Secondary Logo

Lost in Translation

Converting Empirical Evidence to Organ Acceptance Decision-making

Schold, Jesse D., PhD1,2

doi: 10.1097/TP.0000000000002586
Commentaries
Free

1 Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.

2 Center for Populations Health Research, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.

Received 13 December 2018.

Accepted 16 December 2018.

The author declares no funding or conflicts of interest.

Correspondence: Jesse D. Schold, PhD, Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave, JJN3-01, Cleveland, OH 44195. (scholdj@ccf.org).

Implementation science is the study of methods to promote the adoption and integration of evidence-based practices, interventions, and policies into routine health care and public health settings.1 A long-term lament of researchers is the failure of empirical evidence to translate to practice, particularly in contexts that ostensibly seem feasible and beneficial to patient care. Even inconsistent or selective uptake of best evidenced-based practices may exacerbate disparities in care by individual healthcare facility or patient demographics. Alternatively, systematic integration of evidenced-based practices has potential to simultaneously improve health and attenuate disparities in care. There are a variety of barriers that may impede translation of evidence into practice that include feasibility, technology capacity, effective dissemination, and mixed perceptions of regimented guidelines. Further understanding of the mechanisms that facilitate implementation is important to promote best practices.

Three of the most robust epidemiological findings in kidney transplantation include as follows: transplantation is efficacious relative to dialysis, graft survival associated with the quality of deceased donor kidneys is highly variable, and increased time on dialysis is associated with diminished outcomes following transplantation.2-4 However, optimizing each of these factors simultaneously (ie, receiving the highest quality donor kidney with limited time on dialysis) is not always feasible, and as such, most patients and caregivers are left to balance an organ selection decision in a context in which accepting a particular offer may not be clearly ideal relative to waiting for a better (uncertain) donor offer to follow. There have been several prominent studies evaluating the efficacy of transplantation associated with donor kidneys with increased risk factors. Ojo et al5 formally demonstrated the survival benefit of transplantation with marginal deceased donor kidneys relative to remaining on the waiting list on dialysis using national data. Merion et al6 evaluated the benefits of expanded criteria donor organs and specifically evaluated circumstances (based on waiting time and patient characteristics) that were associated with improved survival with accepting a lower-quality organ relative to waiting for a better-quality option. Massie et al7 extended this concept with Kidney Donor Profile Index strata and with consideration of potential survival benefit associated with subsequent offers. Other studies have also attempted to isolate circumstances that optimize particular organ acceptance decisions with the general premise that most patients benefit from transplantation across quality strata, but select patient characteristics and regional factors may modify these decisions.8–10

The current study by Kilambi et al11 depicts a tree-based decision tool, which can provide timely information to inform organ offer decisions. The models incorporate patient and donor characteristics as well as transplant center and organ procurement characteristics to assess potential survival benefit associated with a given donor offer. Model output may help to refine objective risks and benefits and provide a tool for shared decision-making for patients and clinicians in circumstances in which rapid responses may be needed that have potential life-altering ramifications. One novel aspect of the study is to incorporate patient utilities, indicating potential personal valuations of dialysis versus transplant, into the algorithm to assess benefit. A potential challenge with this portion of the algorithm is that (a) utilities are not always easy to measure and unknown for individual patients and (b) the effects of the model significantly vary by the assumed utilities for treatment modalities, such that using a generic set of utilities may be problematic. Thus, although more complex, the analysis does suggest that more specific patient information may be needed to rigorously assess potential benefits associated with organ offer selection.

Overall, the study by Kilambi et al11 provides important results in a context that is highly affected by decision-making informed by a complex set of parameters. More broadly, the study highlights that efforts to translate research and empirical evidence into practical, tangible tools that can be introduced into the practice of transplantation are critically important with potential broad applications. Transplantation is often (appropriately) characterized as a rare healthcare context in the United States with comprehensive capture of patients and donors. Leveraging these data to directly impact clinical decision-making is important but is potentially underutilized. The challenges translating empirical evidence to practice is itself worthy of further study using many of the concepts of implementation science. Certainly, we know there are many influential factors that affect clinical decision-making, including selecting potential donor organs, which extend beyond objective criteria. The mode of disseminating risks and benefits is important, and the specific tools used to facilitate information to improve practice and better align decisions are important to evaluate. As is the case with Public Health Service high-risk donor organs, perceptions of risks, even with best empirical evidence available, may significantly affect decision-making and vary significantly by individual patient. Linguistic and cultural barriers influence the ability to translate evidence in the most appropriate manner to all patient populations. Certainly even among clinicians, consistent practice and incorporation of technology to facilitate empirical-based tools will inherently vary. Moreover, survival benefit per se may not be the sole consideration for decision-making and quality of life; economic factors, regulatory oversight, complication profile, and logistical factors ultimately may weigh heavily in combination with a pure mathematical formulation of the decision process.

Ultimately, implementation of models as described in the current study will require multi-faceted approaches to incorporate into routine practice. Results of this study reinforce that there is potential to improve upon current shared decision-making and increase survival benefit of transplantation and organ utilization. Leveraging the great wealth of data in transplantation has numerous potential utilities for patient care and policy. Efforts to translate the body of rigorous research studies in our field into practice using emerging technologies and practical tools will be critically important in the years to come.

Back to Top | Article Outline

REFERENCES

1. National Institutes of Health. Implementation science information and resources. 2018. Available at https://www.fic.nih.gov/ResearchTopics/Pages/ImplementationScience.aspx. Accessed December 13, 2018.
2. Meier-Kriesche HU, Schold JDThe impact of pretransplant dialysis on outcomes in renal transplantation. Semin Dial. 2005;18(6):499–504.
3. Schold JD, Kaplan B, Baliga RS, et alThe broad spectrum of quality in deceased donor kidneys. Am J Transplant. 2005;5(4 Pt 1):757–765.
4. Wolfe RA, Ashby VB, Milford EL, et alComparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341:1725–1730.
5. Ojo AO, Hanson JA, Meier-Kriesche H, et alSurvival in recipients of marginal cadaveric donor kidneys compared with other recipients and wait-listed transplant candidates. J Am Soc Nephrol. 2001;12(3):589–597.
6. Merion RM, Ashby VB, Wolfe RA, et alDeceased-donor characteristics and the survival benefit of kidney transplantation. JAMA. 2005;294(21):2726–2733.
7. Massie AB, Luo X, Chow EKH, et alSurvival benefit of primary deceased donor transplantation with high-KDPI kidneys. Am J Transplant. 2014;14(10):2310–2316.
8. Bae S, Massie AB, Thomas AG, et alWho can tolerate a marginal kidney? Predicting survival after deceased donor kidney transplant by donor-recipient combination. Am J Transplant. 2019;19:425–433.
9. Schold JD, Meier-Kriesche HUWhich renal transplant candidates should accept marginal kidneys in exchange for a shorter waiting time on dialysis? Clin J Am Soc Nephrol. 2006;1(3):532–538.
10. Wey A, Salkowski N, Kremers WK, et alA kidney offer acceptance decision tool to inform the decision to accept an offer or wait for a better kidney. Am J Transplant. 2018;18(4):897–906.
11. Kilambi VB, Bui K, Hazen GB, et alEvaluation of accepting kidneys of varying quality for transplantation or expedited placement with decision trees. Transplantation. 2019;103:980–989.
Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.