Development and external validation study combining existing models and recent data into an up-to-date prediction model for evaluating kidneys from older deceased donors for transplantation

With a rising demand for kidney transplantation, reliable pre-transplant assessment of organ quality becomes top priority. In clinical practice, physicians are regularly in doubt whether suboptimal kidney offers from older donors should be accepted. Here, we externally validate existing prediction models in a European population of older deceased donors, and subsequently developed and externally validated an adverse outcome prediction tool. Recipients of kidney grafts from deceased donors 50 years of age and older were included from the Netherlands Organ Transplant Registry (NOTR) and United States organ transplant registry from 2006-2018. The predicted adverse outcome was a composite of graft failure, death or chronic kidney disease stage 4 plus within one year after transplantation, modelled using logistic regression. Discrimination and calibration were assessed in internal, temporal and external validation. Seven existing models were validated with the same cohorts. The NOTR development cohort contained 2510 patients and 823 events. The temporal validation within NOTR had 837 patients and the external validation used 31987 patients in the United States organ transplant registry. Discrimination of our full adverse outcome model was moderate in external validation (C-statistic 0.63), though somewhat better than discrimination of the seven existing prediction models (average C-statistic 0.57). The model's calibration was highly accurate. Thus, since existing adverse outcome kidney graft survival models performed poorly in a population of older deceased donors, novel models were developed and externally validated, with maximum achievable performance in a population of older deceased kidney donors. These models could assist transplant clinicians in deciding whether to accept a kidney from an older donor.Clinical epidemiolog

A formal semantics for Z and the link between Z and the relational algebra

Revised version of CSNote 89/17, appeared in the Proceedings of the VDM'90 Symposium, Springer Verlag (1990

A biophysical typology for a spatially-explicit agri-environmental modelling framework

The Agri-Environmental Zonation (AEnZ) is a biophysical typology based on a recently available detailed database on organic carbon content of the topsoil of Europe, the Environmental Stratification (EnS) and an Agri-mask. The AEnZ is used within the integrated assessment framework of SEAMLESS. The basis for this typology is the Environmental Stratification of Europe (EnS) building mainly on climate and altitude characteristics. The 84 environmental strata were aggregated into 13 environmental zones (EnZs). The environmental zones were then combined with organic carbon topsoil data (OCTOP) to cover the wide range of agri-environmental diversity of Europe. The OCTOP content was selected as soil variable as it explained most of the variation in soils in Europe. The EnZs/OCTOP land units were combined with an Agri-mask representing major obstacles for farming resulting in the final AEnZ typology. The Agri-mask, which is based on CORINE Land Cover, soil, altitude and slope data, divides Europe into three zones with different agricultural potential (suited, unsuited and marginally suited). The AEnZ consists of 238 land types of which 82 classes are referred as suitable for agriculture (75.8% of EU27+). For the SEAMLESS framework two of the three dimensions of the Agri-Environmental Zonation have been used to build the spatial framework to link information on farming and biophysics. The spatial building block of SEAMLESS is thus the Seamzones, that is an overlay of the 13 environmental zones, the seven OCTOP classes and 270 administrative (NUTS2) regions. In total this results in 3,513 Seamzones that are used to structure the biophysical data as well as the data on farming across the EU27+