Editing Physicians' Responses Using GPT-4 for Academic Research

The integration of Artificial Intelligence (AI) into digital healthcare, particularly in the anonymisation and processing of health information, holds considerable potential.

OBJECTIVES

To develop a methodology using Generative Pre-trained Transformer (GPT) models to preserve the essence of medical advice in doctors' responses, while editing them for use in scientific studies.

METHODS

German and English responses from EXABO, a rare respiratory disease platform, were processed using iterative refinement and other prompt engineering techniques, with a focus on removing identifiable and irrelevant content.

RESULTS

Of 40 responses tested, 31 were accurately modified according to the developed guidelines. Challenges included misclassification and incomplete removal, with incremental prompting proving more accurate than combined prompting.

CONCLUSION

GPT-4 models show promise in medical response editing, but face challenges in accuracy and consistency. Precision in prompt engineering is essential in medical contexts to minimise bias and retain relevant information.

Translation of Ontological Concepts from English into German Using Commercial Translation Software and Expert Evaluation

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

Rare Diseases in Citizen Science - Preliminary Experiences in Developing a Personal Health App

SelEe is a German citizen science project aiming to develop a smartphone app for a patient-managed record. The goal is to study rare diseases with the support of interested citizens and people affected by rare diseases. We established a core research team, including professional researchers (leading the project) and citizens. Citizens have the opportunity to discuss the progress, make suggestions regarding the app's design and data entry and contribute to the dissemination of the project. To gather feedback and experiences from the core research team, we performed an online questionnaire regarding the topics "influence and communication", "improvements and learning effect", and "satisfaction". Finally, 9 citizens of the core research team participated. The results show that the citizens are very satisfied with the design of the app, their participation opportunities and the communication in the project.

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.

Machine translation of standardised medical terminology using natural language processing: A scoping review

Standardised medical terminologies are used to ensure accurate and consistent communication of information and to facilitate data exchange. Currently, many terminologies are only available in English, which hinders international research and automated processing of medical data. Natural language processing (NLP) and Machine Translation (MT) methods can be used to automatically translate these terms. This scoping review examines the research on automated translation of standardised medical terminology. A search was performed in PubMed and Web of Science and results were screened for eligibility by title and abstract as well as full text screening. In addition to bibliographic data, the following data items were considered: 'terminology considered', 'terms considered', 'source language', 'target language', 'translation type', 'NLP technique', 'NLP system', 'machine translation system', 'data source' and 'translation quality'. The results showed that the most frequently translated terminology is SNOMED CT (39.1%), followed by MeSH (13%), ICD (13%) and UMLS (8.7%). The most common source language is English (55.9%), and the most common target language is German (41.2%). Translation methods are often based on Statistical Machine Translation (SMT) (41.7%) and, more recently, Neural Machine Translation (NMT) (30.6%), but can also be combined with various MT methods. Commercial translators such as Google Translate (36.4%) and automatic validation methods such as BLEU (22.2%) are frequently used tools for translation and subsequent validation.

Enhancing HIV Patient Support Through Telehealth: Exploring Design Solutions

In recent years, telemedicine has advanced significantly, offering new possibilities for improving healthcare and patient outcomes. This paper presents a telemedicine app for HIV patients, developed using a human-centered design approach. Designed to meet the diverse and specific needs of Pre-Exposure Prophylaxis (PrEP) users and Late Presenters (LP), the app is part of the COMTRAC-HIV Project at the University Hospital Frankfurt. Through interviews with HIV experts and healthcare professionals, initial design solutions were derived. The paper explores the app's design process, core functionalities, and future directions, aiming to provide comprehensive support for individuals living with HIV.

Methods Used in the Development of Common Data Models for Health Data: Scoping Review

BACKGROUND

Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems.

OBJECTIVE

This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level.

METHODS

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development.

RESULTS

We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process.

