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

Kirsten, Toralf; Meineke, Frank A.; Löffler-Wirth, Henry; Uciteli, Alexandr; Beger, Christoph; Stäubert, Sebastian; Löbe, Matthias; Hänsel, René; Rauscher, Franziska G.; Schuster, Judith Christina; Peschel, Thomas; Herre, Heinrich; Wagner, Jonas; Zachariae, Silke; Engel, Christoph; Scholz, Markus; Rahm, Erhard; Binder, Hans; Löffler, Markus

doi:10.1055/a-1914-1985

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Normative data should include factors that influence RNFLT, such as age, sex, and refraction. 8 , 11 – 13 ,31 The found absence of improvement may be caused by the lack of adjustment for such influence factors in the tested reference database. One example of norms that do accommodate these factors is available in a cloud-based visualizer ( https://apps.health-atlas.de/rnflt-visualizer/ ), developed by Peschel et al 12 Our results demonstrate the need to provide external validation of machine normative outputs to clinical populations.…”

Section: Discussionmentioning

confidence: 95%

“…OCT manufacturers attempt to ease the interpretation of structural information by providing population normative datasets, as RNFLT is known to be associated with age, sex, eye-side, and ocular magnification. 8 – 13 Machine printouts provide a graphical representation of an individual patient's percentiles of RNFLT compared to a healthy control population ( Fig. 1 ).…”

Section: Introductionmentioning

confidence: 99%

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Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss

Singh,

Rauscher,

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose Circumpapillary retinal nerve fiber layer thickness (RNFLT) measurement aids in the clinical diagnosis of glaucoma. Spectral domain optical coherence tomography (SD-OCT) machines measure RNFLT and provide normative color-coded plots. In this retrospective study, we investigate whether normative percentiles of RNFLT (pRNFLT) from Spectralis SD-OCT improve prediction of glaucomatous visual field loss over raw RNFLT. Methods A longitudinal database containing OCT scans and visual fields from Massachusetts Eye & Ear glaucoma clinic patients was generated. Reliable OCT-visual field pairs were selected. Spectralis OCT normative distributions were extracted from machine printouts. Supervised machine learning models compared predictive performance between pRNFLT and raw RNFLT inputs. Regional structure-function associations were assessed with univariate regression to predict mean deviation (MD). Multivariable classification predicted MD, pattern standard deviation, MD change per year, and glaucoma hemifield test. Results There were 3016 OCT-visual field pairs that met the reliability criteria. Spectralis norms were found to be independent of age, sex, and ocular magnification. Regional analysis showed significant decrease in R 2 from pRNFLT models compared to raw RNFLT models in inferotemporal sectors, across multiple regressors. In multivariable classification, there were no significant improvements in area under the curve of receiver operating characteristic curve (ROC-AUC) score with pRNFLT models compared to raw RNFLT models. Conclusions Our results challenge the assumption that normative percentiles from OCT machines improve prediction of glaucomatous visual field loss. Raw RNFLT alone shows strong prediction, with no models presenting improvement by the manufacturer norms. This may result from insufficient patient stratification in tested norms. Translational Relevance Understanding correlation of normative databases to visual function may improve clinical interpretation of OCT data.

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Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss

Singh,

Rauscher,

et al. 2023

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

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“…It will be expectedly challenging to get high quality metadata from data providers who need to do manual research on it, e.g., providence information. In the future, the LHA will serve as a template for "Local Data Hubs (LDH)" of NFDI4Health [9], which requires standards-compliant, machine-readable vocabularies and interfaces [10]. From a clinical perspective, it will be important to set the bar low for discoverability and access.…”

Section: Discussionmentioning

confidence: 99%

Concept for a Basic ISO 14721 Archive Information Package for Clinical Studies

Löbe,

Kuntz,

Henke

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Secondary use of medical data for research is desirable for intrinsic, ethical and financial reasons. In this context, the question becomes relevant as to how such datasets are to be made accessible to a larger target group in the long term. Typically, datasets are not extracted ad hoc from the primary systems, because they are processed qualitatively (FAIR data). Special data repositories are currently being built for this purpose. This paper examines the requirements for the reuse of clinical trial data in a data repository utilizing the Open Archiving Information System (OAIS) reference model. In particular, a concept for an Archive Information Package (AIP) is developed with the central focus on a cost-effective trade-off between the effort of creation for the data producer and the comprehensibility of the data for the data consumer.

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“…the data is gathered in a central data warehouse. The "Leipzig Health Atlas (LHA)" [21] consists of an i2b2 and a seek instance, that also rely on imported central data. In addition, tools with a different focus were not investigated further, i.e.…”

Section: Identified Feasibility Toolsmentioning

confidence: 99%

Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms

Witte,

Schoneberg,

Hanss

et al. 2023

German Medical Data Sciences 2023 – Science. Close to People.

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Introduction: The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data. It is crucial for data scientists to assess the number and distribution of cohorts that meet complex combinations of criteria defined by the research question. This so-called feasibility test is increasingly offered as a self-service, where scientists can filter the available data according to specific parameters. Early feasibility tools have been developed for biosamples or image collections. They are of high interest for clinical study platforms that federate multiple studies and data types, but they pose specific requirements on the integration of data sources and data protection. Methods: Mandatory and desired requirements for such tools were acquired from two user groups — primary users and staff managing a platform’s transfer office. Open Source feasibility tools were sought by different literature search strategies and evaluated on their adaptability to the requirements. Results: We identified seven feasibility tools that we evaluated based on six mandatory properties. Discussion: We determined five feasibility tools to be most promising candidates for adaption to a clinical study research data platform, the Clinical Communication Platform, the German Portal for Medical Research Data, the Feasibility Explorer, Medical Controlling, and the Sample Locator.

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

Cited by 6 publications

References 32 publications

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss

Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss

Concept for a Basic ISO 14721 Archive Information Package for Clinical Studies

Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms

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