Mark Lachmann

578 total citations
17 papers, 208 citations indexed

About

Mark Lachmann is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Mark Lachmann has authored 17 papers receiving a total of 208 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cardiology and Cardiovascular Medicine, 8 papers in Pulmonary and Respiratory Medicine and 5 papers in Epidemiology. Recurrent topics in Mark Lachmann's work include Cardiac Valve Diseases and Treatments (11 papers), Cardiovascular Function and Risk Factors (7 papers) and Infective Endocarditis Diagnosis and Management (5 papers). Mark Lachmann is often cited by papers focused on Cardiac Valve Diseases and Treatments (11 papers), Cardiovascular Function and Risk Factors (7 papers) and Infective Endocarditis Diagnosis and Management (5 papers). Mark Lachmann collaborates with scholars based in Germany, Japan and Canada. Mark Lachmann's co-authors include Shinsuke Yuasa, Yoshikazu Kishino, Mai Kimura, Dai Kusumoto, Keiichi Fukuda, Tomohisa Seki, Karl‐Ludwig Laugwitz, Cedric Manlhiot, Moritz von Scheidt and Shelby Kutty and has published in prestigious journals such as European Heart Journal, Biomolecules and Journal of Clinical Medicine.

In The Last Decade

Mark Lachmann

13 papers receiving 205 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Lachmann Germany 8 79 55 41 39 35 17 208
Fridtjof Schiefenhövel Germany 7 21 0.3× 25 0.5× 27 0.7× 38 1.0× 15 0.4× 11 272
Ahmad Alimadadi United States 8 40 0.5× 135 2.5× 9 0.2× 19 0.5× 23 0.7× 16 270
Łukasz Michałowski Poland 7 22 0.3× 14 0.3× 31 0.8× 29 0.7× 105 3.0× 11 295
Alexey Pryalukhin Germany 9 7 0.1× 81 1.5× 16 0.4× 82 2.1× 67 1.9× 22 294
Jan Walter Benjamins Netherlands 7 69 0.9× 42 0.8× 26 0.6× 29 0.7× 47 1.3× 15 195
Eva Nyktari United Kingdom 10 223 2.8× 20 0.4× 33 0.8× 38 1.0× 116 3.3× 28 324
Sekeun Kim United States 6 43 0.5× 8 0.1× 28 0.7× 20 0.5× 67 1.9× 11 172
Carlos Martín-Isla Spain 9 106 1.3× 22 0.4× 101 2.5× 28 0.7× 219 6.3× 14 371
Marija Habijan Croatia 10 35 0.4× 7 0.1× 54 1.3× 9 0.2× 111 3.2× 29 249
Felicitas J. Detmer United States 13 42 0.5× 23 0.4× 29 0.7× 33 0.8× 21 0.6× 14 426

Countries citing papers authored by Mark Lachmann

Since Specialization
Citations

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

Fields of papers citing papers by Mark Lachmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Lachmann

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

All Works

17 of 17 papers shown
1.
Pellegrini, Costanza, Mark Lachmann, Christian Thilo, et al.. (2024). Volumen de calcificación de la válvula aórtica y su pronóstico en pacientes sometidos a implante percutáneo de válvula aórtica. Revista Española de Cardiología. 78(6). 507–518.
2.
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Joner, Michael, Erion Xhepa, N. Patrick Mayr, et al.. (2024). Outcomes after transcatheter mitral valve implantation in valve‐in‐valve, valve‐in‐ring, and valve‐in‐mitral annular calcification. Catheterization and Cardiovascular Interventions. 104(4). 837–852. 1 indexed citations
4.
Pellegrini, Carlo, Mark Lachmann, Christian Thilo, et al.. (2024). Aortic valve calcification volume and prognosis in patients undergoing transcatheter aortic valve implantation. Revista Española de Cardiología (English Edition). 78(6). 507–518.
5.
Fortmeier, Vera, Mark Lachmann, Maria Isabel Körber, et al.. (2023). Sex-Related Differences in Clinical Characteristics and Outcome Prediction Among Patients Undergoing Transcatheter Tricuspid Valve Intervention. JACC: Cardiovascular Interventions. 16(8). 909–923. 17 indexed citations
6.
Güldener, Ulrich, Thorsten Kessler, Moritz von Scheidt, et al.. (2023). Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography. Journal of Clinical Medicine. 12(8). 2941–2941. 6 indexed citations
7.
Lachmann, Mark, Daniel Rueckert, Tibor Schuster, et al.. (2022). Harnessing feature extraction capacities from a pre-trained convolutional neural network (VGG-16) for the unsupervised distinction of aortic outflow velocity profiles in patients with severe aortic stenosis. European Heart Journal - Digital Health. 3(2). 153–168. 9 indexed citations
8.
Eynde, Jef Van den, Mark Lachmann, Karl‐Ludwig Laugwitz, Cedric Manlhiot, & Shelby Kutty. (2022). Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends in Cardiovascular Medicine. 33(5). 265–271. 25 indexed citations
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10.
Fortmeier, Vera, Mark Lachmann, Maria Isabel Körber, et al.. (2022). Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence. JACC: Cardiovascular Interventions. 15(4). 381–394. 11 indexed citations
11.
Lachmann, Mark, Tibor Schuster, Erion Xhepa, et al.. (2021). Subphenotyping of Patients With Aortic Stenosis by Unsupervised Agglomerative Clustering of Echocardiographic and Hemodynamic Data. JACC: Cardiovascular Interventions. 14(19). 2127–2140. 30 indexed citations
12.
Vilne, Baiba, Dario Bongiovanni, Tilman Ziegler, et al.. (2021). Vascular Tissue Specific miRNA Profiles Reveal Novel Correlations with Risk Factors in Coronary Artery Disease. Biomolecules. 11(11). 1683–1683. 19 indexed citations
13.
14.
Fortmeier, Vera, Mark Lachmann, Muhammed Gerçek, et al.. (2021). Predicting procedural success in patients treated with Cardioband system for severe tricuspid regurgitation by employing a random forest algorithm. European Heart Journal. 42(Supplement_1).
15.
Kusumoto, Dai, Mark Lachmann, Shinsuke Yuasa, et al.. (2018). Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells. Stem Cell Reports. 10(6). 1687–1695. 69 indexed citations
16.
Lachmann, Mark, et al.. (2018). Progression of a coronary artery aneurysm with symptomatic compression of cardiac structures. European Heart Journal. 39(35). 3336–3336. 3 indexed citations
17.
Lachmann, Mark, Burak Salgin, Sebastian Kummer, et al.. (2015). Remission of congenital hyperinsulinism following conservative treatment: an exploratory study in patients with KATP channel mutations. Journal of Pediatric Endocrinology and Metabolism. 29(3). 281–7. 8 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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2026