People in need of organs were, in the words of Henry Beecher, the Harvard committee member credited with writing the report, "stacked up waiting for suitable donors".
In short, the app will offer a "template" for those in need of organs to tell their story, and will allow potential donors to link directly to the transplant center for further information, or to share the story with their Facebook circle.
The concept is nonetheless a topic of active discussion, motivated by the suffering of patients in need of organs and exploitation of the victims of human trafficking.
As there is such a discrepancy between those in need of organs and those willing to donate, the author suggested standard monetary incentives for potential donors could potentially narrow the gap.
Accuracy of risk scores AIMS65 and GBS to predict need of organ dysfunction support was assessed by the area under the curve (AUC).
On multivariate analysis, ischemic cardiopathy, chronic kidney disease, raised urea and low hemoglobin were found to be independent predictors for need of organ dysfunction support in this population.
The four variables found in our study together showed more accuracy to predict need of organ dysfunction support (AUC, 0,911; 95% CI 0,873-0,949).
Thirty years of development in this field shows the potential impact of this field for eliminating the need of organ transplants altogether.
Risk scores AIMS65 and GBS were found to be both predictors for need of organ dysfunction support and AIMS65 had a higher accuracy in detecting these patients (AUC, 0,734; 95% CI 0,651-0,816) than GBS (AUC, 0,717; 95% CI 0,628-0,807).
