Background: Rare diseases affect approximately 20 million Europeans, presenting unique challenges such as delayed diagnoses, limited therapies, and significant personal and financial burden. While resilience-supporting factors such as peer support are available and artificial intelligence-based diagnostic tools are being developed further, there is a lack of a dedicated online social network connecting patients, caregivers, relatives, and experts. This study presents the development and preliminary findings of Unrare.me, a novel social network designed to provide a secure space for experts and individuals affected by rare and chronic diseases (diagnosed and undiagnosed). Objective: This study aimed to design, develop, and evaluate a social networking platform tailored to the needs of different stakeholders of the rare disease community, facilitating interaction, knowledge exchange, and emotional support while prioritizing data security. Methods: This multidisciplinary, multicenter initiative brought together patient groups, health care professionals, psychologists, and web design experts. A literature review assessed existing networking approaches in the rare disease community. Structured interviews and user journey mapping defined user needs and essential app features. Iterative prototyping and stakeholder discussions informed the final design, which was developed into a functional app launched in December 2023 on major platforms. A survey conducted four months post-launch evaluated user feedback. Data security was prioritized throughout development. Results: A total of 270 users (approximately 1 in 7 users at the time) participated in the evaluation. Most of them (n=221, 81.9%) registered to connect with others in similar situations, whereas 56.7% (n=153) sought expert input and 44.4% (n=120) looked for disease-related information. The app received positive ratings for usability (mean 6.12, SD 1.03; out of 7), accessibility (mean 5.59, SD 1.22), and design (mean 5.84, SD 1.12), as well as overall impression (mean grade of 2.24, SD 0.90 on a scale from 1-6, with 1 being the best score). Data security was highly rated (mean 5.58, SD 1.15). The app's ontology was suitable for 77% (n=208) of the participants, enabling them to find their diagnosis, and 60.7% (n=164) of users found at least one match. Matching preferences centered on shared diagnosis (mean 82.5, SD 25.1 on a visual analog scale from 0 to 100), symptoms (mean 74.2, SD 25.8), and everyday experiences (mean 69.6, SD 29.5). Overall, users welcomed the opportunity to network with each other securely and highlighted areas for further improvement, such as enhanced matching features and group chat options. Conclusions: Unrare.me has generated significant interest and engagement within the German rare disease community, serving as a valuable tool for peer support, knowledge sharing, and expert identification. Current challenges include optimizing user acquisition and refining matching algorithms. Planned features include group chats, expert interaction, and gamification elements. Unrare.me illustrates the potential of tailored digital solutions to address unmet needs in the rare disease community.

INTRODUCTION:Rare diseases (RD) are characterized by chronicity and may be associated with reduced life expectancy and quality of life. Case series and reports regarding pregnancies in individuals with specific RD exist, but there is no data on the outcome of pregnancies in the overall group. MATERIAL AND METHODS:A retrospective analysis was conducted of all pregnancies in women with RD who were managed at our center between January 2018 and July 2022. Maternal, fetal, and obstetric parameters were recorded. RESULTS:During the study period, 388 pregnant women with 434 RD were managed. Of these, 11.9% had more than one RD. The breakdown of conditions was as follows: 50.7% acquired diseases, 21% congenital diseases excluding malformations, 17.5% malformations, and 10.8% tumors. Disease-specific complications occurred in 23.2% of women, and pregnancy-specific complications in 25.1% of live births. Women with preconception stability experienced significantly fewer complications. The cesarean section rate was 50.6%. Preterm birth occurred in 15.3% of cases, and 20.4% of newborns required admission to the neonatal intensive care unit. CONCLUSIONS:Women with RD experience a high rate of disease-specific and pregnancy complications. Preconception stability is a key factor for an uncomplicated course of pregnancy and birth.

