Graft Failure Due to Nonadherence among 150 Prospectively-Followed Kidney Transplant Recipients at 18 Years Post-transplant: Our Results and Review of the Literature

Competing Risks Analysis of Kidney Transplant Waitlist Outcomes: Two Important Statistical Perspectives

Modern competing risks analysis has 2 primary goals in clinical epidemiology as follows: (i) to maximize the clinician's knowledge of etiologic associations existing between potential predictor variables and various cause-specific outcomes via cause-specific hazard models, and (ii) to maximize the clinician's knowledge of noteworthy differences existing in cause-specific patient risk via cause-specific subdistribution hazard models (cumulative incidence functions [CIFs]). A perfect application exists in analyzing the following 4 distinct outcomes after listing for a deceased donor kidney transplant (DDKT): (i) receiving a DDKT, (ii) receiving a living donor kidney transplant (LDKT), (iii) waitlist removal due to patient mortality or a deteriorating medical condition, and (iv) waitlist removal due to other reasons. It is important to realize that obtaining a complete understanding of subdistribution hazard ratios (HRs) is simply not possible without first having knowledge of the multivariable relationships existing between the potential predictor variables and the cause-specific hazards (perspective #1), because the cause-specific hazards form the "building blocks" of CIFs. In addition, though we believe that a worthy and practical alternative to estimating the median waiting-time-to DDKT is to ask, "what is the conditional probability of the patient receiving a DDKT, given that he or she would not previously experience one of the competing events (known as the cause-specific conditional failure probability)," only an appropriate estimator of this conditional type of cumulative incidence should be used (perspective #2). One suggested estimator, the well-known "one minus Kaplan-Meier" approach (censoring competing events), simply does not represent any probability in the presence of competing risks and will almost always produce biased estimates (thus, it should never be used).

Adherence in transplantation

PURPOSE OF REVIEW

Adequately managing a complex medical regimen is paramount to the success of organ transplants. When patients stray from their prescribed medical regimen posttransplant, graft rejection, and death can occur. Predictors of adherence have been studied for many years, and various factors have been identified as contributing to adequate or poor adherence. Both demographic and personal characteristics have been associated with adherence behavior. However, recent developments, such as the COVID-19 pandemic, increased use of mobile health interventions, and use of medical biomarkers have affected the way adherence is measured and applied.

RECENT FINDINGS

The COVID-19 pandemic affected patients' comfort with accessing outpatient care and created a wider use of telehealth services. Measurement of adherence through serum lab levels continues to be reviewed as a potential objective assessment of adherence. Psychosocial factors continue to be identified as major contributors to nonadherence.

SUMMARY

Adherence to antirejection medication, lab work, appointments, and exercise and dietary instructions remains critical to the health of the transplant patient. It is critical that providers involved in the selection process and posttransplant treatment of these patients remain well informed of potential new factors affecting adherence.