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PD Dr. med. Paul Jasper Simon Boeddinghaus

Department of Clinical Research
Profiles & Affiliations

To improve the diagnosis and management of acute cardiovascular diseases

Dr. Jasper Boeddinghaus's work in the field of cardiac biomarkers focuses on their role in diagnosing and managing cardiovascular diseases. He investigates how specific biomarkers can aid in the early detection of heart conditions, improve risk stratification, and guide treatment decisions. His research emphasizes the clinical utility of these biomarkers in various settings, aiming to enhance patient outcomes by facilitating timely and appropriate interventions. His research in the field of AI and machine learning explores how these technologies can enhance diagnostic accuracy, improve risk prediction, and optimize treatment strategies for cardiovascular diseases. His work involves developing algorithms that analyze medical data, including imaging and biomarker information, to assist clinicians in decision-making. By leveraging AI and machine learning, Dr. Boeddinghaus aims to improve patient outcomes and streamline healthcare processes in cardiology.

Selected Publications

Boeddinghaus, Jasper, Doudesis, Dimitrios, Lopez-Ayala, Pedro, Lee, Kuan Ken, Koechlin, Luca, Wildi, Karin, Nestelberger, Thomas, Borer, Raphael, Miró, Òscar, Javier Martin-Sanchez, F., Strebel, Ivo, Giménez, Maria Rubini, Keller, Dagmar I., Christ, Michael, Bularga, Anda, Li, Ziwen, Ferry, Amy V., Tuck, Chris, Anand, Atul, et al. (2024). Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways. Circulation, 149, 1090–1101. https://doi.org/10.1161/CIRCULATIONAHA.123.066917

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Doudesis, Dimitrios, Lee, Kuan Ken, Boeddinghaus, Jasper, Bularga, Anda, Ferry, Amy V., Tuck, Chris, Lowry, Matthew T. H., Lopez-Ayala, Pedro, Nestelberger, Thomas, Koechlin, Luca, Bernabeu, Miguel O., Neubeck, Lis, Anand, Atul, Schulz, Karen, Apple, Fred S., Parsonage, William, Greenslade, Jaimi H., Cullen, Louise, Pickering, John W., et al. (2023). Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nature Medicine, 29, 1201–1210. https://doi.org/10.1038/s41591-023-02325-4

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Boeddinghaus, Jasper, Nestelberger, Thomas, Koechlin, Luca, Wussler, Desiree, Lopez-Ayala, Pedro, Walter, Joan Elias, Troester, Valentina, Ratmann, Paul David, Seidel, Funda, Zimmermann, Tobias, Badertscher, Patrick, Wildi, Karin, Rubini Giménez, Maria, Potlukova, Eliska, Strebel, Ivo, Freese, Michael, Miró, Òscar, Martin-Sanchez, F. Javier, Kawecki, Damian, et al. (2020). Early Diagnosis of Myocardial Infarction With Point-of-Care High-Sensitivity Cardiac Troponin I. Journal of the American College of Cardiology, 75(10), 1111–1124. https://doi.org/10.1016/j.jacc.2019.12.065

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Boeddinghaus, Jasper, Nestelberger, Thomas, Twerenbold, Raphael, Neumann, Johannes Tobias, Lindahl, Bertil, Giannitsis, Evangelos, Sörensen, Nils Arne, Badertscher, Patrick, Jann, Janina E., Wussler, Desiree, Puelacher, Christian, Rubini Giménez, Maria, Wildi, Karin, Strebel, Ivo, Du Fay de Lavallaz, Jeanne, Selman, Farah, Sabti, Zaid, Kozhuharov, Nikola, Potlukova, Eliska, et al. (2018). Impact of age on the performance of the ESC 0/1h-algorithms for early diagnosis of myocardial infarction. European heart journal, 39(42), 3780–3794. https://doi.org/10.1093/eurheartj/ehy514

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Boeddinghaus J, Nestelberger T, Twerenbold R, Wildi K, Badertscher P, Cupa J, Bürge T, Mächler P, Corbière S, Grimm K, Giménez MR, Puelacher C, Shrestha S, Flores Widmer D, Fuhrmann J, Hillinger P, Sabti Z, Honegger U, Schaerli N, et al. (2017). Direct Comparison of 4 Very Early Rule-Out Strategies for Acute Myocardial Infarction Using High-Sensitivity Cardiac Troponin I. Circulation, 135(17), 1597–1611. https://doi.org/10.1161/circulationaha.116.025661

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Selected Projects & Collaborations