Sazzli Kasim

834 total citations
36 papers, 148 citations indexed

About

Sazzli Kasim is a scholar working on Cardiology and Cardiovascular Medicine, Surgery and Epidemiology. According to data from OpenAlex, Sazzli Kasim has authored 36 papers receiving a total of 148 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Cardiology and Cardiovascular Medicine, 12 papers in Surgery and 9 papers in Epidemiology. Recurrent topics in Sazzli Kasim's work include Lipoproteins and Cardiovascular Health (8 papers), Infective Endocarditis Diagnosis and Management (6 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (5 papers). Sazzli Kasim is often cited by papers focused on Lipoproteins and Cardiovascular Health (8 papers), Infective Endocarditis Diagnosis and Management (6 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (5 papers). Sazzli Kasim collaborates with scholars based in Malaysia, Australia and United Kingdom. Sazzli Kasim's co-authors include Khairul Shafiq Ibrahim, Wan Azman Wan Ahmad, Sorayya Malek, Rosli Mohd Ali, Hapizah Nawawi, Suraya Abdul-Razak, Zaliha Ismail, Azhari Rosman, Rohana Abdul Ghani and Noor Alicezah Mohd Kasim and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American College of Cardiology and PLoS ONE.

In The Last Decade

Sazzli Kasim

24 papers receiving 148 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sazzli Kasim Malaysia 6 106 44 27 26 23 36 148
Khairul Shafiq Ibrahim Malaysia 7 99 0.9× 20 0.5× 42 1.6× 36 1.4× 14 0.6× 31 159
Simrat Gill United Kingdom 6 246 2.3× 19 0.4× 25 0.9× 21 0.8× 28 1.2× 14 308
Jeong Cheon Choe South Korea 6 65 0.6× 29 0.7× 15 0.6× 12 0.5× 20 0.9× 24 124
Péter Perge Hungary 10 188 1.8× 26 0.6× 10 0.4× 12 0.5× 19 0.8× 38 254
Jan Budzianowski Poland 6 131 1.2× 19 0.4× 24 0.9× 28 1.1× 32 1.4× 19 248
Chiara Cappelletto Italy 10 238 2.2× 82 1.9× 11 0.4× 7 0.3× 33 1.4× 21 302
Luke T. Slater United Kingdom 8 115 1.1× 31 0.7× 64 2.4× 11 0.4× 55 2.4× 28 272
Pedro Cox‐Alomar United States 5 207 2.0× 62 1.4× 19 0.7× 17 0.7× 54 2.3× 10 260
Angelo Oliva Italy 7 128 1.2× 82 1.9× 6 0.2× 6 0.2× 28 1.2× 40 172
Keita Koseki Japan 10 236 2.2× 79 1.8× 11 0.4× 11 0.4× 41 1.8× 46 287

Countries citing papers authored by Sazzli Kasim

Since Specialization
Citations

This map shows the geographic impact of Sazzli Kasim'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 Sazzli Kasim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sazzli Kasim more than expected).

Fields of papers citing papers by Sazzli Kasim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sazzli Kasim. 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 Sazzli Kasim. The network helps show where Sazzli Kasim may publish in the future.

