Pella Daniel, Toth Stefan, Paralic Jan, Gonsorcik Jozef, Fedacko Jan, Jarcuska Peter, Pella Dominik, Pella Zuzana, Sabol Frantisek, Jankajova Monika, Valocik Gabriel, Putrya Alina, Kirschová Andrea, Plachy Lukas, Rabajdova Miroslava, Hunavy Mikulas, Kafkova Bibiana, Doci Ivan, Timkova Silvia, Dvorožňáková Mariana, Babic Frantisek, Butka Peter, Dimunova Lucia, Marekova Maria, Paralicova Zuzana, Majernik Jaroslav, Luczy Jan, Janosik Jakub, Kmec Martin
2 Department of Cardiology, Faculty of Medicine, Pavol Jozef Safarik University and East Slovak Institute of Cardiovascular Diseases, Košice, Slovak Republic.
SLOVACRIN & Medical Science Park, Faculty of Medicine, Pavol Jozef Safarik University, Kosice, Slovak Republic.
Arch Med Sci. 2020 Sep 21;18(4):991-997. doi: 10.5114/aoms.2020.99156. eCollection 2022.
Currently, just a few major parameters are used for cardiovascular (CV) risk quantification to identify many of the high-risk subjects; however, they leave a lot of them with an underestimated level of CV risk which does not reflect the reality.
The submitted study design of the Kosice Selective Coronarography Multiple Risk (KSC MR) Study will use computer analysis of coronary angiography results of admitted patients along with broad patients' characteristics based on questionnaires, physical findings, laboratory and many other examinations.
Obtained data will undergo machine learning protocols with the aim of developing algorithms which will include all available parameters and accurately calculate the probability of coronary artery disease.
The KSC MR study results, if positive, could establisha base for development of proper software for revealing high-risk patients, as well as patients with suggested positive coronary angiography findings, based on the principles of personalised medicine.
目前，仅使用少数几个主要参数来量化心血管（CV）风险，以识别许多高危受试者；然而，仍有许多人的CV风险水平被低估，这并不能反映实际情况。
提交的科希策选择性冠状动脉造影多重风险（KSC MR）研究的设计将利用对入院患者冠状动脉造影结果的计算机分析，以及基于问卷、体格检查、实验室检查和许多其他检查得出的广泛患者特征。
获得的数据将采用机器学习方案，以开发算法，这些算法将纳入所有可用参数，并准确计算冠状动脉疾病的概率。
如果KSC MR研究结果呈阳性，可为基于个性化医疗原则开发用于识别高危患者以及冠状动脉造影结果呈阳性的患者的合适软件奠定基础。
