Aize van der Sluis

510 total citations
18 papers, 281 citations indexed

About

Aize van der Sluis is a scholar working on Cardiology and Cardiovascular Medicine, Surgery and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Aize van der Sluis has authored 18 papers receiving a total of 281 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cardiology and Cardiovascular Medicine, 7 papers in Surgery and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Aize van der Sluis's work include Acute Myocardial Infarction Research (13 papers), Cardiac Imaging and Diagnostics (7 papers) and Coronary Interventions and Diagnostics (6 papers). Aize van der Sluis is often cited by papers focused on Acute Myocardial Infarction Research (13 papers), Cardiac Imaging and Diagnostics (7 papers) and Coronary Interventions and Diagnostics (6 papers). Aize van der Sluis collaborates with scholars based in Netherlands, United States and Sweden. Aize van der Sluis's co-authors include Arnoud W.J. van ‘t Hof, Rudolf T Tolsma, Jan Paul Ottervanger, Marion Fokkert, Robbert J. Slingerland, Erik Badings, Wouter Remkes, Vincent Roolvink, Jurriën M. ten Berg and A.T. Marcel Gosselink and has published in prestigious journals such as European Heart Journal, The American Journal of Cardiology and Acta Psychiatrica Scandinavica.

In The Last Decade

Aize van der Sluis

18 papers receiving 279 citations

Peers

Aize van der Sluis
Lewis Macken Australia
Dederia H. Nicholas United States
Valentin Suma United States
Georg Gaul Austria
David R. Wharton United States
Alejandro Cabello‐López Mexico
Daniel A. van Hoeijen Netherlands
Gianluca Mirra Italy
D V M Verhaert Netherlands
Gennaro Provenza Italy
Lewis Macken Australia
Aize van der Sluis
Citations per year, relative to Aize van der Sluis Aize van der Sluis (= 1×) peers Lewis Macken

Countries citing papers authored by Aize van der Sluis

Since Specialization
Citations

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

Fields of papers citing papers by Aize van der Sluis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aize van der Sluis. 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 Aize van der Sluis. The network helps show where Aize van der Sluis may publish in the future.

Co-authorship network of co-authors of Aize van der Sluis

This figure shows the co-authorship network connecting the top 25 collaborators of Aize van der Sluis. A scholar is included among the top collaborators of Aize van der Sluis 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 Aize van der Sluis. Aize van der Sluis 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.
Tolsma, Rudolf T, Marion Fokkert, Jan Paul Ottervanger, et al.. (2023). Consequences of Different cut-off Values for high-sensitivity Cardiac Troponin for Risk Stratification of Patients Suspected for NSTE-ACS With a Modified Heart Score. Future Cardiology. 19(10). 497–504. 3 indexed citations
2.
Tolsma, Rudolf T, Marion Fokkert, Erik Badings, et al.. (2021). Referral decisions based on a pre-hospital HEART score in suspected non-ST-elevation acute coronary syndrome: final results of the FamouS Triage study. European Heart Journal Acute Cardiovascular Care. 11(2). 160–169. 26 indexed citations
3.
Tolsma, Rudolf T, Marion Fokkert, Robbert J. Slingerland, et al.. (2021). Referral decisions based on a pre-hospital HEART score in suspected non-ST-elevation acute coronary syndrome: final results of the Famous Triage study. European Heart Journal. 42(Supplement_1). 3 indexed citations
4.
Tolsma, Rudolf T, Marion Fokkert, Erik Badings, et al.. (2020). Referral Decisions Based On a Prehospital Heart Score in Suspected non-ST-elevation Acute Coronary Syndrome: Design of the Famous Triage 3 Study. Future Cardiology. 16(4). 217–226. 8 indexed citations
5.
Badings, Erik, Marion Fokkert, Rudolf T Tolsma, et al.. (2020). Pre-hospital versus hospital acquired HEART score for risk classification of suspected non ST-elevation acute coronary syndrome. European Journal of Cardiovascular Nursing. 20(1). 40–47. 5 indexed citations
6.
Ottervanger, Jan Paul, Rudolf T Tolsma, Marion Fokkert, et al.. (2019). In-Hospital Healthcare Utilization, Outcomes, and Costs in Pre-Hospital-Adjudicated Low-Risk Chest-Pain Patients. Applied Health Economics and Health Policy. 17(6). 875–882. 13 indexed citations
7.
Fokkert, Marion, Rudolf T Tolsma, Aize van der Sluis, et al.. (2019). Accuracy of pre-hospital HEART score risk classification using point of care versus high sensitive troponin in suspected NSTE-ACS. The American Journal of Emergency Medicine. 38(8). 1616–1620. 18 indexed citations
8.
Ottervanger, Jan Paul, Rudolf T Tolsma, Marion Fokkert, et al.. (2019). P5247In-hospital healthcare utilization, outcomes and costs in pre-hospital adjudicated low-risk chest pain patients. European Heart Journal. 40(Supplement_1). 1 indexed citations
9.
Fokkert, Marion, Rudolf T Tolsma, Erik Badings, et al.. (2018). Value of Prehospital Troponin Assessment in Suspected Non-ST-Elevation Acute Coronary Syndrome. The American Journal of Cardiology. 122(10). 1610–1616. 27 indexed citations
10.
11.
Tolsma, Rudolf T, Marion Fokkert, Erik Badings, et al.. (2018). Pre-hospital risk assessment in suspected non-ST-elevation acute coronary syndrome: A prospective observational study. European Heart Journal Acute Cardiovascular Care. 9(1_suppl). 5–12. 38 indexed citations
12.
Tolsma, Rudolf T, Marion Fokkert, Jan Paul Ottervanger, et al.. (2017). 48The pre-hospital HEART score is a strong predictor of MACE in patients with suspected non-STEMI. European Heart Journal. 38(suppl_1). 4 indexed citations
13.
Fokkert, Marion, Robbert J. Slingerland, Rudolf T Tolsma, et al.. (2017). Fast assessment and management of chest pain patients without ST-elevation in the pre-hospital gateway (FamouS Triage): ruling out a myocardial infarction at home with the modified HEART score. European Heart Journal Acute Cardiovascular Care. 7(2). 102–110. 48 indexed citations
14.
Kennedy, Mark, Rik Hermanides, E. Kaplan, et al.. (2016). Fractional Flow Reserve–Guided Deferred Versus Complete Revascularization in Patients With Diabetes Mellitus. The American Journal of Cardiology. 118(9). 1293–1299. 17 indexed citations
15.
Kennedy, Mark, E. Kaplan, Rik Hermanides, et al.. (2016). Clinical outcomes of deferred revascularisation using fractional flow reserve in patients with and without diabetes mellitus. Cardiovascular Diabetology. 15(1). 100–100. 30 indexed citations
16.
17.
Oppen, Patricia van, Erik de Haan, Jos W. R. Twisk, et al.. (2007). Predictors of course in obsessive–compulsive disorder: logistic regression versus Cox regression for recurrent events. Acta Psychiatrica Scandinavica. 116(3). 201–210. 24 indexed citations
18.
Sleijfer, Dirk Th., et al.. (2002). How to diagnose cardiac tamponade.. PubMed. 60(8). 334–8. 2 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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