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The Observer Effect in Liver Transplantation Exemplified by Unintended Consequences of Regulation and Selected Outcome Metrics

Since the 1984 National Organ Transplantation Act mandated transplant outcomes be monitored and openly reported, the United Network of Organ Sharing (UNOS) has been obligated to publicly report outcome metrics and provide regulatory oversight to transplant centers. Clinicians and researchers have previously asserted that regulation in general, and specific outcome metrics in particular, have unduly affected clinical decision-making behavior. It is our aim to demonstrate an example of this observer effect in solid organ transplantation. United Network of Organ Sharing regulatory agents and insurance companies regard 1-year posttransplant survival as a benchmark metric of outcomes. Accordingly, liver transplant programs have achieved steadily improving 1-year post-liver transplant survival over the last 3 decades from a 1-year survival in 1986 of 66% to over 92% in 2015 (P < .001). Despite this achievement, other important, but not regulated, metrics have shown little improvement over the past 30 years. Oneyear waitlist mortality (20% in 1988, 21% in 2013) and longterm survival of 1-year survivors (85% in 1987, 84% in 2011) have remained stable. Confounding the demonstration of a causal relationship, posttransplant outcomes are affected by the cumulative impact of the observer effect, a waning donor supply, sicker recipients, and the lack of novel immunosuppressants. Nonetheless, we believe we have identified an example of the observer effect in OPO (Organ Procurement Organization) reporting. The Organ Donation Breakthrough Collaborative in 2003 advocated assessment of OPO performance based on the rate of conversion of eligible donors to actual donors rather than population-based donor production. Shortly after, the number of reported eligible donors per million population dropped from 39.0 to 28.7 despite a near constant death rate in the United States (8.44 crude death rate per thousand population in 2003, 8.39 in 2011). Although the actual donor production did not significantly change (24.0 liver donors per million population in 2003 to 26.1 in 2011, P 1⁄4 .08), we found an increase in the use of marginal donors, including older (mean age 38.2 in 2003, 40.1 in 2011, P < .001), donation after cardiac death (2.5% in 2003, 12.7% in 2011, P < .001), and liver-alone donors (10.5% in 2003 vs 12.1% in 2011, P 1⁄4 .006; See Supplementary Figure 1). In striving to increase the donor conversion rate, the new metric of interest, donor production was maintained at the expense of using more marginal grafts. Additionally, these data suggest the reporting of eligible donors was influenced rather than the actual donor production. Perhaps potential donors unlikely to produce allografts were preferentially not declared to be eligible in response to the observer effect. Regulation creates the observer effect, influencing clinical behavior to favor the chosen outcome metric. Our objective was to highlight the existence of the observer effect, to promote further discussion about its influence on our chosen outcome metrics. Regulation and selected metrics to grade performance, although necessary, can have a profound influence on clinical behavior and should be implemented with extreme care and deliberation.