CONCLUSIONS

The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

Interviews with HIV Experts for Development of a Mobile Health Application in HIV Care-A Qualitative Study

The Communication and Tracing App HIV (COMTRAC-HIV) project aims to develop a mobile health application for integrated care of HIV patients due to the low availability of those apps in Germany. This study addressed organizational conditions and necessary app functionalities, especially for the care of late diagnosed individuals (late presenters) and those using pre-exposure prophylaxis. We followed a human-centered design approach and interviewed HIV experts in Germany to describe the context of use of the app. The interviews were paraphrased and analyzed with a qualitative content analysis. To define the context of use, user group profiles were defined and tasks derived, which will represent the functionalities of the app. A total of eight experts were included in the study. The results show that the app should include a symptom diary for entering symptoms, side effects, and their intensity. It offers chat/video call functionality for communication with an HIV expert, appointment organization, and sharing findings. The app should also provide medication overview and reminders for medications and appointments. This qualitative study is a first step towards the development of an app for HIV individuals in Germany. Further research includes involving patients in the initial app design and test design usability.

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process

BACKGROUND

As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs.

METHODS

Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements.

RESULTS

Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended.

CONCLUSIONS

Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

Diagnosis Support for Rare Diseases Using Phenotypic Profiles

The common occurrence of characteristic symptoms can be used to infer diagnoses. The aim of this study is to show how syndrome similarity analysis using given phenotypic profiles can help in the diagnosis of rare diseases. HPO was used to map syndromes and phenotypic profiles. The system architecture described is planned to be implemented in a clinical decision support system for unclear diseases.

Die Medizininformatik-Initiative und Seltene Erkrankungen: Routinedaten der nächsten Generation für Diagnose, Therapiewahl und Forschung

Bei Menschen mit Seltenen Erkrankungen (SE) besteht ein besonderes Potenzial, von der Digitalisierung im Gesundheitswesen zu profitieren. Das Nationale Aktionsbündnis für Menschen mit Seltenen Erkrankungen (NAMSE) hat sich dafür eingesetzt, dass SE bei der Digitalisierung des Gesundheitswesens in Deutschland konkret berücksichtigt werden. In der Medizininformatik-Initiative (MII) des Bundesministeriums für Bildung und Forschung (BMBF) wurde das Thema aufgegriffen. Hier wird aktuell ausgehend von den Universitätskliniken eine digitale Infrastruktur für die datenschutzkonforme Mehrfachnutzung von standardisierten Versorgungs- und Forschungsdaten aufgebaut. Teil der Initiative ist seit dem Jahr 2020 das Projekt CORD-MI (Collaboration on Rare Diseases), in dem sich Universitätskliniken und weitere Partner deutschlandweit zusammengeschlossen haben, um die Patientenversorgung und die Forschung im Bereich der SE zu verbessern.

In diesem Beitrag wird beleuchtet, in welcher Weise die MII die Belange der SE berücksichtigt und welche Chancen die gewonnenen „neuen Routinedaten“ bieten. Ein SE-Modul wurde in den „MII-Kerndatensatz“ aufgenommen – ein Informationsmodell, das auf dem Datenstandard „FHIR“ (Fast Healthcare Interoperability Resources) basiert. Daten, die im Rahmen von Versorgungs- und Forschungsroutinen erhoben werden, können so zukünftig zwischen den beteiligten Einrichtungen ausgetauscht werden und im Bereich SE z. B. die Diagnosefindung, die Therapiewahl und Forschungsvorhaben unterstützen. Das Projekt CORD-MI hat sich zum Ziel gesetzt, mit Hilfe exemplarischer Fragestellungen Erkenntnisse über die Versorgungssituation von Menschen mit SE zu erhalten und daraufhin Rückschlüsse für weitere notwendige Schritte im Bereich der Digitalisierung zu ziehen.

Identifying project topics and requirements in a citizen science project in rare diseases: a participative study

Background

Due to their low prevalence (< 5 in 10,000), rare diseases are an important area of research, with the active participation of those affected being a key factor. In the Citizen Science project “SelEe” (Researching rare diseases in a citizen science approach), citizens collaborate with researchers using a digital application, developed as part of the project together with those affected, to answer research questions on rare diseases. The aim of this study was to define the rare diseases to be considered, the project topics and the initial requirements for the implementation in a digital application.

