In trying to solve some of these conundrums, a great deal can be learned from the experience of heart transplantation, and, specifically, some of the answers provided by the Cardiac Transplant Research Database (CTRD). The CTRD was started in 1990 and gathers demographic data regarding cardiac transplant donors and recipients. The information obtained is then analyzed in regard to various events (death, rejection, infection) and their determinants. Rigorous analysis of the cardiac data has provided meaningful information about heart transplant outcomes; hopefully a similar strategy could one day be used to learn more about what works, and what does not work, in lung transplantation. For example, the CTRD uses data on > 1,700 transplant procedures from > 25 institutions to examine risk factors for death after primary transplantation. Recipient variables (such as age, hemodynamic status, and ventilator status) were analyzed, in addition to several donor variables (eg, the relationship between donor and recipient age, ischemic time, inotropic support, and diabetes) in the hopes of finding donor/recipient variables that in their interaction were associated with an increased risk for death. Depicted in Figure 1
is an example of how donor variables may interact, forming concentric circles that describe the probability of survival within 30 days following heart transplantation.9 One learns from these data that, as donor age and ischemic time increases, the probability of survival decreases. In this example, actual data-based evidence supports the assertion that a 50-year-old donor with 120 min of ischemic time has a 90% chance of surviving the first 30 days after a heart transplant. This information provides a powerful platform on which to base clinical decisions.