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960 Publications visible to you, out of a total of 960
Transcriptional states of CAR-T infusion relate to neurotoxicity - lessons from high-resolution single-cell SOM expression portraying.
imSAVAR - Immune safety avatar: nonclinical mimicking of the immune system effects of immunomodulatory therapies

Abstract (Expand)

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular … data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient's infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Authors: H. Loeffler-Wirth, M. Rade, A. Arakelyan, M. Kreuz, M. Loeffler, U. Koehl, K. Reiche, H. Binder

Date Published: 17th Oct 2022

Publication Type: Journal article

Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data … then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications. Clinical data structures, patient cohorts, and clinical protocols may be highly biased among hospitals such that sampling of representative learning datasets to learn ML models remains a challenge. As ML models exhibit poor predictive performance over data ranges sparsely or not covered by the learning dataset, in this study, we propose a novel method to assess their generalization capability among different hospitals based on the convex hull (CH) overlap between multivariate datasets. To reduce dimensionality effects, we used a two-step approach. First, CH analysis was applied to find mean CH coverage between each of the two datasets, resulting in an upper bound of the prediction range. Second, 4 types of ML models were trained to classify the origin of a dataset (i.e., from which hospital) and to estimate differences in datasets with respect to underlying distributions. To demonstrate the applicability of our method, we used 4 critical-care patient datasets from different hospitals in Germany and USA. We estimated the similarity of these populations and investigated whether ML models developed on one dataset can be reliably applied to another one. We show that the strongest drop in performance was associated with the poor intersection of convex hulls in the corresponding hospitals’ datasets and with a high performance of ML methods for dataset discrimination. Hence, we suggest the application of our pipeline as a first tool to assess the transferability of trained models. We emphasize that datasets from different hospitals represent heterogeneous data sources, and the transfer from one database to another should be performed with utmost care to avoid implications during real-world applications of the developed models. Further research is needed to develop methods for the adaptation of ML models to new hospitals. In addition, more work should be aimed at the creation of gold-standard datasets that are large and diverse with data from varied application sites.

Authors: Konstantin Sharafutdinov, Jayesh S Bhat, Sebastian Johannes Fritsch, Kateryna Nikulina, Moein E Samadi, Richard Polzin, Hannah Mayer, Gernot Marx, Johannes Bickenbach, Andreas Schuppert

Date Published: 1st Oct 2022

Publication Type: Journal article

POLAR – „POLypharmazie, Arzneimittelwechselwirkungen und Risiken“ – wie können Daten aus der stationären Krankenversorgung zur Beurteilung beitragen?
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

Zusammenfassung Hintergrund Mit der zunehmenden Anzahl eingenommener Arzneimittel steigt die Prävalenz von Medikationsrisiken. Hierzu zählen beispielsweise Arzneimittelwechselwirkungen, welche erwünschte … und unerwünschte Wirkungen einzelner Arzneistoffe reduzieren aber auch verstärken können. Fragestellung Das Verbundvorhaben POLAR (POLypharmazie, Arzneimittelwechselwirkungen und Risiken) hat das Ziel, mit Methoden und Prozessen der Medizininformatikinitiative (MII) auf Basis von „Real World Data“ (stationärer Behandlungsdaten von Universitätskliniken) einen Beitrag zur Detektion von Medikationsrisiken bei Patient:innen mit Polymedikation zu leisten. Im Artikel werden die konkreten klinischen Probleme dargestellt und am konkreten Auswertebeispiel illustriert. Material und Methoden Konkrete pharmakologische Fragestellungen werden algorithmisch abgebildet und an 13 Datenintegrationszentren in verteilten Analysen ausgewertet. Eine wesentliche Voraussetzung für die Anwendung dieser Algorithmen ist die Kerndatensatzstruktur der MII, die auf internationale IT-, Interoperabilitäts- und Terminologiestandards setzt. Ergebnisse In POLAR konnte erstmals gezeigt werden, dass stationäre Behandlungsdaten standortübergreifend auf der Basis abgestimmter, interoperabler Datenaustauschformate datenschutzkonform für Forschungsfragen zu arzneimittelbezogenen Problemen nutzbar gemacht werden können. Schlussfolgerungen Als Zwischenstand in POLAR wird ein erstes vorläufiges Ergebnis einer Analyse gezeigt. Darüber hinaus werden allgemeinere technische, rechtliche, kommunikative Chancen und Herausforderungen dargestellt, wobei der Fokus auf dem Fall der Verwendung stationärer Behandlungsdaten als „Real World Data“ für die Forschung liegt.