Methods

To address our research questions, we took several steps to engage citizens, especially those affected by rare diseases. This approach included the following methods: pre- and post-survey (questionnaire), two workshops with focus group discussion and a requirements analysis workshop (with user stories).

Results

In the pre-survey, citizens suggested 45 different rare diseases and many different disease groups to be considered in the project. Two main project topics (A) “Patient-guided documentation and data collection” (20 votes) and (B) “Exchange of experience and networking” (13 votes) were identified as priorities in the workshops and ranked in the post-survey. The requirements workshop resulted in ten user stories and six initial requirements to be implemented in the digital application.

Conclusion

Qualitative, citizen science research can be used to collectively identify stakeholder needs, project topics and requirements for a digital application in specific areas, such as rare diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02514-3.

The ERN-LUNG Population Registry: Aims, Software-Implementation and First Results

The ERN-LUNG Population Registry is a new European-wide collection of patients with rare lung diseases, allowing patients to register online in the registry. Medical experts can recruit patients in the registry for disease-specific registries and care options. The Population Registry was implemented on the basis of the open source software OSSE and extended by functions for the self-registration of patients. Patients were invited through patient organizations between May and November 2022. 115 patients registered online in the registry, whereas 60 of them provided full data in the registry form. After first months of usage, further dissemination of the registry is necessary to reach more patients, e.g. by recruiting them via medical centres directly. Improvements of the registry should be conducted to achieve a higher number of fully completed forms.

Conception, Development and Validation of Classification Methods for Coding Support of Rare Diseases Using Artificial Intelligence

Automated coding of diseases can support hospitals in the billing of inpatient cases with the health insurance funds. This paper describes the implementation and evaluation of classification methods for two selected Rare Diseases. Different classifiers of an off-the-shelf system and an own application are applied in a supervised learning process and comparatively examined for their suitability and reliability. Using Natural Language Processing and Machine Learning, disease entities are recognized from unstructured historical patient records and new billing cases are coded automatically. The results of the performed classifications show that even with small datasets (≤ 200), high correctness (F1 score ∼0.8) can be achieved in predicting new cases.

Development of an Interactive Dashboard for OSSE Rare Disease Registries

BACKGROUND

The Open Source Registry System for Rare Diseases (OSSE), a web-based tool to create rare disease patient registries, currently offers no possibility to view aggregated registry data within the system. Here, we present the development and implementation of a dashboard for the registry of the German NEOCYST (Network for early onset cystic kidney diseases) consortium.

METHODS

Based on user requirements from NEOCYST, we developed a general dashboard for all OSSE registries, which was extended with NEOCYST-specific statistics.

RESULTS

The dashboard now allows users to gain a quick overview of key data, such as patient counts or the availability of biospecimens.

CONCLUSION

This work represents a first prototypical approach for an OSSE dashboard, demonstrated in an existing rare disease registry, to be further evaluated and enhanced in the future.

User-Centred Development of a Diagnosis Support System for Rare Diseases

The diagnosis of rare diseases is often challenging for physicians, but can be supported by Clinical Decision Support Systems. The MIRACUM consortia, which includes ten university hospitals in Germany, develops a Clinical Decision Support System to support the diagnosis of patients with rare diseases. The users are involved in different phases using a user-centred design process. This publication has the objective to summarize the results of all studies performed in context of the requirements elicitation and to derive concrete requirements for the development of the system. Several studies were performed for requirements elicitation: a cross-sectional survey, expert interviews and a focus group. Participants were experts in rare diseases of the MIRACUM locations. 32 requirements were derived and implemented in a prototype. The prototype allows similarity analyses as a decision support functionality by comparing patients without a diagnosis to patients with a rare disease. In the final evaluation, the prototype was rated with a good usability. Since the system is limited in its functionality, further work and improvements are necessary to make it ready for clinical usage.