BACKGROUND:Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals, offer targeted diagnostic and therapeutic care to reduce diagnostic delays. Tools like "Isabel Healthcare" can support clinicians by streamlining the differential diagnosis process and aiding in the accurate identification of rare conditions. RESULTS:The study included 100 patients with a mean age of 44 years. "Isabel Healthcare DDx companion" and the interdisciplinary case conferences generated a total of 727 diagnosis suggestions. Among the top ten diagnoses suggested by "Isabel Healthcare DDx companion", 28% matched at least one diagnosis identified during the interdisciplinary case conferences. The diagnoses suggested as "more likely" by "Isabel Healthcare DDx companion" showed a higher correlation with the differential diagnoses and procedures identified during the interdisciplinary case conferences, suggesting a potential alignment in clinical decision-making processes. CONCLUSION:This study has demonstrated the potential of the differential diagnostic tool "Isabel Healthcare DDx companion" to assist in patient diagnosis. However, discrepancies between the tool's findings and expert decisions suggest that, although it can support clinicians in decision-making, its independent effectiveness may be limited by accurately filtering and interpreting the essential medical history required for a precise diagnosis.

BACKGROUND:Rare diseases often present with a variety of clinical symptoms and therefore are challenging to diagnose. Fabry disease is an x-linked rare metabolic disorder. The severity of symptoms is usually different in men and women. Since therapeutic options for Fabry disease exist, early diagnosis is important. An artificial intelligence (AI)-based diagnosis support algorithm for rare diseases has been developed in preliminary studies. OBJECTIVE:Our aim was to extend and train the questionnaire-based AI, capable of distinguishing patients with from those without rare diseases, to achieve satisfactory sensitivity for the detection of a single rare disease, Fabry disease, taking into account gender differences in disease perception. METHODS:We collected 33 complete datasets from patients with confirmed Fabry disease. These records contained answered AI questionnaires, general information on disease progression, demographic information and quality of life (QoL) measures. The AI was trained to distinguish patients with Fabry disease from patients with relevant differential diagnoses. Its performance was assayed using stratified eleven-fold cross-validation and ROC curve calculation. Variables influencing the performance of the AI were examined with linear regression and calculation of the coefficient of determination. RESULT:We were able to show that a relatively small sample is sufficient to achieve a sensitivity of 88.12% for the presence of Fabry disease, taking into account gender-specific differences in the disease perception during the pre-diagnostic phase. No confounders of the tool's performance could be found in the data collected concerning the patients' quality of life and diagnostic history. CONCLUSION:This study illustrates on the example of Fabry disease that differences between female and male Fabry patients, not only in the expression of symptoms, but also with regard to disease perception, might be relevant influencing variables for improving the performance of AI-based diagnostic support tools for rare diseases.

Individuals with ultrarare disorders pose a structural challenge for healthcare systems since expert clinical knowledge is required to establish diagnoses. In TRANSLATE NAMSE, a 3-year prospective study, we evaluated a novel diagnostic concept based on multidisciplinary expertise in Germany. Here we present the systematic investigation of the phenotypic and molecular genetic data of 1,577 patients who had undergone exome sequencing and were partially analyzed with next-generation phenotyping approaches. Molecular genetic diagnoses were established in 32% of the patients totaling 370 distinct molecular genetic causes, most with prevalence below 1:50,000. During the diagnostic process, 34 novel and 23 candidate genotype–phenotype associations were identified, mainly in individuals with neurodevelopmental disorders. Sequencing data of the subcohort that consented to computer-assisted analysis of their facial images with GestaltMatcher could be prioritized more efficiently compared with approaches based solely on clinical features and molecular scores. Our study demonstrates the synergy of using next-generation sequencing and phenotyping for diagnosing ultrarare diseases in routine healthcare and discovering novel etiologies by multidisciplinary teams.

