Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis, the ProVal-MS study.

INTRODUCTION: In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients. METHODS: ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing-Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing-Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. PERSPECTIVE: Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)-ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034.

Biomarker analysis from the phase 2b randomized placebo-controlled trial of riociguat in early diffuse cutaneous systemic sclerosis.

OBJECTIVE: To examine disease and target engagement biomarkers in the RISE-SSc trial of riociguat in early diffuse cutaneous systemic sclerosis and their potential to predict the response to treatment. METHODS: Patients were randomized to riociguat (n = 60) or placebo (n = 61) for 52 weeks. Skin biopsies and plasma/serum samples were obtained at baseline and week 14. Plasma cyclic guanosine monophosphate (cGMP) was assessed using radio-immunoassay. Alpha smooth muscle actin (αSMA) and skin thickness were determined by immunohistochemistry, mRNA markers of fibrosis by qRT-PCR in skin biopsies, and serum CXC motif chemokine ligand 4 (CXCL-4) and soluble platelet endothelial cell adhesion molecule-1 (sPECAM-1) by enzyme-linked immunosorbent assay. RESULTS: By week 14, cGMP increased by 94 ± 78% with riociguat and 10 ± 39% with placebo (p < 0.001, riociguat vs placebo). Serum sPECAM-1 and CXCL-4 decreased with riociguat vs placebo (p = 0.004 and p = 0.008, respectively). There were no differences in skin collagen markers between the 2 groups. Higher baseline serum sPECAM-1 or the detection of αSMA-positive cells in baseline skin biopsies were associated with a larger reduction of modified Rodnan skin score from baseline at week 52 with riociguat vs placebo (interaction P-values 0.004 and 0.02, respectively). CONCLUSION: Plasma cGMP increased with riociguat, suggesting engagement with the nitric oxide-soluble guanylate cyclase-cGMP pathway. Riociguat was associated with a significant reduction in sPECAM-1 (an angiogenic biomarker) vs placebo. Elevated sPECAM-1 and the presence of αSMA-positive skin cells may help to identify patients who could benefit from riociguat in terms of skin fibrosis. TRIAL REGISTRATION: Clinicaltrials.gov, NCT02283762.

Differences in the annotation between facial images and videos for training an artificial intelligence for skin type determination.

BACKGROUND: The Grand-AID research project, consisting of GRANDEL-The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared. MATERIALS AND METHODS: Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi-quantitative and a linear scale in a cross-over design to evaluate differences between the image modalities and between the raters. RESULTS: In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters. CONCLUSION: The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross-validation of the annotation.

Accelerometer vs. other activity measures in heart failure with preserved ejection fraction: the VITALITY-HFpEF trial.

AIMS: The relationship between accelerometry data and changes in Kansas City Cardiomyopathy Questionnaire-Physical Limitation Score (KCCQ-PLS) or 6 min walk test (6MWT) is not well understood. METHODS AND RESULTS: VITALITY-HFpEF accelerometry substudy (n = 69) data were assessed at baseline and 24 weeks. Ordinal logistic regression models were used to assess the association between accelerometry activity and deterioration, improved, or unchanged KCCQ-PLS (≥8.33 and ≤ -4.17 points) and 6MWT (≥32 vs. ≤ -32 m). KCCQ-PLS score deteriorated in 16 patients, improved in 34, and was unchanged in 19. 6MWT deteriorated in 8 patients, improved in 21, and was unchanged in 19. Mean accelerometer wear was 21.4 (±2.1) h/day. Changes in hours active from baseline to 24 weeks were not significantly different among patients who exhibited deterioration, improvement, or no change in KCCQ-PLS [odds ratio (OR) 0.91, 95% confidence interval (CI) 0.71-1.18; P = 0.48] or 6MWT (OR 1.21, 95% CI 0.91-1.60; P = 0.18). Similar lack of association was observed for other accelerometry metrics and change in KCCQ and 6MWT. These findings were unaffected when KCCQ and 6MWT were examined as continuous variables. CONCLUSIONS: Accelerometer-based activity measures did not correlate with subjective or objective standard measures of health status and functional capacity in heart failure with preserved ejection fraction. Further investigation of their relationships to clinical outcomes is required.