Citizen Science in Human Medicine and the Use of Software-Systems: A Rapid Scoping Review

Citizen science allows involving interested citizen in the entire research process in science. In the past, various citizen science projects have been performed in different research fields, especially in human medicine. We conducted a rapid scoping review to determine which citizen projects in human medicine already used software-based systems to engage citizens in the research process. Furthermore, we analysed which of the software-systems are publicly available, especially in the field of rare diseases, how citizens can participate using those tools and whether the usability was rated by the participants. To get insights for our project "SelEe (Seltene Erkrankungen bürgerwissenschaftlich erforschen)", which is a citizen science project in rare diseases funded by the Federal Ministry of Education and Research (BMBF), we aimed to identify projects in this research area. We searched PubMed for articles between 2011 and 2021 and performed a title- and abstract screening, as well as a full-text screening. Finally, 12 studies were identified in different research areas like public health, genetic research and infectious diseases. We could not identify any study directly associated with rare diseases. None of the studies investigated usability of those systems. Furthermore, five publicly available citizen science software-systems were identified. Three of them are general systems that allow creating, operating, managing citizen science projects and including citizens in the research process. In further investigations, we will check and compare these systems, if they are appropriate for use in our SelEe-project.

Visualization of Similar Patients in a Clinical Decision Support System for Rare Diseases - A Focus Group Study

The diagnosis of patients with rare diseases is often delayed. A Clinical Decision Support System using similarity analysis of patient-based data may have the potential to support the diagnosis of patients with rare diseases. This qualitative study has the objective to investigate how the result of a patient similarity analysis should be presented to a physician to enable diagnosis support. We conducted a focus group with physicians practicing in rare diseases as well as medical informatics researchers. To prepare the focus group, a literature search was performed to check the current state of research regarding visualization of similar patients. We then created software-mockups for the presentation of these visualization methods for the discussion within the focus group. Two persons took independently field notes for data collection of the focus group. A questionnaire was distributed to the participants to rate the visualization methods. The results show that four visualization methods are promising for the visualization of similar patients: "Patient on demand table", "Criteria selection", "Time-Series chart" and "Patient timeline. "Patient on demand table" shows a direct comparison of patient characteristics, whereas "Criteria selection" allows the selection of different patient criteria to get deeper insights into the data. The "Time-Series chart" shows the time course of clinical parameters (e.g. blood pressure) whereas a "Patient timeline" indicates which time events exist for a patient (e.g. several symptoms on different dates). In the future, we will develop a software-prototype of the Clinical Decision Support System to include the visualization methods and evaluate the clinical usage.

Evaluation of a clinical decision support system for rare diseases: a qualitative study

BACKGROUND

Rare Diseases (RDs) are difficult to diagnose. Clinical Decision Support Systems (CDSS) could support the diagnosis for RDs. The Medical Informatics in Research and Medicine (MIRACUM) consortium developed a CDSS for RDs based on distributed clinical data from eight German university hospitals. To support the diagnosis for difficult patient cases, the CDSS uses data from the different hospitals to perform a patient similarity analysis to obtain an indication of a diagnosis. To optimize our CDSS, we conducted a qualitative study to investigate usability and functionality of our designed CDSS.

METHODS

We performed a Thinking Aloud Test (TA-Test) with RDs experts working in Rare Diseases Centers (RDCs) at MIRACUM locations which are specialized in diagnosis and treatment of RDs. An instruction sheet with tasks was prepared that the participants should perform with the CDSS during the study. The TA-Test was recorded on audio and video, whereas the resulting transcripts were analysed with a qualitative content analysis, as a ruled-guided fixed procedure to analyse text-based data. Furthermore, a questionnaire was handed out at the end of the study including the System Usability Scale (SUS).

RESULTS

A total of eight experts from eight MIRACUM locations with an established RDC were included in the study. Results indicate that more detailed information about patients, such as descriptive attributes or findings, can help the system perform better. The system was rated positively in terms of functionality, such as functions that enable the user to obtain an overview of similar patients or medical history of a patient. However, there is a lack of transparency in the results of the CDSS patient similarity analysis. The study participants often stated that the system should present the user with an overview of exact symptoms, diagnosis, and other characteristics that define two patients as similar. In the usability section, the CDSS received a score of 73.21 points, which is ranked as good usability.

