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.

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.

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.

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.

Conception of an Expert Advisory Board for the European Reference Network for Rare Respiratory Diseases.

Due to low prevalence of rare diseases and lack of expertise, patients suffering rare diseases are challenged with finding experts that are specialized and experienced in treating their conditions and get qualified answers. To address this issue, the approach of an interactive platform was made, which should allow affected patients or physicians the possibility to ask experts their specific questions to gather and improve their knowledge about their condition. This paper introduces the pan-European Internet platform EXABO, an Expert Advisory Board, which is developed within the framework of the European Reference Network for rare respiratory diseases (ERN-LUNG). The purpose of establishing this platform is initially dedicated to rare respiratory diseases in whole Europe, whilst the set-up is however, planned to be a generic tool for additional diseases or groups of diseases. This paper gives an overview of the concept and planning of the EXABO platform and shows how the platform is to be implemented.

Mapping of Health Care Providers for People with Rare Diseases - From Vision to Implementation.

se-atlas - the health service information platform for rare diseases - is part of the German National Action Plan for People with Rare Diseases. The website www.se-atlas.de provides an overview of health care providers and support groups focusing on rare diseases in Germany. Since the start of se-atlas in 2013, several strategies are being developed and evaluated. This paper gives an overview about the expectations and visions for se-atlas at the beginning of the project, the challenges and lessons learned within the project period and how se-atlas is implemented today.

2,3,7,8-Tetrachlorodibenzo-p-dioxin induced cytochrome P450s alter the formation of reactive oxygen species in liver cells.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) was classified by the International Agency for Research on Cancer as a carcinogen in humans. It acts through an aryl hydrocarbon receptor-mediated mechanism, inducing the transcription of numerous genes, including various cytochrome P450s (CYPs - CYP1A1, 1A2, 1B1). Induction of CYPs may lead to genotoxicity by generating reactive oxygen species (ROS) which can damage DNA directly and/or via the generation of reactive metabolites. We determined ROS formation with the 2',7'-dihydrodichlorofluorescein diacetate fluorescence assay after incubation of HepG2 hepatoma cells or primary rat hepatocytes with TCDD. The amount of 8-oxo-2'-deoxyguanosine (8-oxo-dG) in DNA was measured using HPLC-MS/MS, the amount of CYP1A1 protein by Western blotting. The catalytic activity of CYP1A enzymes was determined as 7-ethoxyresorufin-O-deethylase (EROD) activity. Incubation of cells with TCDD for 48 h caused increased levels of ROS in primary rat hepatocytes as well as increased levels of 8-oxo-dG in DNA compared to untreated cells. In the HepG2 cell line no significant effects were observed for both ROS formation and 8-oxo-dG levels. Both effects were in good agreement with the extent of induction of CYP1A1 protein and EROD activity, suggesting that CYP1 induction is a major source of ROS formation in TCDD-treated hepatocytes.

Ghrelin-reactive immunoglobulins and anxiety, depression and stress-induced cortisol response in adolescents. The TRAILS study.

BACKGROUND: Ghrelin, a hunger hormone, has been implicated in the regulation of stress-response, anxiety and depression. Ghrelin-reactive immunoglobulins (Ig) were recently identified in healthy and obese humans showing abilities to increase ghrelin's stability and orexigenic effects. Here we studied if ghrelin-reactive Ig are associated with anxiety and depression and with the stress-induced cortisol response in a general population of adolescents. Furthermore, to test the possible infectious origin of ghrelin-reactive Ig, their levels were compared with serum IgG against common viruses. METHODS: We measured ghrelin-reactive IgM, IgG and IgA in serum samples of 1199 adolescents from the Dutch TRAILS study and tested their associations with 1) anxiety and depression symptoms assessed with the Youth Self-Report, 2) stress-induced salivary cortisol levels and 3) IgG against human herpesvirus 1, 2, 4 and 6 and Influenza A and B viruses. RESULTS: Ghrelin-reactive IgM and IgG correlated positively with levels of antibodies against Influenza A virus. Ghrelin-reactive IgM correlated negatively with antibodies against Influenza B virus. Ghrelin-reactive IgM correlated positively with anxiety scores in girls and ghrelin-reactive IgG correlated with stress-induced cortisol secretion, but these associations were weak and not significant after correction for multiple testing. CONCLUSION: These data indicate that production of ghrelin-reactive autoantibodies could be influenced by viral infections. Serum levels of ghrelin-reactive autoantibodies probably do not play a role in regulating anxiety, depression and the stress-response in adolescents from the general population.

Corticotropin (ACTH)-reactive immunoglobulins in adolescents in relation to antisocial behavior and stress-induced cortisol response. The TRAILS study.

Elevated levels of corticotropin (ACTH)-reactive immunoglobulins (ACTH IgG) were found in males with conduct disorder, suggesting their involvement in the biology of antisocial behavior. We first aimed to confirm these findings in a large general population sample of adolescents. Secondly, we studied the association between ACTH IgG levels and hypothalamic-pituitary-adrenal (HPA) axis response to stress. Free and total ACTH IgG levels were measured in sera of 1230 adolescents (15-18 years). HPA axis activity was determined by measuring salivary cortisol before, during, and after a social stress test. Antisocial behavior was assessed using the Antisocial Behavior Questionnaire. ACTH peptide and IgG affinity kinetics for ACTH were assayed in a subsample of 90 adolescents selected for high or low ACTH IgG levels. In boys, higher total ACTH IgG levels were associated with higher antisocial behavior scores (ß=1.05, p=0.04), especially at high levels of free ACTH IgG. In girls, antisocial behavior was associated with low free ACTH IgG levels (ß=-0.20, p=0.04). Stress-induced cortisol release was associated with free ACTH IgG in boys (ßareaunderthecurve=-0.67, p<0.01), and with total ACTH IgG in girls (ßrecovery=0.84, p=0.05). The affinity kinetics assay showed that ACTH IgG association rates were lower in both boys and girls with high ACTH IgG levels. These data show that ACTH IgG levels are related to antisocial behavior and HPA axis response to stress in adolescents. The mechanisms behind these associations, including different ACTH binding properties of IgG in subjects with antisocial behavior, deserve further attention.