Abstract Purpose Pain and anxiety-inducing interventions have a major impact on pediatric patients. Pain reduction by virtual reality (VR) during port and vein punctures is well studied. This study investigates peri-interventional reduction of pain, anxiety and distress using VR compared to the standard of care (SOC) in a pediatric oncology outpatient clinic. Methods In a randomized, controlled cross-over design, patients aged 6–18 years experience potentially painful interventions accompanied by VR. Observational instruments include NRS, FPS-r, BAADS, mYPAS-SF, PedsQL and SSKJ3-8R. All patients undergo two observations: SOC (A) and VR (B) in a randomized order. In addition, parents and staff are interviewed. Specific conditions for VR in an outpatient clinic setting derived from interprofessional focus group discussion are being explored. Results Between July 2021 and December 2022 57 eligible patients were included and randomized to the orders A/B (n = 28) and B/A (n = 29). Thirty-eight patients completed both observations. Characteristics in both groups did not differ significantly. More than half of the patients had no previous experience with VR, 5% decided to discontinue VR prematurely. Peri-interventional pain, anxiety and distress were significantly reduced by VR compared with SOC. 71% of patients and 76% of parents perceived punctures with VR to be more relaxed than previous ones. 95% of patients perceived fun with VR goggles. Detailed questionnaires on individual stress and anxiety were returned from 26 of 38 patients. Focus group discussion with staff yielded evidence for successful implementation of VR in an outpatient clinic. Conclusions The present study shows that VR can be used for peri-interventional reduction of pain, anxiety, and distress in the special environment of a pediatric outpatient clinic. Specific conditions must be met for successful implementation. Further studies are needed to identify particularly susceptible patients and to illuminate alternatives for distraction that are feasible to implement with limited resources. Trial registration number (ClinicalTrials.gov ID): NCT06235723; 01/02/2024; retrospectively registered. This study adheres to the standard checklist of CONSORT guidelines.

: McArdle disease is a genetic glycogen storage disease characterised by impaired muscle metabolism. Although typical clinical features such as physical activity intolerance, muscle pain, cramps and weakness and second wind phenomenon can be identified through careful history taking, delay in diagnosis is still a common problem. This article aims to support timely diagnosis by highlighting the classic anamnestic and clinical features of the disease. Additionally, it provides impulses for structured and continuous medical care for people with a chronic illness.

Background: A major challenge faced by patients with rare diseases (RDs) often stems from delays in diagnosis, typically due to nonspecific clinical symptoms or doctors’ limited experience in connecting symptoms to the underlying RD. Using patient-oriented questionnaires (POQs) as a data source for machine learning (ML) techniques can serve as a potential solution. These questionnaires enable patients to portray their day-to-day experiences living with their condition, irrespective of clinical symptoms. This systematic review—registered at PROSPERO with the Registration-ID: CRD42023490838—aims to present the current state of research in this domain by conducting a systematic literature search and identifying the potentials and limitations of this methodology. Methods: The review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was primarily funded by the German Federal Ministry of Education and Research under grant no. 16DHBKI056 (ki4all). The methodology involved a systematic search across the databases PubMed, Semantic Scholar and Google Scholar, covering articles published until June 2023. The inclusion criteria encompass examining the use of POQs in diagnosing rare and common diseases. Additionally, studies that focused on applying ML techniques to the resulting datasets were considered for inclusion. The primary objective was to include English as well as German research that involved the generation of predictions regarding the underlying disease based on the information gathered from POQs. Furthermore, studies exploring identifying predictive indicators associated with the underlying disease were also included in the literature review. The following data were extracted from the selected studies: year of publication, number of questions in the POQs, answer scale in the questionnaires, the ML algorithms used, the input data for the ML algorithms, the performance of these algorithms and how the performance was measured. In addition, information on the development of the questionnaires was recorded. Results: This search retrieved 421 results in total. After one superficial and two comprehensive screening runs performed by two authors independently, we ended up with 26 studies for further consideration. Sixteen of these studies deal with diseases and ML algorithms to analyse data; the other ten studies provide contributing research in this field. We discuss several potentials and limitations of the evaluated approach. Conclusions: Overall, the results show that the full potential has not yet been exploited and that further research in this direction is worthwhile, because the study results show that ML algorithms can achieve promising results on POQ data; however, their use in everyday medical practice has not yet been investigated.

