What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Disease
- Gene
The scientist’s investigation covers issues in Internal medicine, C-reactive protein, Endocrinology, Cardiology and Genome-wide association study.
He combines subjects such as Diabetes mellitus and Surgery with his study of Internal medicine.
His C-reactive protein study combines topics in areas such as Rosuvastatin Calcium, Disease and Pathogenesis.
His Cardiology study integrates concerns from other disciplines, such as Lipoprotein, Fibrinogen and Vascular disease.
He interconnects Genetic variation, Genetic association and Bioinformatics in the investigation of issues within Genome-wide association study.
In his research, Blood viscosity is intimately related to Stroke, which falls under the overarching field of Myocardial infarction.
His most cited work include:
- Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. (4538 citations)
- Rosuvastatin to Prevent Vascular Events in Men and Women With Elevated C-Reactive Protein (3523 citations)
- From Vulnerable Plaque to Vulnerable Patient A Call for New Definitions and Risk Assessment Strategies: Part I (2483 citations)
What are the main themes of his work throughout his whole career to date?
Wolfgang Koenig mainly investigates Internal medicine, Cardiology, Endocrinology, Myocardial infarction and C-reactive protein.
The Internal medicine study combines topics in areas such as Diabetes mellitus and Type 2 diabetes.
In his study, Risk factor is strongly linked to Surgery, which falls under the umbrella field of Cardiology.
His research on Myocardial infarction often connects related areas such as Stroke.
Research on Inflammation and Immunology is a part of his C-reactive protein study.
His studies in Hazard ratio integrate themes in fields like Quartile, Proportional hazards model and Cohort.
He most often published in these fields:
- Internal medicine (83.02%)
- Cardiology (30.76%)
- Endocrinology (27.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Internal medicine (83.02%)
- Hazard ratio (15.91%)
- Cohort study (14.37%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Internal medicine, Hazard ratio, Cohort study, Diabetes mellitus and Cohort.
His research integrates issues of Endocrinology and Cardiology in his study of Internal medicine.
His Coronary artery disease, Coronary heart disease, Troponin T and Atrial fibrillation study in the realm of Cardiology interacts with subjects such as Evolocumab.
His work in Hazard ratio addresses issues such as Prospective cohort study, which are connected to fields such as Body mass index.
He has researched Cohort study in several fields, including Disease and Cardiac troponin.
His Diabetes mellitus research is multidisciplinary, relying on both Odds ratio and Bioinformatics.
Between 2018 and 2021, his most popular works were:
- Efficacy and safety of statin therapy in older people: a meta-analysis of individual participant data from 28 randomised controlled trials (219 citations)
- Two Phase 3 Trials of Inclisiran in Patients With Elevated LDL Cholesterol (125 citations)
- Two Phase 3 Trials of Inclisiran in Patients With Elevated LDL Cholesterol (125 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Disease
- Gene
His main research concerns Internal medicine, Hazard ratio, Cohort study, Cohort and Myocardial infarction.
His studies deal with areas such as Diabetes mellitus and Cardiology as well as Internal medicine.
Wolfgang Koenig has included themes like Framingham Risk Score and Clinical trial in his Cardiology study.
His study in Hazard ratio is interdisciplinary in nature, drawing from both Meta-analysis and Prospective cohort study.
His Myocardial infarction research includes elements of Stroke, Time to treatment, Emergency medicine and Surrogate endpoint.
The Disease study which covers Renal function that intersects with Genetics.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
