Ramin Shadman

1.7k total citations
18 papers, 722 citations indexed

About

Ramin Shadman is a scholar working on Cardiology and Cardiovascular Medicine, Surgery and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ramin Shadman has authored 18 papers receiving a total of 722 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cardiology and Cardiovascular Medicine, 4 papers in Surgery and 3 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ramin Shadman's work include Cardiac pacing and defibrillation studies (11 papers), Heart Failure Treatment and Management (8 papers) and Cardiovascular Function and Risk Factors (8 papers). Ramin Shadman is often cited by papers focused on Cardiac pacing and defibrillation studies (11 papers), Heart Failure Treatment and Management (8 papers) and Cardiovascular Function and Risk Factors (8 papers). Ramin Shadman collaborates with scholars based in United States, Sweden and United Kingdom. Ramin Shadman's co-authors include Michael H. Criqui, Arnost Fronek, Julie O. Denenberg, Mary Mcdermott, Anthony Gamst, Warner P. Bundens, Wayne C. Levy, Matthew Allison, Karl Swedberg and Aldo P. Maggioni and has published in prestigious journals such as Circulation, Journal of the American College of Cardiology and Life Sciences.

In The Last Decade

Ramin Shadman

16 papers receiving 714 citations

Peers

Ramin Shadman
Michael A. Weber United States
Jason W. Greenberg United States
Janet E. Rush United States
Peter D. Cahill United States
Roberto J. Cubeddu United States
Periaswamy Velavan United Kingdom
Richard A. Moggio United States
Ramin Shadman
Citations per year, relative to Ramin Shadman Ramin Shadman (= 1×) peers Harbans S. Wasir

Countries citing papers authored by Ramin Shadman

Since Specialization
Citations

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

Fields of papers citing papers by Ramin Shadman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramin Shadman

This figure shows the co-authorship network connecting the top 25 collaborators of Ramin Shadman. A scholar is included among the top collaborators of Ramin Shadman 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 Ramin Shadman. Ramin Shadman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Shadman, Ramin, Jeanne E. Poole, Todd Dardas, et al.. (2024). Seattle proportional risk model in GISSI-HF: Estimated benefit of ICD in patients with EF less than 50%. American Heart Journal. 275. 35–44.
2.
Schrage, Benedikt, Lars H. Lund, Lina Benson, et al.. (2022). Predictors of primary prevention implantable cardioverter‐defibrillator use in heart failure with reduced ejection fraction: impact of the predicted risk of sudden cardiac death and all‐cause mortality. European Journal of Heart Failure. 24(7). 1212–1222. 11 indexed citations
3.
Bilchick, Kenneth C., Yongfei Wang, Jeptha P. Curtis, et al.. (2022). Survival Probability and Survival Benefit Associated With Primary Prevention Implantable Cardioverter‐Defibrillator Generator Changes. Journal of the American Heart Association. 11(13). e023743–e023743.
4.
Kohno, Takashi, Shun Kohsaka, Yasuyuki Shiraishi, et al.. (2020). Prediction of sudden cardiac death in Japanese heart failure patients: international validation of the Seattle Proportional Risk Model. EP Europace. 22(4). 588–597. 17 indexed citations
5.
Kristensen, Søren Lund, Wayne C. Levy, Ramin Shadman, et al.. (2019). Risk Models for Prediction of Implantable Cardioverter-Defibrillator Benefit. JACC Heart Failure. 7(8). 717–724. 23 indexed citations
6.
Bilchick, Kenneth C., Yongfei Wang, Jeptha P. Curtis, et al.. (2019). Modeling defibrillation benefit for survival among cardiac resynchronization therapy defibrillator recipients. American Heart Journal. 222. 93–104. 3 indexed citations
7.
Shiraishi, Yasuyuki, Shun Kohsaka, Toshiyuki Nagai, et al.. (2018). Validation and Recalibration of Seattle Heart Failure Model in Japanese Acute Heart Failure Patients. Journal of Cardiac Failure. 25(7). 561–567. 28 indexed citations
8.
Levy, Wayne C., Anne S. Hellkamp, Daniel B. Mark, et al.. (2018). Improving the Use of Primary Prevention Implantable Cardioverter-Defibrillators Therapy With Validated Patient-Centric Risk Estimates. JACC. Clinical electrophysiology. 4(8). 1089–1102. 12 indexed citations
9.
Bilchick, Kenneth C., Yongfei Wang, Alan Cheng, et al.. (2017). Seattle Heart Failure and Proportional Risk Models Predict Benefit From Implantable Cardioverter-Defibrillators. Journal of the American College of Cardiology. 69(21). 2606–2618. 62 indexed citations
10.
Levy, Wayne C., Yanhong Li, Shelby D. Reed, et al.. (2016). Does the Implantable Cardioverter-Defibrillator Benefit Vary With the Estimated Proportional Risk of Sudden Death in Heart Failure Patients?. JACC. Clinical electrophysiology. 3(3). 291–298. 29 indexed citations
11.
Shadman, Ramin, Jeanne E. Poole, Todd Dardas, et al.. (2015). A novel method to predict the proportional risk of sudden cardiac death in heart failure: Derivation of the Seattle Proportional Risk Model. Heart Rhythm. 12(10). 2069–2077. 69 indexed citations
12.
Shen, Albert, Steven S. Khan, Michael Jorgensen, et al.. (2014). GENDER DIFFERENCES IN PRESENTATION AND LONG-TERM OUTCOMES BY TYPE OF AORTIC DISSECTION IN A LARGE COMMUNITY BASED COHORT. Journal of the American College of Cardiology. 63(12). A2058–A2058. 1 indexed citations
13.
Rho, Robert W., Kristen K. Patton, Jeanne E. Poole, et al.. (2012). Important Differences in Mode of Death Between Men and Women With Heart Failure Who Would Qualify for a Primary Prevention Implantable Cardioverter-Defibrillator. Circulation. 126(20). 2402–2407. 52 indexed citations
14.
Shadman, Ramin, Jeanne E. Poole, Dariush Mozaffarian, et al.. (2011). Abstract 17819: Predicting the Proportional Risk of Sudden Cardiac Death in a Multicenter Heart Failure Cohort. Circulation. 124(suppl_21). 2 indexed citations
15.
Shadman, Ramin, Matthew Allison, & Michael H. Criqui. (2007). Glomerular Filtration Rate and N-Terminal Pro-Brain Natriuretic Peptide as Predictors of Cardiovascular Mortality in Vascular Patients. Journal of the American College of Cardiology. 49(22). 2172–2181. 21 indexed citations
16.
Aboyans, Victor, Michael H. Criqui, Mary Mcdermott, et al.. (2007). The Vital Prognosis of Subclavian Stenosis. Journal of the American College of Cardiology. 49(14). 1540–1545. 91 indexed citations
17.
Shadman, Ramin, Michael H. Criqui, Warner P. Bundens, et al.. (2004). Subclavian Artery Stenosis: Prevalence, Risk Factors, and Association With Cardiovascular Diseases. Journal of the American College of Cardiology. 44(3). 618–623. 257 indexed citations
18.
Moeckel, Gilbert, et al.. (2002). Organic osmolytes betaine, sorbitol and inositol are potent inhibitors of erythrocyte membrane ATPases. Life Sciences. 71(20). 2413–2424. 44 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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