Authors: André Scherag, Wahram Andrikyan, Tobias Dreischulte, Pauline Dürr, Martin F Fromm, Jan Gewehr, Ulrich Jaehde, Miriam Kesselmeier, Renke Maas, Petra A Thürmann, Frank Meineke, Daniel Neumann, Julia Palm, Thomas Peschel, Editha Räuscher, Susann Schulze, Torsten Thalheim, Thomas Wendt, Markus Loeffler, D Ammon, W Andrikyan, U Bartz, B Bergh, T Bertsche, O Beyan, S Biergans, H Binder, M Boeker, H Bogatsch, R Böhm, A Böhmer, J Brandes, C Bulin, D Caliskan, I Cascorbi, M Coenen, F Dietz, F Dörje, T Dreischulte, J Drepper, P Dürr, A Dürschmid, F Eckelt, R Eils, A Eisert, C Engel, F Erdfelder, K Farker, M Federbusch, S Franke, N Freier, T Frese, M Fromm, K Fünfgeld, T Ganslandt, J Gewehr, D Grigutsch, W Haefeli, U Hahn, A Härdtlein, R Harnisch, S Härterich, M Hartmann, R Häuslschmid, C Haverkamp, O Heinze, P Horki, M Hug, T Iskra, U Jaehde, S Jäger, P Jürs, C Jüttner, J Kaftan, T Kaiser, K Karsten Dafonte, M Kesselmeier, S Kiefer, S Klasing, O Kohlbacher, D Kraska, S Krause, S Kreutzke, R Krock, K Kuhn, S Lederer, M Lehne, M Löbe, M Loeffler, C Lohr, V Lowitsch, N Lüneburg, M Lüönd, I Lutz, R Maas, U Mansmann, K Marquardt, A Medek, F Meineke, A Merzweiler, A Michel-Backofen, Y Mou, B Mussawy, D Neumann, J Neumann, C Niklas, M Nüchter, K Oswald, J Palm, T Peschel, H Prokosch, J Przybilla, E Räuscher, L Redeker, Y Remane, A Riedel, M Rottenkolber, F Rottmann, F Salman, J Schepers, A Scherag, F Schmidt, S Schmiedl, K Schmitz, G Schneider, A Scholtz, S Schorn, B Schreiweis, S Schulze, A K Schuster, M Schwab, H Seidling, S Semler, K Senft, M Slupina, R Speer, S Stäubert, D Steinbach, C Stelzer, H Stenzhorn, M Strobel, T Thalheim, M Then, P Thürmann, D Tiller, P Tippmann, Y Ucer, S Unger, J Vogel, J Wagner, J Wehrle, D Weichart, L Weisbach, S Welten, T Wendt, R Wettstein, I Wittenberg, R Woltersdorf, M Yahiaoui-Doktor, S Zabka, S Zenker, S Zeynalova, L Zimmermann, D Zöller, für das POLAR-Projekt

Date Published: 1st Sep 2022

Publication Type: Journal article

GRASCCO — The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

Editor:

Date Published: 17th Aug 2022

Publication Type: InProceedings

The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.
LHA - Leipzig Health Atlas

Abstract (Expand)

BACKGROUND: Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research … domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. OBJECTIVE: The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. METHODS: Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. RESULTS: The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Authors: T. Kirsten, F. A. Meineke, H. Loeffler-Wirth, C. Beger, A. Uciteli, S. Staubert, M. Lobe, R. Hansel, F. G. Rauscher, J. Schuster, T. Peschel, H. Herre, J. Wagner, S. Zachariae, C. Engel, M. Scholz, E. Rahm, H. Binder, M. Loeffler

Date Published: 3rd Aug 2022

Publication Type: Journal article

GRASCCO — the first publicly shareable, multiply-alienated German clinical text corpus
SMITH - Smart Medical Information Technology for Healthcare

Abstract (Expand)

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation … steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

Authors: Luise Modersohn, Stefan Schulz, Christina Lohr, Udo Hahn

Date Published: 1st Aug 2022

Publication Type: InCollection

On the Parametrization of Epidemiologic Models-Lessons from Modelling COVID-19 Epidemic.
Genetical Statistics and Systems Biology, Task Force COVID-19 Leipzig

Abstract (Expand)

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing … testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models.

Authors: Y. Kheifetz, H. Kirsten, M. Scholz

Date Published: 2nd Jul 2022

Publication Type: Journal article

Human Diseases: COVID-19

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