Two approaches to modeling the risk of progressive atherosclerosis

Progressive or accelerated atherosclerosis is accompanied by unfavorable clinical outcomes. Studying and understanding this process and creating a personalized method for assessing the risk and prognosis of this disease are necessary to optimize approaches to treatment and prevention.
Aim: To compare two approaches to the creation of prognostic risk model of progressive atherosclerosis: non-linear regression model of logistic type and free cross-platform visual programming system Orange method.
Material and Methods. The retrospective cohort study included 202 patients with confirmed coronary heart disease: 147 men and 55 women. The mean age of the patients was 53.3 ± 7.16 years. Group 1 included patients with myocardial infarction or unstable stenocardia, emergency arterial stenting, stroke, peripheral arterial thrombosis, critical ischemia and lower extremity amputation within 2 years before inclusion in the study. Patients in the comparison group did not have these events. Predictive models of the influence of different studied parameters on the probability of rapid progression of atherosclerosis were built using factor and correlation analysis and free cross-platform Orange visual programming system.
Results. The authors’ suggested approaches to the evaluation of the risk of progressive atherosclerosis have a good prognostic accuracy (sensitivity 94.1, specificity 97.0 and accuracy 95.5 coefficients, respectively) for the regression model and 0,950 (95,0%) for the machine learning model. However, the construction of the regression model is a more complex procedure compared to the second approach, where the choice of informative indicators for the prediction model is made by Orange. Nevertheless, the above two approaches can successfully complement each other, allowing to build more accurate predictive risk models.
Conclusion. The proposed authors’ approaches to assessing the risk of progressive atherosclerosis have a good prognostic accuracy.

1. Shah P., Bajaj S., Virk H., Bikkina M., Shamoon F. Rapid progression of coronary atherosclerosis: a review. Thrombosis. 2015;2015:634983. DOI: 10.1155/2015/634983.

2. Li M., Ren C., Wu C., Li X., Li X., Mao W. Bioinformatics analysis reveals diagnostic markers and vital pathways involved in acute coronary syndrome. Cardiol. Res. Pract. 2020;2020:3162581. DOI: 10.1155/2020/3162581.

3. Kukharchuk V.V., Ezhov M.V., Sergienko I.V., Arabidze G.G., Bubnova M.G., Balakhonova T.V. et al. Diagnostics and correction of lipid metabolism disorders in order to prevent and treat of atherosclerosis Russian recommendations, VII revision. Moscow, 2020. Atherosclerosis and Dyslipidemia. 2020;38(1):7–42. (In Russ.). DOI: 10.34687/2219-8202.JAD.2020.01.0002.

4. Knuuti J., Wijns W., Saraste A., Capodanno D., Barbato E., Funck-Brentano C. et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes: The Task Force for the diagnosis and management of chronic coronary syndromes of the European Society of Cardiology (ESC). Eur. Heart J. 2020;41(3):407–477. DOI: 10.1093/eurheartj/ehz425.

5. Gulati M., Levy P.D., Mukherjee D., Amsterdam E., Bhatt D.L., Birtcher K.K. et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the evaluation and diagnosis of chest pain: Executive summary: A Report of the American College of Cardiology. American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2021;144(22):e368–e454. DOI: 10.1161/CIR.0000000000001029.

6. Thygesen K., Alpert J.S., Jaffe A.S., Chaitman B.R., Bax J.J., Morrow D.A. et al. ESC Scientific Document Group. Fourth universal definition of myocardial infarction (2018). Eur. Heart J. 2019;40(3):237–269. DOI: 10.1093/eurheartj/ehy462.

7. Landmesser U., Chapman M.J., Stock J.K., Amarenco P., Belch J.J.F., Borén J. et al. 2017 Update of ESC/EAS Task Force on practical clinical guidance for proprotein convertase subtilisin/kexin type 9 inhibition in patients with atherosclerotic cardiovascular disease or in familial hypercholesterolaemia. Eur. Heart J. 2017;39(14):1131–1143. DOI: 10.1093/eurheartj/ehx549.

8. Bilenko M.V., Vladimirov Ju.A., Khil’chenko A.V., Pavlova S.A. Patent Russian Federation, 2408019 , Int. Cl. G01N 33/50. Instant severity diagnosis of ischemic heart damage in patient with chronic heart disease and propensity for progressing atherosclerosis. 2009121616/15, date of filing: 08.06.2009, date of publication: 27.12.2010. URL: http://www.chemilum.ru/files/patent_ru-2408019.pdf (10.05.2023).

9. Aref’eva T.I., Balakhonova T.V., Krasnikova T.L., Noeva E.A., Potekhina A.V., Provatorov S.I. et al. Patent RU 2566288 C1, Int. Cl. G01N 33/53. Diagnostic technique for predisposition to atherosclerosis progression in patients with chronic ischemic heart disease as shown by peripheral blood interleukin-10 and interleukin-17 concentrations. Application: 2014141099/15, date of filing: 13.10.2014, date of publication: 20.10.2015. URL: https://patentimages.storage.googleapis.com/99/db/72/ea591c80020882/RU2566256C1.pdf (10.05.2023).

10. Aref’eva T.I., Balakhonova T.V., Krasnikova T.L., Noeva E.A., Potekhina A.V., Provatorov S.I. et al. Patent RU 2575791 C1, Int. Cl. G01N 33/50. Diagnostictechnique for disposition to atherosclerosis progression in patients with chronicischemic heart diseases by peripheral blood interleukin-10-producing lymphocyte count; Application: 2014141098/15, date of filing: 13.10.2014, date of publ.: 20.02.2016. URL: https://patentimages.storage.googleapis.com/d9/b2/ae/bd04c55b44d480/RU2575257C1.pdf (10.05.2023).

11. Ragino Yu.I. Unstable atherosclerotic plaque and its laboratory biochemical markers. Novosibirsk: Nauka; 2019:120. (In Russ.).

12. Obermeyer Z., Emanuel E.J. Predicting the future – big data, machine learning, and clinical medicine. New Engl. J. Med. 2016;375(13):1216–1219. DOI: 10.1056/NEJMp1606181.