Maya Safarova

2.0k total citations · 1 hit paper
47 papers, 736 citations indexed

About

Maya Safarova is a scholar working on Surgery, Cardiology and Cardiovascular Medicine and Cancer Research. According to data from OpenAlex, Maya Safarova has authored 47 papers receiving a total of 736 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Surgery, 20 papers in Cardiology and Cardiovascular Medicine and 8 papers in Cancer Research. Recurrent topics in Maya Safarova's work include Lipoproteins and Cardiovascular Health (29 papers), Coronary Interventions and Diagnostics (8 papers) and Acute Myocardial Infarction Research (7 papers). Maya Safarova is often cited by papers focused on Lipoproteins and Cardiovascular Health (29 papers), Coronary Interventions and Diagnostics (8 papers) and Acute Myocardial Infarction Research (7 papers). Maya Safarova collaborates with scholars based in United States, Russia and Czechia. Maya Safarova's co-authors include М. В. Ежов, O. Afanasieva, S. Pokrovsky, Iftikhar J. Kullo, Iftikhar Kullo, Hongfang Liu, I. Adamová, G A Konovalov, Patrick M. Moriarty and Daniel Soffer and has published in prestigious journals such as New England Journal of Medicine, SHILAP Revista de lepidopterología and Journal of the American College of Cardiology.

In The Last Decade

Maya Safarova

41 papers receiving 723 citations

Hit Papers

A focused update to the 2... 2024 2026 2024 40 80 120

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Maya Safarova United States 15 522 211 167 159 116 47 736
Ilse K. Luirink Netherlands 13 673 1.3× 247 1.2× 260 1.6× 187 1.2× 141 1.2× 19 906
Nelva Mata Spain 14 786 1.5× 201 1.0× 294 1.8× 280 1.8× 146 1.3× 20 952
Michael K. Palmer United Kingdom 15 423 0.8× 125 0.6× 211 1.3× 100 0.6× 164 1.4× 20 786
Mogens Lytken Larsen Denmark 14 723 1.4× 286 1.4× 478 2.9× 191 1.2× 161 1.4× 32 1.0k
Issa Alesh United States 9 519 1.0× 367 1.7× 233 1.4× 91 0.6× 128 1.1× 21 982
Dick C.G. Basart Netherlands 8 819 1.6× 298 1.4× 459 2.7× 250 1.6× 177 1.5× 13 1.1k
Iris Kindt Netherlands 17 984 1.9× 232 1.1× 398 2.4× 327 2.1× 232 2.0× 27 1.2k
Angelique C.M. Jansen Netherlands 11 460 0.9× 136 0.6× 245 1.5× 181 1.1× 46 0.4× 12 644
Paul Ziajka United States 11 1.0k 2.0× 315 1.5× 499 3.0× 271 1.7× 228 2.0× 18 1.3k
Andrea Ruzza United States 17 830 1.6× 279 1.3× 145 0.9× 76 0.5× 223 1.9× 45 980

Countries citing papers authored by Maya Safarova

Since Specialization
Citations

This map shows the geographic impact of Maya Safarova's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maya Safarova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Safarova more than expected).

Fields of papers citing papers by Maya Safarova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maya Safarova. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maya Safarova. The network helps show where Maya Safarova may publish in the future.