CONCLUSIONS

This qualitative study investigated the usability and functionality of a CDSS of RDs. Despite positive feedback about functionality of system, the CDSS still requires some revisions and improvement in transparency of the patient similarity analysis.

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

BACKGROUND

Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support.

METHODS

We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items "Objective and background of the publication/project", "System or project name", "Functionality", "Type of clinical data", "Rare Diseases covered", "Development status", "System availability", "Data entry and integration", "Last software update" and "Clinical usage".

RESULTS

The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: "Analysis or comparison of genetic and phenotypic data," "machine learning" and "information retrieval". Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage.

CONCLUSIONS

Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study

BACKGROUND

Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs when diagnose patients (e.g. the diagnosis workflow), (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS.

METHODS

Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring's qualitative content analysis approach.

RESULTS

A total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers (with a focus on different RDs) are involved in the diagnostic process. Furthermore, interdisciplinary case discussions between physicians are conducted. The experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Furthermore, the experts considered both physicians and experts from the B-centers to be potential users of a CDSS. The experts also have different experiences with CDSS for RDs.

CONCLUSIONS

This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. Further research is necessary to create solutions by also including the experts on RDs.

The Status Quo of Rare Diseases Centres for the Development of a Clinical Decision Support System - A Cross-Sectional Study

Clinical decision support systems (CDSS) help to improve the diagnostics and treatment of rare diseases (RD). As one of four funded consortia of the Medical Informatics Initiative supported by the Federal Ministry of Education and Research (BMBF, Germany), MIRACUM develops a clinical decision support system (CDSS) for RD based on distributed data of ten university hospitals. The CDSS will be developed at the Rare Diseases Centres (RDC) of the MIRACUM consortium. Since it is essential to deliver decision support at the right time and place in the clinician's workflow, this study aimed to capture relevant information of the RDCs regarding patient admission and diagnostic process. Additionally, we investigated how patient documentation and digitalisation is performed at the centres. Therefore, we conducted a cross-sectional survey involving experts in the RDs domain to capture relevant information for the further development of a CDSS in RD. For each centre, one expert on RDs participated in the study (n=8). The survey identified several challenges regarding the reuse of patient data, e.g. the paper-based documentation of a patientâĂŹs medical history and coding of diagnoses using ICD-10. However, we noticed a relevant use of current software diagnosis support and a similarly performed diagnostic process in all RDC. Further studies are needed to get more detailed insights and to define specific requirements.

European lipodystrophy registry: background and structure

BACKGROUND

Lipodystrophy syndromes comprise a group of extremely rare and heterogeneous diseases characterized by a selective loss of adipose tissue in the absence of nutritional deprivation or catabolic state. Because of the rarity of each lipodystrophy subform, research in this area is difficult and international co-operation mandatory. Therefore, in 2016, the European Consortium of Lipodystrophies (ECLip) decided to create a registry for patients with lipodystrophy.

RESULTS

The registry was build using the information technology Open Source Registry System for Rare Diseases in the EU (OSSE), an open-source software and toolbox. Lipodystrophy specific data forms were developed based on current knowledge of typical signs and symptoms of lipodystrophy. The platform complies with the new General Data Protection Regulation (EU) 2016/679 by ensuring patient pseudonymization, informational separation of powers, secure data storage and security of communication, user authentication, person specific access to data, and recording of access granted to any data. Inclusion criteria are all patients with any form of lipodystrophy (with the exception of HIV-associated lipodystrophy). So far 246 patients from nine centres (Amsterdam, Bologna, Izmir, Leipzig, Münster, Moscow, Pisa, Santiago de Compostela, Ulm) have been recruited. With the help from the six centres on the brink of recruitment (Cambridge, Lille, Nicosia, Paris, Porto, Rome) this number is expected to double within the next one or 2 years.

CONCLUSIONS

A European registry for all patients with lipodystrophy will provide a platform for improved research in the area of lipodystrophy. All physicians from Europe and neighbouring countries caring for patients with lipodystrophy are invited to participate in the ECLip Registry.

STUDY REGISTRATION

ClinicalTrials.gov (NCT03553420). Registered 14 March 2018, retrospectively registered.

