BackgroundWorldwide approximately 7,000 rare diseases have been identified. Accordingly, 4 million individuals live with a rare disease in Germany. The mean time to diagnosis is about 6 years and patients receive several incorrect diagnoses during this time. A multiplicity of factors renders diagnosing a rare disease extremely difficult. Detection of shared phenomena among individuals with different rare diseases could assist the diagnostic process. In order to explore the demand for diagnostic support and to obtain the commonalities among patients, a nationwide Delphi survey of centers for rare diseases and patient groups was conducted.MethodsA two-step Delphi survey was conducted using web-based technologies in all centers for rare diseases in Germany. Moreover, the leading patient support group, the German foundation for rare diseases (ACHSE), was contacted to involve patients as experts in their disease. In the survey the experts were invited to name rare diseases with special need for diagnostic improvement. Secondly, communal experiences of affected individuals were collected.Results166 of 474 contacted experts (35%) participated in the first round of the Delphi process and 95 of 166 (57%) participated in the second round. Metabolic (n = 74) and autoimmune diseases (n = 39) were ranked the highest for need for diagnostic support. For three diseases (i.e. scleroderma, Pompe’s disease, and pulmonary arterial hypertension), a crucial need for diagnostic support was explicitly stated. A typical experience of individuals with a rare disease was stigmatization of having psychological or psychosomatic problems. In addition, most experts endured an ‘odyssey’ of seeing many different medical specialists before a correct diagnosis (n = 38) was confirmed.ConclusionThere is need for improving the diagnostic process in individuals with rare diseases. Shared experiences in individuals with a rare disease were observed, which could possibly be utilized for diagnostic support in the future.

BackgroundRare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established.ObjectiveWe aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.Methods20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires.ResultsThe questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY.ConclusionDespite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.

Zusammenfassung Hintergrund Die Diagnosestellung einer seltenen Stoffwechselerkrankung stellt eine Herausforderung für Familien und betreuende Ärzte dar. Um den Weg zur Diagnose zu unterstützen, wurde ein diagnostisches Werkzeug entwickelt, welches die Erfahrungen Betroffener nutzt. Methoden 17 Interviews mit Eltern oder Betroffenen einer ausgewählten, seltenen Stoffwechselerkrankung (Mukopolysaccharidose (MPS), M. Fabry und M. Gaucher) wurden durchgeführt. Die Ergebnisse wurden in diagnostischen Fragebogen abgebildet. Die Fragebogen wurden verteilt und von Eltern oder Betroffenen mit einer gesicherten Diagnose einer MPS, eines M. Fabry oder eines M. Gaucher beantwortet. Vier kombinierte Data Mining Klassifikatoren wurden trainiert, um in den beantworteten Fragebogen Antwortmuster zu finden. Ergebnisse Das binäre Data Mining System wurde mit 56 Fragebogen trainiert und erzielte eine Anzahl von 91% richtigen Diagnosen für die Diagnose ‚MPS’. Weitere 20 Fragebogen, die nicht Teil des Trainingsdatensatzes waren, konnten als ein erster prospektiver Test ausgewertet werden. Das System erkannte bei diesen 20 Fragebogen 18 bzw. 90% korrekte Diagnosen. Diskussion und Schlussfolgerung Fragebogen zur Diagnoseunterstützung basierend auf Interviews mit Eltern und Betroffenen wurden entwickelt und Antwortmuster durch Data Mining Verfahren ausgewertet. Diese vorläufigen Ergebnisse illustrieren, dass Data Mining Systeme Muster in Fragebogen erkennen können. Dieser Ansatz könnte zukünftig hilfreich bei der Erkennung ausgewählter Stoffwechselerkrankungen sein.