Novel method of analysis of risk of development of ischemic heart disease with the use of genomic and computer technologiesстатья
Статья опубликована в журнале из списка RSCI Web of Science
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 26 августа 2016 г.
Аннотация:Aim: to assess effectiveness of the use of modern methods of prognostication for assessment of risk of development of ischemic heart disease (IHD). Material and methods. We examined 131 patients with diagnosis of IHD verified by coronary angiography and 159 subjects of control group. Initial information on each patient included the following parameters: traditional risk factors, laboratory parameters, results of instrumental examination, genetic markers. We studied 29 polymorphisms in 27 genes which according to international databases were associated with IHD. Genotype was assessed as 2 models: dominant and recessive. For each patient we calculated individual genetic index as sum of present polymorphic markers with addition of data of familial anamnesis. The data obtained were analyzed with the «RECOGNITION» system which used for solution of prognostication problems main ap proaches and algorithms of t6he theory of recognition by precedents. Results. Accuracy of recognition varied from 70 to 75% with small number of traits and up to 90% on informative trait subsystems. The method «linear machine» showed the highest accuracy. The voting algorithm showed maximal accuracy of prognosis relative to some algorithms. In IHD prognostication most information systems comprised genetic markers, most significant of which was the genetic index representing sum of available polymorphic markers with addition of data of familial anamnesis. Conclusion. Analysis with the use of methods of recognition by precedents is a perspective technique for stratification of IHD risk and support of optimal decision making on prevention. The use of collectives of different methods of prognostication allows to increase accuracy of prognosis.