GERNERMED++: Semantic annotation in German medical NLP through transfer-learning, translation and word alignment.

We present a statistical model, GERNERMED++, for German medical natural language processing trained for named entity recognition (NER) as an open, publicly available model. We demonstrate the effectiveness of combining multiple techniques in order to achieve strong results in entity recognition performance by the means of transfer-learning on pre-trained deep language models (LM), word-alignment and neural machine translation, outperforming a pre-existing baseline model on several datasets. Due to the sparse situation of open, public medical entity recognition models for German texts, this work offers benefits to the German research community on medical NLP as a baseline model. The work serves as a refined successor to our first GERNERMED model. Similar to our previous work, our trained model is publicly available to other researchers. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED-pp.

MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data.

Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arise when images with a very small region of interest or without a region of interest at all are assessed. As a solution to these limitations, we propose a new medical image segmentation metric: MISm. This metric is a composition of the Dice similarity coefficient and the weighted specificity. MISm was investigated for definition gaps, an appropriate scoring gradient, and different weighting coefficients used to propose a constant value. Furthermore, an evaluation was performed by comparing the popular metrics in the medical image segmentation and MISm using images of magnet resonance tomography from several fictitious prediction scenarios. Our analysis shows that MISm can be applied in a general way and thus also covers the mentioned edge cases, which are not covered by other metrics, in a reasonable way. In order to allow easy access to MISm and therefore widespread application in the community, as well as reproducibility of experimental results, we included MISm in the publicly available evaluation framework MISeval.

Annotated dataset creation through large language models for non-english medical NLP.

Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom-designed datasets to address NLP tasks in a supervised machine learning fashion. When operating in non-English languages for medical data processing, this exposes several minor and major, interconnected problems such as the lack of task-matching datasets as well as task-specific pre-trained models. In our work, we suggest to leverage pre-trained large language models for training data acquisition in order to retrieve sufficiently large datasets for training smaller and more efficient models for use-case-specific tasks. To demonstrate the effectiveness of your approach, we create a custom dataset that we use to train a medical NER model for German texts, GPTNERMED, yet our method remains language-independent in principle. Our obtained dataset as well as our pre-trained models are publicly available at https://github.com/frankkramer-lab/GPTNERMED.

Evaluation of the Availability of Nursing Quality Indicators in German FHIR Implementations.

Standardized nursing data sets facilitate data analysis and help to improve nursing research and quality management in Germany. Recently, governmental standardization approaches have favored the FHIR standard and helped to define it as the state of the art for healthcare interoperability and data exchange. In this study, we identify common data elements used for nursing quality research purposes by analyzing nursing quality data sets and databases. We then compare the results with current FHIR implementations in Germany to find most relevant data fields and overlaps. Our results show that most of the patient focused information has already been modelled in national standardization efforts and FHIR implementations. However, representation of data fields describing nursing staff related information, such as experience, workload or satisfaction, is missing or lacking.

Data and Information Problems in Health Care and How Creative Methods Might Solve Them.

This paper discusses the concept of data and information and highlights the problems associated with their usage in healthcare. While data refers to facts and statistics collected for reference or analysis, information includes the context provided to data to gain meaning. Healthcare professionals use the information obtained from data to improve patients' health status and satisfaction. Nevertheless, the value of information depends on the data and how it is presented. As a result, many problems can arise in the collection and processing of data and the provision of information. In this paper, these are called data and information problems. One possible approach to reduce such problems in the future could be to use creative methods. To initially address this idea, exemplary keyword research was carried out, and examples are presented in this paper.

Towards Automated COVID-19 Presence and Severity Classification.

COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.

The RCX Extension Hub: A Resource for Implementations Extending the R Adaption of the Cytoscape Exchange Format.

