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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Classification, epidemiology and clinical subgrouping of antineutrophil cytoplasmic antibody (ANCA)-associated
Richard A Watts1, Alfred Mahr2, Aladdin J Mohammad3
1Department of Rheumatology, Ipswich Hospital and Norwich Medical School, University of East Anglia, Norwich, UK.
Abstract:
It is now 25 years since the first European studies on vasculitis--the anti-neutrophil cytoplasmic antibody (ANCA) standardization project. Over that period of time, there have been major developments in the classification of the vasculitides, which has permitted the conduct of high-quality epidemiology studies. Studying the epidemiology of rare diseases such as the ANCA-associated vasculitides (AAV) poses considerable challenges to epidemiologists. The first is the need for a clear definition of a case with good differentiation from similar disorders. The second is case capture. The vasculitides are rare, and therefore, a large population is required to determine the incidence and prevalence, and this poses questions of feasibility. A large population increases the risk of incomplete case detection but permits a reasonable number of cases to be collected in a practicable time frame, whereas a smaller population requires a much longer time frame to collect the necessary cases, which may also not be feasible. Statistical methods of capture-recapture analysis enable estimates to be made of the number of missing cases. The third is case ascertainment. The AAV are virtually always managed in secondary care, and therefore, hospital-based case ascertainment may be appropriate. Fourthly, the rarity of the conditions makes prospective case-control studies investigating risk factors difficult to conduct because the population size required to achieve statistical confidence is in excess of that which is readily available. Thus, much of the data on risk factors are derived from retrospective studies with inherent potential bias.
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