Finding the Needle in the Hay Stack: An Open Architecture to Support Diagnosis of Undiagnosed Patients

Clinical Decision Support Systems (CDSS) are promising to support physicians in finding the right diagnosis of patients with rare diseases (RD). The MIRACUM consortium, which includes ten university hospitals in Germany, will establish a diagnosis support system for RD. This system conducts a similarity analysis on distributed clinical data with the aim to identify similar patient cases at each MIRACUM site to offer the physician a hint to a possible diagnosis.

[Registries for rare diseases : OSSE - An open-source framework for technical implementation]

Meager amounts of data stored locally, a small number of experts, and a broad spectrum of technological solutions incompatible with each other characterize the landscape of registries for rare diseases in Germany. Hence, the free software Open Source Registry for Rare Diseases (OSSE) was created to unify and streamline the process of establishing specific rare disease patient registries. The data to be collected is specified based on metadata descriptions within the registry framework's so-called metadata repository (MDR), which was developed according to the ISO/IEC 11179 standard. The use of a central MDR allows for sharing the same data elements across any number of registries, thus providing a technical prerequisite for making data comparable and mergeable between registries and promoting interoperability.With OSSE, the foundation is laid to operate linked patient registries while respecting strong data protection regulations. Using the federated search feature, data for clinical studies can be identified across registries. Data integrity, however, remains intact since no actual data leaves the premises without the owner's consent. Additionally, registry solutions other than OSSE can participate via the OSSE bridgehead, which acts as a translator between OSSE registry networks and non-OSSE registries. The pseudonymization service Mainzelliste adds further data protection.Currently, more than 10 installations are under construction in clinical environments (including university hospitals in Frankfurt, Hamburg, Freiburg and Münster). The feedback given by the users will influence further development of OSSE. As an example, the installation process of the registry for undiagnosed patients at University Hospital Frankfurt is described in more detail.

OSSE Goes FAIR - Implementation of the FAIR Data Principles for an Open-Source Registry for Rare Diseases

The Open Source Registry for Rare Diseases (OSSE) provides a concept and a software for the management of registries for patients with rare diseases. A disease is defined as rare if less than 5 out of 10,000 people are affected. Up to date, approximately 6,000 rare diseases are catalogued. Networking and data exchange for research purposes remains challenging due to the paucity of interoperability and due to the fact that small data stocks are stored locally. The so called "Findable, Accessible, Interoperable, Reusable" (FAIR) Data Principles have been developed to improve research in the field of rare diseases. Subsequently, the OSSE architecture was adapted to implement the FAIR Data Principles. Therefore, the so-called FAIR Data Point was integrated into OSSE to provide a description of metadata in a FAIR manner. OSSE relies on the existing metadata repository (MDR), which is used in to define data elements in the system. This is an important step towards unified documentation across multiple registries. The integration and use of new procedures to improve interoperability plays an important role in the context of registries for rare diseases.

Vaginal delivery versus caesarean section in preterm breech delivery: a systematic review

There is controversy on the preferred mode of delivery (vaginal delivery (VD) versus caesarean section (CS)) in preterm breech delivery in relation to neonatal outcome. While CS is supposed to be safer for the fetus, arguments against CS can be the increased risk of maternal morbidity, risks for future pregnancies, and costs. Moreover, neonatal respiratory distress syndrome occurs more frequently after CS compared to VD. In the past, several RCTs have been started on this subject, but they were all preliminary and stopped due to recruitment difficulties. As the Cochrane review of these RCT's reported on 116 women only, knowledge on the effectiveness of CS and VD can at present only be obtained from non-randomized studies. We performed a systematic review and meta-analysis of non-randomized studies that assessed the association between mode of delivery and neonatal mortality in women with preterm breech presentation. We searched Pubmed, Embase and the Cochrane library for articles comparing neonatal mortality after VD versus CS in preterm breech presentation (gestational age 25(+0) till 36(+6) weeks). Seven studies, involving a total of 3557 women, met the eligibility criteria and were included in this systematic review. The weighted risk of neonatal mortality was 3.8% in the CS group and 11.5% in the VD group (pooled RR 0.63 (95% CI 0.48-0.81)). We conclude that cohort studies indicate that CS reduces neonatal mortality as compared to VD.