Public repositories provide access to biological networks for investigations, and subsequently serve to distribute the network encoded biomedical and even clinically relevant results. However, inclusion of complementary information requires data structures and implementations customized to the integrated data for network representation, usage in supporting application, and extending analysis functionality. Partitioning of this information into individual aspects of a network facilitates compatibility and reusability of the network-based results, but also requires support and accessibility of the extensions and their implementations. The RCX extension hub offers overview and access to extensions of the Cytoscape exchange format implemented in R. The hub supports the realization of self-customized extension through guides, example implementations, and a template for the creation of R extension packages.

German Medical Named Entity Recognition Model and Data Set Creation Using Machine Translation and Word Alignment: Algorithm Development and Validation.

BACKGROUND: Data mining in the field of medical data analysis often needs to rely solely on the processing of unstructured data to retrieve relevant data. For German natural language processing, few open medical neural named entity recognition (NER) models have been published before this work. A major issue can be attributed to the lack of German training data. OBJECTIVE: We developed a synthetic data set and a novel German medical NER model for public access to demonstrate the feasibility of our approach. In order to bypass legal restrictions due to potential data leaks through model analysis, we did not make use of internal, proprietary data sets, which is a frequent veto factor for data set publication. METHODS: The underlying German data set was retrieved by translation and word alignment of a public English data set. The data set served as a foundation for model training and evaluation. For demonstration purposes, our NER model follows a simple network architecture that is designed for low computational requirements. RESULTS: The obtained data set consisted of 8599 sentences including 30,233 annotations. The model achieved a class frequency-averaged F1 score of 0.82 on the test set after training across 7 different NER types. Artifacts in the synthesized data set with regard to translation and alignment induced by the proposed method were exposed. The annotation performance was evaluated on an external data set and measured in comparison with an existing baseline model that has been trained on a dedicated German data set in a traditional fashion. We discussed the drop in annotation performance on an external data set for our simple NER model. Our model is publicly available. CONCLUSIONS: We demonstrated the feasibility of obtaining a data set and training a German medical NER model by the exclusive use of public training data through our suggested method. The discussion on the limitations of our approach includes ways to further mitigate remaining problems in future work.

Riociguat in patients with early diffuse cutaneous systemic sclerosis (RISE-SSc): open-label, long-term extension of a phase 2b, randomised, placebo-controlled trial.

BACKGROUND: The phase 2b Riociguat Safety and Efficacy in Patients with Diffuse Cutaneous Systemic Sclerosis (RISE-SSc) trial investigated riociguat versus placebo in early diffuse cutaneous systemic sclerosis. The long-term extension evaluated safety and exploratory treatment effects for an additional year. METHODS: Patients were enrolled to RISE-SSc between Jan 15, 2015, and Dec 8, 2016. Those who completed the 52-week, randomised, parallel-group, placebo-controlled, double-blind phase were eligible for the long-term extension. Patients originally assigned to riociguat continued therapy (riociguat-riociguat group). Those originally assigned to placebo were switched to riociguat (placebo-riociguat group), adjusted up to 2·5 mg three times daily in a 10-week, double-blind dose-adjustment phase, followed by an open-label phase. Statistical analyses were descriptive. Safety including adverse events and serious adverse events was assessed in the long-term safety analysis set (all patients randomly assigned and treated with study medication in the double-blind phase who continued study medication in the long-term extension). The RISE-SSc trial is registered with ClinicalTrials.gov, NCT02283762. FINDINGS: In total, 87 (72%) of 121 patients in the main RISE-SSc study entered the long-term extension (riociguat-riociguat, n=42; placebo-riociguat, n=45). 65 (75%) of 87 patients were women, 22 (25%) were men, and 62 (71%) were White. Overall, 82 (94%) of 87 patients in the long-term extension had an adverse event; most (66 [76%] of 87) were of mild to moderate severity, with no increase in pulmonary-related serious adverse events in patients with interstitial lung disease. INTERPRETATION: No new safety signals were observed with long-term riociguat in patients with early diffuse cutaneous systemic sclerosis. Study limitations include the absence of a comparator group in this open-label extension study. FUNDING: Bayer and Merck Sharp & Dohme.