Co-authorship network of co-authors of Maya Safarova

This figure shows the co-authorship network connecting the top 25 collaborators of Maya Safarova. A scholar is included among the top collaborators of Maya Safarova based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maya Safarova. Maya Safarova is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Vörös, Szilárd, Anthony Lozama, David Watson, et al.. (2025). FIRST DEMONSTRATION BY CARDIAC COMPUTED TOMOGRAPHY THAT LP(A) DRIVES CORONARY PLAQUE DEVELOPMENT IN PATIENTS WITH LOW APOB AND LOW LDL-C LEVELS. Journal of the American College of Cardiology. 85(12). 1832–1832.
3.
Mohananey, Divyanshu, et al.. (2024). Diagnosis and management of arrhythmogenic cardiomyopathy: a case report. European Heart Journal - Case Reports. 8(7). ytae321–ytae321. 1 indexed citations
4.
Koschinsky, Marlys L., Archna Bajaj, Michael B. Boffa, et al.. (2024). A focused update to the 2019 NLA scientific statement on use of lipoprotein(a) in clinical practice. Journal of clinical lipidology. 18(3). e308–e319. 127 indexed citations breakdown →
5.
Safarova, Maya & Patrick M. Moriarty. (2023). Lipoprotein Apheresis: Current Recommendations for Treating Familial Hypercholesterolemia and Elevated Lipoprotein(a). Current Atherosclerosis Reports. 25(7). 391–404. 27 indexed citations
6.
Saadatagah, Seyedmohammad, Ozan Dikilitas, Alexandra Miller, et al.. (2021). Genetic basis of hypercholesterolemia in adults. npj Genomic Medicine. 6(1). 28–28. 29 indexed citations
7.
Kumbamu, Ashok, et al.. (2018). A Clinical Decision Support Tool for Familial Hypercholesterolemia Based on Physician Input. SHILAP Revista de lepidopterología. 2(2). 103–112. 16 indexed citations
8.
Ежов, М. В., O. Afanasieva, Larisa Ilina, et al.. (2017). Association of lipoprotein(a) level with short- and long-term outcomes after CABG: The role of lipoprotein apheresis. Atherosclerosis Supplements. 30. 187–192. 18 indexed citations
9.
Safarova, Maya, Eric W. Klee, Linnea M. Baudhuin, et al.. (2017). Variability in assigning pathogenicity to incidental findings: insights from LDLR sequence linked to the electronic health record in 1013 individuals. European Journal of Human Genetics. 25(4). 410–415. 10 indexed citations
10.
Safarova, Maya & Iftikhar J. Kullo. (2016). My Approach to the Patient With Familial Hypercholesterolemia. Mayo Clinic Proceedings. 91(6). 770–786. 27 indexed citations
11.
Ежов, М. В., et al.. (2016). Lipoprotein(a) level as a predictor of long-term cardiovascular outcomes after percutaneous coronary interventions. Atherosclerosis. 252. e128–e129. 1 indexed citations
12.
Safarova, Maya, et al.. (2015). Lowering of lipoprotein(a) level under niacin treatment is dependent on apolipoprotein(a) phenotype. Atherosclerosis Supplements. 18. 53–58. 16 indexed citations
13.
Ежов, М. В., Maya Safarova, O. Afanasieva, et al.. (2015). Specific Lipoprotein(a) apheresis attenuates progression of carotid intima-media thickness in coronary heart disease patients with high lipoprotein(a) levels. Atherosclerosis Supplements. 18. 163–169. 24 indexed citations
14.
Sobenin, Igor A., Andrey V. Zhelankin, Tatiana V. Kirichenko, et al.. (2015). [Conventional and novel cardiovascular risk factors and predisposition to the development of atherosclerosis].. PubMed. 59(1). 4–11. 1 indexed citations
15.
Шахнович, Р. М., et al.. (2015). Comparison of Atherosclerotic Lesions in Patients With Acute Myocardial Infarction and Stable Angina Pectoris Using Intravascular Ultrasound. Kardiologiia. 7_2015(7). 5–13. 4 indexed citations
16.
Safarova, Maya, М. В. Ежов, O. Afanasieva, et al.. (2013). Effect of specific lipoprotein(a) apheresis on coronary atherosclerosis regression assessed by quantitative coronary angiography. Atherosclerosis Supplements. 14(1). 93–99. 95 indexed citations
17.
Safarova, Maya & М. В. Ежов. (2011). A Head Shot. New England Journal of Medicine. 365(26). 2519–2519. 1 indexed citations
18.
Safarova, Maya, et al.. (2011). [Pleiotropic effects of nicotinic acid therapy in men with coronary heart disease and elevated lipoprotein(a) levels].. PubMed. 51(5). 9–16. 10 indexed citations
19.
Ежов, М. В., et al.. (2010). Lipoprotein (a) polymorphism as a risk factor of coronary and carotid atherosclerosis and its complications in women. SHILAP Revista de lepidopterología. 2 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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