosuppressive therapy. West J Nurs Res
1996; 18: 595.
29. Greenstein S, Siegal B. Compliance and noncompliance in patients with a functioning renal transplant: A multicenter study. Transplantation
1998; 66: 1718.
30. Cooper JD, Billingham M, Egan T, et al. A working formulation for the standardization of nomenclature and for clinical staging of chronic dysfunction in lung allografts. International Society for Heart and Lung Transplantation. J Heart Lung Transplant
1993; 12: 713.
31. International Expert Panel. Banff schema for grading liver allograft rejection: An international consensus document. Hepatology
1997; 25: 658.
32. Billingham ME, Cary NR, Hammond ME, et al. A working formulation for the standardization of nomenclature in the diagnosis of heart and lung rejection: Heart Rejection Study Group. The International Society for Heart Transplantation. J Heart Transplant
1990; 9: 587.
33. Lindenfeld J, Miller GG, Shakar SF, et al. Drug therapy in the heart transplant recipient. I. Cardiac rejection and immunosuppressive drugs. Circulation
2004; 110: 3734.
34. Desmyttere A, Dobbels F, Cleemput I, et al. Noncompliance in organ transplantation: Is it worth worrying about? Acta Gastroenterol Belg
2005; 68: 437.
35. Florman S, Schiano T, Kim L, et al. The incidence and significance of late cellular rejection (>1000 days) after liver transplantation. Clin Transplant
2004; 18: 152.
36. Kirklin JK. Is biopsy-proven cellular rejection an important clinical consideration in heart transplantation? Curr Opin Cardiol
2005; 20: 127.
37. Hachem RR, Trulock EP. Bronchiolitis obliterans syndrome: Pathogenesis and management. Semin Thorac Cardiovasc Surg
2004; 16: 350.
38. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC, American Psychiatric Association 1994.
39. Dunbar-Jacob J, Sereika S, Rohay JM, et al. Electronic methods of assessing adherence to medical regimens. In: Krantz D, Baum A, eds. Technologies and methods in behavioral medicine. Mahwah, NJ, Lawrence Erlbaum Associates 1995.
40. Sabate E. World Health Organization Report: Adherence to long-term therapies. Evidence for action. Switzerland, World Health Organization 2003.
41. Bunzel B, Laederach-Hofmann K. Solid organ transplantation: Are there predictors for posttransplant noncompliance? A literature overview. Transplantation
2000; 70: 711.
42. Douglas S, Blixen C, Bartucci MR. Relationship between pretransplant noncompliance and posttransplant outcomes in renal transplant recipients. J Transplant Coord
1996; 6: 53.
43. DiMatteo MR. Social support and patient adherence to medical treatment: A meta-analysis. Health Psychol
2004; 23: 207.
44. DiMatteo MR. Variations in patients’ adherence to medical recommendations. A quantitative review of 50 years of research. Med Care
2004; 42: 200.
45. Park DC, Hertzog C, Leventhal H, et al. Medication adherence in rheumatoid arthritis patients: Older is wiser. J Am Geriatr Soc
1999; 47: 172.
46. Christensen AJ, Smith TW. Personality and patient adherence: Correlates of the five-factor model in renal dialysis. J Behav Med
1995; 18: 305.
47. Wiebe JS, Christensen AJ. Health beliefs, personality and adherence in hemodialysis patients: An interactional perspective. Ann Behav Med
1997; 19: 30.
48. Stilley CS, Sereika S, Muldoon MF, et al. Psychological and cognitive function: Predictors of adherence with cholesterol lowering treatment. Ann Behav Med
2004; 27: 117.
49. Bunzel B, Wollenek G. Heart transplantation: Are there psychosocial
predictors for clinical success of surgery? Thorac Cardiovasc Surg
1994; 42: 103.
50. Berkman LF, Glass T, Brissette I, et al. From social integration to health: Durkheim in the new millennium. Soc Sci Med
2000; 51: 843.
51. Dobbels F, De Geest S, Van Cleemput J, et al. Effect of late medication non-compliance on outcome after heart transplantation: A 5-year follow-up. J Heart Lung Transplant
2004; 23: 1245.
52. Dew MA, Kormos RL, Roth LH, et al. Early post-transplant medical compliance and mental health predict physical morbidity and mortality one to three years after heart transplantation. J Heart Lung Transplant
1999; 18: 549.
53. Nevins TE, Kruse L, Skeans MA, et al. The natural history of azathioprine compliance after renal transplantation. Kidney Int
2001; 60: 1565.
54. Vlaminck H, Maes B, Evers G, et al. Prospective study on late consequences of subclinical non-compliance with immunosuppressive therapy in renal transplant patients. Am J Transplant
2004; 4: 1509.
55. Dobbels F, Vanhaecke J, Desmyttere A, et al. Prevalence and correlates of self-reported pre-transplant non-adherence with medication in heart, liver and lung transplant candidates. Transplantation
2005; 79: 1588.
56. Dew MA, DiMartini AF, DeVito Dabbs A, et al. Rates and risk factors for nonadherence to the medical regimen after solid organ transplantation. Transplantation
2007; 83: 858.