Web-based Prototype for Graphical Exploration of FHIR® Questionnaire Responses.

The evaluation of clinical questionnaires is an important part of gaining knowledge in empirical research. The electronically captured responses are encoded in a standard format such as HL7 FHIR® that facilitates data exchange and systems interoperability. However, this also complicates access of the information to explore and interpret the results without appropriate tools. In this work, we present the design of a web-based graphical exploration tool for categorical questionnaire response data that can interact with FHIR-conformant HTTP endpoints. The web app enables non-technical users with simplified, direct visual access to highly structured FHIR questionnaire response data and preserves the applicability in arbitrary data exploration tasks. We describe the abstract feature design with the derived technical implementation to allow a universal, user-configurable data subselection mechanism to generate conditional one- and two-data-dimensional charts. The applicability of our developed prototype is demonstrated on synthetic FHIR data with the source code available at https://github.com/frankkramer-lab/FHIR-QR-Explorer.

Adaptation of HL7 FHIR for the Exchange of Patients' Gene Expression Profiles.

High-throughput technologies, especially gene expression analyses can accurately capture the molecular state in patients under different conditions. Thus, their application in clinical routine gains increasing relevance and fosters patient stratification towards individualized treatment decisions. Electronic health records already evolved to capture genomic data within clinical systems and standards like FHIR enable sharing within, and even between institutions. However, FHIR only provides profiles tailored to variations in the molecular sequence, while expression patterns are neglected although being equally important for decision making. Here we provide an exemplary implementation of gene expression profiles of a microarray analysis of patients with acute myeloid leukemia using an adaptation of the FHIR genomics extension. Our results demonstrate how FHIR resources can be facilitated in clinical systems and thereby pave the way for usage for the aggregation and exchange of transcriptomic data in multi-center studies.

Data-dependent visualization of biological networks in the web-browser with NDExEdit.

Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be directly exported to the NDEx platform or saved as a compatible CX file, additionally to standard image formats like PNG and JPEG.

Perspective on Code Submission and Automated Evaluation Platforms for University Teaching.

We present a perspective on platforms for code submission and automated evaluation in the context of university teaching. Due to the COVID-19 pandemic, such platforms have become an essential asset for remote courses and a reasonable standard for structured code submission concerning increasing numbers of students in computer sciences. Utilizing automated code evaluation techniques exhibits notable positive impacts for both students and teachers in terms of quality and scalability. We identified relevant technical and non-technical requirements for such platforms in terms of practical applicability and secure code submission environments. Furthermore, a survey among students was conducted to obtain empirical data on general perception. We conclude that submission and automated evaluation involves continuous maintenance yet lowers the required workload for teachers and provides better evaluation transparency for students.

Towards a guideline for evaluation metrics in medical image segmentation.

In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful prediction capabilities and achieved similar results as clinicians. However, recent studies revealed that the evaluation in image segmentation studies lacks reliable model performance assessment and showed statistical bias by incorrect metric implementation or usage. Thus, this work provides an overview and interpretation guide on the following metrics for medical image segmentation evaluation in binary as well as multi-class problems: Dice similarity coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen's Kappa, and Hausdorff distance. Furthermore, common issues like class imbalance and statistical as well as interpretation biases in evaluation are discussed. As a summary, we propose a guideline for standardized medical image segmentation evaluation to improve evaluation quality, reproducibility, and comparability in the research field.

Implementation of Gene Expression Profiles in the HL7 FHIR Standard.

Gene expression profiles can capture significant molecular differences paving the way toward precision medicine. However, clinical standards like FHIR only provide encoding of molecular sequence variations, even so, expression patterns are equally important for decision making. Here we provide an exemplary implementation of gene expression profiles of a microarray analysis using an adaption of the FHIR Genomics extension. Our results demonstrate how FHIR resources can be facilitated in bioinformatics-based decision support systems or used for the aggregation of molecular genetics data in multi-center clinical trials.