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1.
Hamostaseologie ; 39(3): 284-293, 2019 Aug.
Article in English | MEDLINE | ID: mdl-30419589

ABSTRACT

The Austrian Haemophilia Registry collects epidemiological data on patients with haemophilia, on treatment modalities and potential side effects. The Registry covers more than 85% of the assumed total number of haemophilia patients in Austria. This report summarizes data on 753 patients: 84.3% (635) have haemophilia A and 15.7% (118) have haemophilia B. Patients' median age is 34 years (range: 1-93 years). Of the total cohort, 39.0% (294) patients have severe haemophilia, 11.3% (85) moderate haemophilia, and 49.4% (372) mild haemophilia. Of the patients with severe haemophilia, 38.4% (113) have been infected with hepatitis C virus (HCV) and 12.6% (37) are human immunodeficiency virus (HIV) positive. Overall, 10.6% (67) of patients with haemophilia A and 1.7% (2) of those with haemophilia B have had an inhibitor in their history. Among patients with severe haemophilia, 68.4% (201) receive prophylaxis and 28.6% (84) receive on-demand therapy. There are 65.0% (191) patients with severe haemophilia who are treated with recombinant products. In conclusion, most patients with severe haemophilia receive prophylactic treatment. HCV and HIV infections are still important issues in the Austrian haemophilia population.


Subject(s)
HIV Infections/epidemiology , Hemophilia A/drug therapy , Hemophilia A/epidemiology , Hepatitis C/epidemiology , Adolescent , Adult , Aged , Aged, 80 and over , Austria/epidemiology , Child , Child, Preschool , Combined Modality Therapy , Female , HIV Infections/complications , Hemophilia A/complications , Hemorrhage/prevention & control , Hepatitis C/complications , Humans , Infant , Male , Middle Aged , Prevalence , Registries , Severity of Illness Index , Young Adult
2.
Stud Health Technol Inform ; 223: 142-9, 2016.
Article in English | MEDLINE | ID: mdl-27139397

ABSTRACT

BACKGROUND: Electronic case report forms (eCRF) play a key role in medical studies and medical data registries (MDR) for clinical research. The creation of suitable eCRFs is a challenging and yet a very individual task. OBJECTIVES: We plan to create evidence-based templates for eCRFs which are aligned with existing studies or MDRs. METHODS: In this paper we investigated existing standards for eCRFs, defined uses cases and derived requirements needed to identify and annotate data items and pertinent information within study protocols, literature or patient cases. RESULTS: In order to establish evidence-based eCRF templates based on annotated text documents, a standard-based, hierarchical structure with linkage to existing data repositories needs to be modeled. Standards like ISO/IEC 11179 provide a necessary base, which needs to be extended with proper linking functions. CONCLUSION: Linking evidence-based sources with eCRFs allows creating templates, which could be used to define eCRFs for new clinical studies or even compare studies among them. As a next step, the derived requirements from this paper will be used to establish an ontology-based structure for annotating existing text-documents with eCRF data elements.


Subject(s)
Data Collection/methods , Documentation , Electronic Health Records/standards , Humans , Medical Records Systems, Computerized , Registries
3.
Stud Health Technol Inform ; 190: 112-4, 2013.
Article in English | MEDLINE | ID: mdl-23823393

ABSTRACT

The term medical registry is used to reference tools and processes to support clinical or epidemiologic research or provide a data basis for decisions regarding health care policies. In spite of this wide range of applications the term registry and the functional requirements which a registry should support are not clearly defined. This work presents preliminary results of a literature review to discover functional requirements which form a registry. To extract these requirements a set of peer reviewed articles was collected. These set of articles was screened by using methods from qualitative research. Up to now most discovered functional requirements focus on data quality (e. g. prevent transcription error by conducting automatic domain checks).


Subject(s)
Data Mining/methods , Databases, Factual , Information Storage and Retrieval/methods , Natural Language Processing , Registries , Terminology as Topic , Pilot Projects
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