Prediction of cardiovascular risk among male patients infected with human immunodeficiency virus in Vojvodina, Serbia: A single centre study

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Abstract

Introduction. Human immunodeficiency virus infection is a disease of the modern era and it is estimated that there are more than 30 million infected individuals worldwide. Although the major cause is still unclear, patients infected with human immunodeficiency virus are at higher risk of ardiovascular diseases by 61% compared to general population. Material and Methods. This study included 111 male patients infected with human immunodeficiency virus treated at the Clinic of Infectious Diseases, Novi Sad, Serbia from January 2008 to December 2018. Five cardiovascular risk scores were used: Data Collection on Adverse Events of Antihuman immunodeficiency virus Drugs, Framingham 10-year Heart Score, Framingham 5-year Heart Score, prediction algorithm for cardiovascular disease and atherosclerotic cardiovascular disease risk estimator, at the beginning of the treatment, whereas cardiovascular events were recorded during the following 10 years. Results. Data Collection on Adverse Events of Anti-human immunodeficiency virus Drugs, Framingham 10-year Heart Score, Framingham 5-year Heart Score, and prediction algorithm for cardiovascular disease are tools that can identify individuals infected with human immunodeficiency virus at cardiovascular risk with statistical significance. The prediction algorithm for cardiovascular disease provides superior risk estimation compared to other scores. The atherosclerotic cardiovascular disease risk estimator did not show to be a marker of cardiovascular risk prediction among this population of patients. Conlusion. The above mentioned cardiovascular risk prediction algorithms, developed for general population, and Data Collection on Adverse Events of Anti-human immunodeficiency virus Drugs score, specific for population infected with human immunodeficiency virus, allow accurate cardiovascular risk estimation. Until the development of more specific algorithms, these scores are adequate tools for identification of patients at risk, providing prevention measures and treatment of cardiovascular disease.

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