Co-authorship network of co-authors of Sazzli Kasim

This figure shows the co-authorship network connecting the top 25 collaborators of Sazzli Kasim. A scholar is included among the top collaborators of Sazzli Kasim 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 Sazzli Kasim. Sazzli Kasim 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.
Kasim, Sazzli, et al.. (2025). Validation of a personalized AI prompt generator (NExGEN-ChatGPT) for obesity management using fuzzy Delphi method. Biology Methods and Protocols. 10(1). bpaf085–bpaf085. 1 indexed citations
2.
Kasim, Sazzli, Nafiza Mat Nasir, Lihang Feng, et al.. (2025). Enhancing cardiovascular risk prediction in Asian populations: A machine learning approach integrated with digital health platforms. International Journal of Cardiology Cardiovascular Risk and Prevention. 27. 200509–200509.
3.
Mohamed-Yassin, Mohamed-Syarif, et al.. (2024). A case report of heterozygous familial hypercholesterolaemia with LDLR gene mutation complicated by premature coronary artery disease detected in primary care. European Heart Journal - Case Reports. 8(2). ytae039–ytae039. 1 indexed citations
4.
5.
Ahmad, Wan Azman Wan, Azhari Rosman, Sunita Bavanandan, et al.. (2023). Current Insights on Dyslipidaemia Management for Prevention of Atherosclerotic Cardiovascular Disease: A Malaysian Perspective. Malaysian Journal of Medical Sciences. 30(1). 67–81.
6.
Kasim, Noor Alicezah Mohd, et al.. (2022). Legacy effect of short-term alirocumab in familial hypercholesterolaemia: A case report.. PubMed. 44(3). 527–531. 1 indexed citations
7.
Kasim, Sazzli, et al.. (2022). In-hospital risk stratification algorithm of Asian elderly patients. Scientific Reports. 12(1). 17592–17592. 5 indexed citations
8.
Nazli, Sukma Azureen, Yung-An Chua, Noor Alicezah Mohd Kasim, et al.. (2022). Familial hypercholesterolaemia and coronary risk factors among patients with angiogram-proven premature coronary artery disease in an Asian cohort. PLoS ONE. 17(9). e0273896–e0273896. 5 indexed citations
9.
Kasim, Sazzli, et al.. (2022). In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. PLoS ONE. 17(12). e0278944–e0278944. 14 indexed citations
10.
Chua, Yung-An, Sukma Azureen Nazli, Azhari Rosman, et al.. (2022). Attainment of Low-Density Lipoprotein Cholesterol Targets and Prescribing Pattern of Lipid-Lowering Medications among Patients with Familial Hypercholesterolemia Attending Specialist Clinics. Journal of Atherosclerosis and Thrombosis. 30(10). 1317–1326.
11.
Malek, Sorayya, et al.. (2021). Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. PLoS ONE. 16(8). e0254894–e0254894. 48 indexed citations
12.
13.
Kasim, Sazzli, et al.. (2020). Tricuspid valve infective endocarditis following intravenous administration of traditional and complementary medicine (T&CM). SHILAP Revista de lepidopterología. 30(1). 47–50.
14.
Kasim, Sazzli, et al.. (2020). Acute heart failure – The ‘real’ Malaysian experience: An observational study from a single non-cardiac centre. SHILAP Revista de lepidopterología. 30(3). 218–224. 7 indexed citations
15.
Nazli, Sukma Azureen, Yung-An Chua, Noor Alicezah Mohd Kasim, et al.. (2019). 【論文摘要】Familial Hypercholesterolaemia among Patients with Coronary Angiogram-proven Premature Coronary Artery Disease. Systems and Computers in Japan. 1. 44–44. 1 indexed citations
16.
Ibrahim, Khairul Shafiq, et al.. (2019). TCTAP A-019 Paradoxical Elevation of Cholesterol Efflux Capacity During Acute Coronary Syndrome in Young Heterogenous Asians. Journal of the American College of Cardiology. 73(15). S9–S10.
17.
Rajadurai, Jeyamalar, Wan Azman Wan Ahmad, Hapizah Nawawi, et al.. (2018). Updates in the management of Dyslipidaemia in the high and very high risk individual for CV risk reduction.. PubMed. 73(3). 154–162. 4 indexed citations
18.
Lim, C.W., et al.. (2018). P1257Myocardial work - a novel technique of assessing myocardial efficiency in different causes of left ventricular hypertrophy. European Heart Journal. 39(suppl_1). 1 indexed citations
19.
Isa, Mohamad Rodi, Z. Ibrahim, Khairul Shafiq Ibrahim, et al.. (2017). Guideline Adherence to Prescription in Heart Failure Population in North Kuala Lumpur Region. International Journal of Cardiology. 249. S38–S38. 1 indexed citations
20.
Chua, Ngee Kiat, et al.. (2017). Impact of Cholesterol Levels on Acute Coronary Syndrome Mortality in North of Kuala Lumpur. International Journal of Cardiology. 249. S32–S32. 3 indexed citations

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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