Rational/Aims and ObjectivesWard rounds are a core routine for interprofessional communication and clinical care planning: Health care professionals and patients meet regularly and it encourages patients to actively participate. In paediatric oncology, the long treatment process, the serious diagnosis, and involvement of both patients and their parents in shared-decision-making require specific ward round skills. Despite its high value for patient-centred care, a universal definition of ward round is lacking. Little is known about attitudes and expectations of different participants towards a 'good' ward round. This study aims to capture experiences and expectations of different stakeholders to better understand ward round needs in paediatric oncology and serve as a basis to improve future ward rounds.MethodSemi-structured interviews were conducted with patients, parents, nurses and medical doctors of a paediatric oncology ward until theoretical saturation (13 interviews). A standardised qualitative analysis using the phenomenological framework defined by Colaizzi was used to identify important aspects in the interviews.ResultsThree major themes were identified in the interviews: [1] Structure and Organisation; [2] Communication; [3] Education. Further analysis revealed 23 categories and elucidated several opportunities and unmet needs recognized by stakeholders: Ward round functions in comforting families in stressful situations, and relationship building. Interviewees expressed their concerns about missing structures. Families pleaded for smaller ward round teams and layperson language. Health care professionals underscored the lack of ward round training. Paediatric patients stated that ward round scared them without proper explanation. All interviewees emphasized the need for professionalization of the ward round in the setting of paediatric oncology.ConclusionThis study gives important insights into ward round functions and organisational requirements. It addresses special challenges for ward round participants in paediatric oncology, such as consideration of the emotional aspect of cancer treatment or the limits of shared decision making. Furthermore, this study underscores the great significance of ward rounds in paediatric oncology, with an emphasis on communication and relationship-building. Although performed universally, ward rounds are poorly explored or evaluated. This structured analysis synthesizes important expectations of different WR stakeholders, revealing opportunities of improvement and stressing the need for guidelines, training, and preparation.

Background and objective The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. Methods Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. Results Pain2D was able to classify the four rare diseases with an accuracy of 61–77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. Conclusions Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.

Zusammenfassung Hintergrund Die Lehre in der Kinderheilkunde ist hinsichtlich ihrer Qualität und ihres Gelingens wesentlich von den individuellen Einstellungen der Lehrenden zur Lehre, von ihren Kenntnissen und ihrem individuellen Engagement abhängig. Nicht zuletzt mit dem Ziel einer kontinuierlichen Verbesserung der Ausbildungsqualität sollten diese Einstellungen stärker wahrgenommen und reflektiert werden. Fragestellung und Methodik Diese Arbeit untersucht das Selbstbild von Kinderärzt:innen als Lehrende an 6 deutschen Universitätskliniken, ihre Einstellung zur Lehre und ihre Erwartungen an die Lehre. Hierfür wurde ein Fragebogen zur „teacher identity“ (TI) erstmalig aus dem Amerikanischen ins Deutsche übersetzt und eingesetzt. Der Fragebogen zur TI setzt sich aus 9 Subskalen zusammen: i) globale TI, ii) intrinsische Befriedigung durch Lehre, iii) Kenntnisse und Fertigkeiten, iv) Zugehörigkeit zu einer Gruppe von Lehrenden, v) Überzeugung, dass ein Mediziner immer ein Lehrender sein muss, vi) Verantwortlichkeit für Lehre, vii) Teilen klinischer Expertise, viii) Honorierung von Lehre und ix) Wünsche an die Lehre. Ergebnisse und Diskussion 252 Kolleg:innen haben an der Befragung teilgenommen. Es zeigten sich einerseits ein hohes Maß an Identifizierung mit der Lehre, aber ebenso auch Bereiche mit gewünschtem Verbesserungspotenzial. Männer zeigten höhere TI-Werte als Frauen. Eine angestrebte oder bereits eingeschlagene universitär-klinische Laufbahn korrelierte ebenfalls mit höheren TI-Werten. Es fanden sich keine Unterschiede zwischen den teilnehmenden Kliniken. Zusätzlich belegen Daten zur Lehrleistung und zu absolvierten Fortbildungen im Bereich der Lehre ein hohes Maß an Engagement der Kinder- und Jugendärzte:innen in der studentischen Lehre.