Working memory subsystems and task complexity in young boys with Fragile X syndrome

BACKGROUND

Working memory problems have been targeted as core deficits in individuals with Fragile X syndrome (FXS); however, there have been few studies that have examined working memory in young boys with FXS, and even fewer studies that have studied the working memory performance of young boys with FXS across different degrees of complexity. The purpose of this study was to investigate the phonological loop and visual-spatial working memory in young boys with FXS, in comparison to mental age-matched typical boys, and to examine the impact of complexity of the working memory tasks on performance.

METHODS

The performance of young boys (7 to 13-years-old) with FXS (n = 40) was compared with that of mental age and race matched typically developing boys (n = 40) on measures designed to test the phonological loop and the visuospatial sketchpad across low, moderate and high degrees of complexity. Multivariate analyses were used to examine group differences across the specific working memory systems and degrees of complexity.

RESULTS

Results suggested that boys with FXS showed deficits in phonological loop and visual-spatial working memory tasks when compared with typically developing mental age-matched boys. For the boys with FXS, the phonological loop was significantly lower than the visual-spatial sketchpad; however, there was no significant difference in performance across the low, moderate and high degrees of complexity in the working memory tasks. Reverse tasks from both the phonological loop and visual-spatial sketchpad appeared to be the most challenging for both groups, but particularly for the boys with FXS.

CONCLUSIONS

These findings implicate a generalised deficit in working memory in young boys with FXS, with a specific disproportionate impairment in the phonological loop. Given the lack of differentiation on the low versus high complexity tasks, simple span tasks may provide an adequate estimate of working memory until greater involvement of the central executive is achieved.

Chronic sinusitis refractory to standard management in patients with humoral immunodeficiencies

Chronic refractory sinusitis is a common feature in patients with primary immunodeficiencies. The efficacy of standard therapeutic strategies is questionable. In an open trial we evaluated the efficacy of azithromycin, N-acetylcysteine and topical intranasal beclomethasone (100 microg twice daily for 6 weeks) in 16 patients with primary immunodeficiencies (median age 13.5 years, range 5-32 years). All patients suffered from chronic sinusitis despite regular immunoglobulin replacement therapy every 3 weeks. Magnetic resonance imaging (MRI) scans were performed before and after 6 weeks of treatment to evaluate morphological changes in the paranasal sinuses. Nasal swabs and washings were taken for microbial analysis and measurement of inflammatory mediators (IL-8, tumour necrosis factor-alpha (TNF-alpha), eosinophilic cationic protein (ECP)) before and post therapy. Inflammatory mediators in nasal secretions were significantly elevated in patients: IL-8 median 2436 pg/ml (range 441-5435 pg/ml), TNF-alpha 37.3 pg/ml (3.75-524 pg/ml) and ECP 33 ng/ml (1.5-250 ng/ml) versus age-matched healthy controls: IL-8 median 212 pg/ml (99-825 pg/ml), TNF-alpha 3.77 pg/ml (2.8-10.2 pg/ml) and ECP 1.5 ng/ml (1.5-14.8 ng/ml) (P < 0.0001). Inflammation of the maxillary sinuses was confirmed by MRI scans in all patients, additionally infection of the ethmoidal and frontal sinuses was recorded in five patients. Bacterial growth appeared in 11 out of 16 cultures. In spite of therapy, no improvement in sinal inflammation visualized by MRI was achieved. Moreover, no significant decrease in pathogens and levels of inflammatory mediators could be detected (IL-8 1141 pg/ml, 426-4556 pg/ml; TNF-alpha 13.9 pg/ml, 4.1-291.6 pg/ml; ECP 32.3 ng/ml, 3.7-58.4 ng/ml). Our results demonstrate that conventional management of sinusitis is of little benefit in patients with chronic refractory sinusitis with an underlying immunodeficiency. More studies are needed to test antibiotic regimens, probably combined with surgical drainage and anti-inflammatory agents.

