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  • 1
    In: Prävention und Gesundheitsförderung, Springer Science and Business Media LLC
    Abstract: As the number of concomitantly used drugs increases, the prevalence of medication risks increases. These include, for example, drug interactions which may reduce or increase the desired and undesired effects of individual drugs. Objectives The POLypharmacy, drug interActions and Risks (POLAR) project aims to contribute to the detection of medication risks in patients with polymedication using methods and processes of the Medical Informatics Initiative (MII) on the basis of “real world data” (inpatient treatment data of university hospitals). In the article, the specific clinical problems are presented and illustrated based on an evaluation example. Materials and methods Specific pharmacological questions are mapped algorithmically and applied in distributed analyses in 13 data integration centers. An essential prerequisite for the application of these algorithms is the core data set structure of the MII, which relies on international information technology, interoperability, and terminology standards. Results In POLAR, it was demonstrated for the first time that inpatient treatment data can be made usable across sites for research questions on drug-related problems based on coordinated, interoperable data exchange formats in a privacy-compliant manner. Conclusions An interim report from the POLAR project shows the first, preliminary result of an analysis. In addition, more general discussions of technical, legal, communicative opportunities and challenges are presented, with a focus on using inpatient treatment data as “real world data” for research purposes.
    Type of Medium: Online Resource
    ISSN: 1861-6755 , 1861-6763
    RVK:
    Language: German
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2211781-7
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  • 2
    In: Journal of Clinical Medicine, MDPI AG, Vol. 11, No. 14 ( 2022-07-18), p. 4167-
    Abstract: Contraindications (CIs) in Summaries of Product Characteristics (SmPCs)/Prescribing Information (PI) that lack clarity may pose a risk to medication safety and increase the risk for adverse drug reactions. We assessed and compared SmPCs/PI from three major drug markets regarding comprehensibility from the prescriber perspective, as well as usability in clinical decision support systems. 158 drugs met the following inclusion criteria: marketed in Germany (DE), United Kingdom (UK) and United States (US) and belonged to the 100 most recently FDA approved and/or 100 most frequently prescribed drugs in either country. In the 474 (3 × 158) SmPCs/PI all expressions for absolute CIs were identified, divided into 3999 stand-alone terms and evaluated according to ‘clarity’ and ‘codability’. The average number of absolute CIs per drug differed drastically between the three markets (DE: 11.7, UK: 9.0, US: 4.6). Expressions were frequently unclear (DE: 27.2% (95% CI 25.2–29.2%), UK: 28.5% (26.2–30.9%), US: 22.6% (19.7–25.8%)). Moreover, 60.9% (58.6–63.1%), 63.6% (61.0–66.0%), and 64.7% (61.2–68.1%) of the expressions were not codable in DE, UK, and US, respectively. Taken together, in three major drug markets, statements regarding CIs in SmPCs/PI substantially differ in frequency and frequently lack clarity and codability which poses an unnecessary obstacle to medication safety.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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  • 3
    In: British Journal of Clinical Pharmacology, Wiley, Vol. 88, No. 12 ( 2022-12), p. 5399-5411
    Abstract: Automated checks for medication‐related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug–drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug–disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses. Methods We identified drugs that are associated with diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert‐based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real‐life sample of medication and diagnosis data excerpts from 3506 discharge records of geriatric patients. Results Depending on the algorithm, the desired focus (i.e., sensitivity vs . specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0–99.8% (95% CI 41.7–100.0%) and a sensitivity of 3.8–83.1% (95% CI 1.0–86.1%). Using only medication data, we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses. Conclusion This study provides a proof of principle that some key diagnosis‐related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug–disease contraindications in community pharmacy or clinical routine data.
    Type of Medium: Online Resource
    ISSN: 0306-5251 , 1365-2125
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1498142-7
    SSG: 15,3
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2022
    In:  British Journal of Clinical Pharmacology Vol. 88, No. 1 ( 2022-01), p. 226-236
    In: British Journal of Clinical Pharmacology, Wiley, Vol. 88, No. 1 ( 2022-01), p. 226-236
    Abstract: A substantial number of Summaries of Product Characteristics (SmPCs)/Prescribing Information (PI) have warnings or contraindications on QT interval prolongation. The goal of this work was to quantify usage of QT interval prolonging drugs according to the CredibleMeds ® database of the German outpatient drug prescription market and to evaluate discrepancies between German SmPCs/US PI and CredibleMeds ® . Methods Drugs listed on CredibleMeds ® with known, possible or conditional risk for torsade de pointes were evaluated from 2000 to 2020. The German drug prescription report was used as source for defined daily dose‐ (DDD‐) based prescriptions of the German outpatient drug prescription market of the public health insurance system. German SmPCs and US PI of 253 CredibleMeds ® ‐listed drugs were evaluated for contents regarding QT interval prolongation. Results Of the drugs currently listed on CredibleMeds ® , 59.7% (95% confidence interval [CI] 53.5–65.5%) were listed after 2012. Due to newly listed drugs, the proportion of DDDs of CredibleMeds ® drugs among all prescriptions increased from 4.6% in 2013 to 21.1% in 2019. DDD‐based usage of the CredibleMeds ® drugs already listed in 2013 was similar in 2019. Among the drugs with known QT risk according to CredibleMeds ® , 7.5% (95% CI 2.6–19.9%) of German SmPCs and 21.1% (95% CI 11.1–36.3%) of US PI had no mention of QT issues whatsoever. Conclusion A significant proportion of all drugs prescribed in the outpatient sector is associated with QT risks according to CredibleMeds ® . SmPCs and PI should systematically be evaluated for concordance with the widely used CredibleMeds ® database to increase medication safety.