Zusammenfassung Hintergrund Die Lehre in der Kinderheilkunde ist hinsichtlich ihrer Qualität und ihres Gelingens wesentlich von den individuellen Einstellungen der Lehrenden zur Lehre, von ihren Kenntnissen und ihrem individuellen Engagement abhängig. Nicht zuletzt mit dem Ziel einer kontinuierlichen Verbesserung der Ausbildungsqualität sollten diese Einstellungen stärker wahrgenommen und reflektiert werden. Fragestellung und Methodik Diese Arbeit untersucht das Selbstbild von Kinderärzt:innen als Lehrende an 6 deutschen Universitätskliniken, ihre Einstellung zur Lehre und ihre Erwartungen an die Lehre. Hierfür wurde ein Fragebogen zur „teacher identity“ (TI) erstmalig aus dem Amerikanischen ins Deutsche übersetzt und eingesetzt. Der Fragebogen zur TI setzt sich aus 9 Subskalen zusammen: i) globale TI, ii) intrinsische Befriedigung durch Lehre, iii) Kenntnisse und Fertigkeiten, iv) Zugehörigkeit zu einer Gruppe von Lehrenden, v) Überzeugung, dass ein Mediziner immer ein Lehrender sein muss, vi) Verantwortlichkeit für Lehre, vii) Teilen klinischer Expertise, viii) Honorierung von Lehre und ix) Wünsche an die Lehre. Ergebnisse und Diskussion 252 Kolleg:innen haben an der Befragung teilgenommen. Es zeigten sich einerseits ein hohes Maß an Identifizierung mit der Lehre, aber ebenso auch Bereiche mit gewünschtem Verbesserungspotenzial. Männer zeigten höhere TI-Werte als Frauen. Eine angestrebte oder bereits eingeschlagene universitär-klinische Laufbahn korrelierte ebenfalls mit höheren TI-Werten. Es fanden sich keine Unterschiede zwischen den teilnehmenden Kliniken. Zusätzlich belegen Daten zur Lehrleistung und zu absolvierten Fortbildungen im Bereich der Lehre ein hohes Maß an Engagement der Kinder- und Jugendärzte:innen in der studentischen Lehre.

Abstract Mucopolysaccharidosis type I (MPS I) is an autosomal‐recessive metabolic disorder caused by an enzyme deficiency of lysosomal alpha‐l‐iduronidase (IDUA). Haematopoietic stem cell transplantation (HSCT) is the therapeutic option of choice in MPS I patients younger than 2.5 years, which has a positive impact on neurocognitive development. However, impaired growth remains a problem. In this monocentric study, 14 patients with MPS I (mean age 1.72 years, range 0.81–3.08) were monitored according to a standardised follow‐up program after successful allogeneic HSCT. A detailed anthropometric program was carried out to identify growth patterns and to determine predictors of growth in these children. All patients are alive and in outpatient care (mean follow‐up 8.1 years, range 0.1–16.0). Progressively lower standard deviation scores (SDS) were observed for body length (mean SDS −1.61; −4.58 – 3.29), weight (−0.56; −3.19 – 2.95), sitting height (−3.28; −7.37 – 0.26), leg length (−1.64; −3.88 – 1.49) and head circumference (0.91; −2.52 – 6.09). Already at the age of 24 months, significant disproportions were detected being associated with increasing deterioration in growth for age. Younger age at HSCT, lower counts for haemoglobin and platelets, lower potassium, higher donor‐derived chimerism, higher counts for leukocytes and recruitment of a matched unrelated donor (MUD) positively correlated with body length (p ≤ 0.05). In conclusion, this study characterised predictors and aspects of growth patterns in children with MPS I after HSCT, underlining that early HSCT of MUD is essential for slowing body disproportion.