Primary Metabolism in Plant Defense (Regulation of a Bean Malic Enzyme Gene Promoter in Transgenic Tobacco by Developmental and Environmental Cues)

NADP-dependent malic enzyme (NADP-ME, EC 1.1.1.40) catalyzes the oxidative decarboxylation of malate to pyruvate, producing CO2 and NADPH. We have examined regulatory properties of a 2.8-kb promoter-leader fragment of a bean (Phaseolus vulgaris L.) NADP-ME gene (PvME1) predicted to encode a cytosolic form of the enzyme by expression analysis of promoter-[beta]-glucuronidase fusions in transgenic tobacco plants. The PvME1 promoter directed strong expression in stems, which was confined to vascular and pith tissues, and was also active in floral and reproductive tissues. Wounding caused a marked induction of promoter activity, which was further strongly enhanced upon application of stimuli related to pathogen defense. Glutathione (reduced form) was the strongest inducer, but oxidized glutathione, fungal elicitor, cellulase, catalase, ascorbic acid, and NADPH were additional potent promoter-stimulating agents. Responsiveness to reduced glutathione was also shown at the level of PvME1 mRNA accumulation in bean plants. The putative contributions of NADP-ME gene expression to the plant defense response and possible mechanisms of defense gene regulation by conditions of oxidative stress as well as by H2O2 and antioxidant levels are discussed.

[Computed tomographic and roentgenologic findings in bronchiolo-alveolar carcinoma of the lung]

Eighteen patients with alveolar carcinomas (ten solitary, eight disseminated) were examined pre-operatively by CT and conventional radiography and the results were compared. The 'pleura fingers' and air bronchogram were shown by conventional tomography as often as by CT in the solitary cases. In the disseminated form, CT is superior by showing small foci in the opposite lung.

[Computed tomographic and roentgenologic evaluation of solitary pulmonary nodules]

One hundred patients with solitary intrapulmonary round lesions were examined by CT and conventional tomography in order to compare their value in reaching a diagnosis. Correct diagnosis as to whether the lesion was malignant was possible in 79% by tomography and in 84% by CT; the type of lesion was diagnosed correctly in 50% and 64% respectively. CT was superior to conventional radiography, particularly for the recognition of calcified tuberculomas. A density value was determined which, when exceeded, always indicated that the lesion was benign.

[Computed tomography in the differential diagnosis of inflammatory and neoplastic lung diseases]

Fourty patients suspected of having a bronchogenic carcinoma but who, in fact, had inflammatory pulmonary lesions were examined by computed tomography. The findings were compared with the CT appearances of 40 patients with bronchogenic carcinomas (20 of these underwent surgery). In 28 patients (70%) suspected of having a bronchogenic carcinoma, the CT findings indicated an inflammatory lesion. As might have been expected, there was no single CT criterion which is found only in inflammatory lesions. Chronic inflammatory processes and inflammatory pseudo-tumours (chronic pneumonias and tuberculosis) cannot be distinguished from malignant tumours by CT (12 out of 40 patients, 30%).

[Pre-operative staging of bronchial carcinomas: a comparison of computed tomography and conventional radiography (author's transl)]

Pre-operative staging was carried out in 52 out of 112 patients suspected of having bronchial carcinomas, using computed tomography and conventional radiology. CT was superior in demonstrating tumour infiltration of the mediastinum, of the thoracic wall and of metastases in mediastinal lymph nodes. Conventional radiology was better for demonstrating intrabronchial tumour and metastases of bronchopulmonary lymph nodes. By extending CT to the upper abdomen, it was possible to show metastases in 18% of patients.

[Clinical relevance of 111In-bleomycine in intrathoracal neoplasms (author's transl)]

With a medium storage ratio of 1:1.4 between sound and neoplastic lung tissue additional diagnostic information cannot be obtained by positive tumor scintigraphy with 111In-bleomycine. This may be accounted for on the one hand by the diffuse borders of the storage foci, on the other hand by the relatively high storage of radioactivity in normal lung tissue.