    Type of Medium: Online Resource
    ISSN: 0306-5251 , 1365-2125
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1498142-7
    SSG: 15,3
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  • 5
    In: British Journal of Clinical Pharmacology, Wiley, Vol. 89, No. 8 ( 2023-08), p. 2552-2560
    Abstract: Prescribing information should follow a defined structure to help prescribers easily find required information. Often information appears in different sections of Summaries of Product Characteristics (SmPCs) in an inconsistent way. Still unknown is how this inconsistency affects absolute contraindications and how it can be improved. Thisstudy aimed to evaluate the structure of absolute contraindications in SmPCs based on absolute drug–drug contraindications (DDCI) in the section ‘contraindications’ and references to sections ‘special warnings and precautions for use’ (here as ‘warnings’) and ‘interaction with other medicinal products and other forms of interaction’ (here as ‘interactions’). Methods SmPCs of 693 commonly prescribed drugs were analysed regarding absolute DDCI in ‘contraindications’ sections. References to sections on ‘warnings’ and ‘interactions’ were evaluated to characterize information provided about DDCI. Results Of 693 analysed SmPCs, 138 (19.9%) contained ≥1 absolute DDCI. Of 178 SmPCs that referred to sections on ‘warnings’ or ‘interactions’, 131 (73.6%) did not contain further information on absolute DDCI, whereas 47 (26.4%) did. Such additional information was found in sections on ‘interactions’ and ‘warnings’ in 41 (87.2%) and 9 (19.1%) SmPCs, respectively. Conclusions Information regarding absolute DDCI was found not only in sections on ‘contraindications’ but also in sections on ‘warnings’ and ‘interactions’. Information was not given with consistently straightforward phrasing and structure and so can leave uncertainty for prescribers. To improve drug safety, clear definitions and wording for absolute and relative contraindications should be provided, ideally in tables.
    Type of Medium: Online Resource
    ISSN: 0306-5251 , 1365-2125
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 1498142-7
    SSG: 15,3
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  • 6
    In: Journal of Clinical Medicine, MDPI AG, Vol. 12, No. 1 ( 2022-12-31), p. 315-
    Abstract: Drug-related problems (DRPs), i.e., adverse drug reactions (ADRs) and medication errors (MEs), constitute a serious threat to the patient’s safety. DRPs are often insufficiently captured by clinical routine documentation, and thus, they frequently remain unaddressed. The aim of this study was to assess the coverage and usability of the new 11th revision of the WHO International Classification of Diseases (ICD-11) to document DRPs. We refined the ‘Quality and Safety Algorithm’ from the ICD-11 Reference Guide and used it for DRP reporting to code 100 different anonymized DRPs (50 ADRs and 50 MEs) in a German hospital. The ICD-11 three-part model consisting of harm, cause, and mode was used whenever they were applicable. Of 50 ADRs, 15 (30.0%), such as drug-induced osteoporosis, were fully classifiable and codable by the ICD-11, whereas 35 (70.0%), such as drug-induced hypokalaemia, could not be fully classified due to sanctioning rules preventing the postcoordination (i.e., a combination of specific codes, such as drug and diagnosis). However, coding without the loss of information was possible in the 35 of these 35 (100.0%) ADR cases when we were deviating from the cluster code order of the Reference Guide. In all 50 MEs, the mode could be encoded, but for none of the MEs, postcoordination, i.e., the assignment of the ME to a specific drug, was allowed. In conclusion, the ICD-11 three-part model enables us to acquire more detailed documentation of DRPs than the previous ICD versions did. However, the codability, documentation, and reporting of DRPs could be significantly improved by simple modifications of the current ICD-11 sanctioning rules and by the addition of new ICD-11 codes.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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