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Aims 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.

Zusammenfassung Hintergrund Mit der zunehmenden Anzahl eingenommener Arzneimittel steigt die Prävalenz von Medikationsrisiken. Hierzu zählen beispielsweise Arzneimittelwechselwirkungen, welche erwünschte und unerwünschte Wirkungen einzelner Arzneistoffe reduzieren aber auch verstärken können. Fragestellung Das Verbundvorhaben POLAR (POLypharmazie, Arzneimittelwechselwirkungen und Risiken) hat das Ziel, mit Methoden und Prozessen der Medizininformatikinitiative (MII) auf Basis von „Real World Data“ (stationärer Behandlungsdaten von Universitätskliniken) einen Beitrag zur Detektion von Medikationsrisiken bei Patient:innen mit Polymedikation zu leisten. Im Artikel werden die konkreten klinischen Probleme dargestellt und am konkreten Auswertebeispiel illustriert. Material und Methoden Konkrete pharmakologische Fragestellungen werden algorithmisch abgebildet und an 13 Datenintegrationszentren in verteilten Analysen ausgewertet. Eine wesentliche Voraussetzung für die Anwendung dieser Algorithmen ist die Kerndatensatzstruktur der MII, die auf internationale IT-, Interoperabilitäts- und Terminologiestandards setzt. Ergebnisse In POLAR konnte erstmals gezeigt werden, dass stationäre Behandlungsdaten standortübergreifend auf der Basis abgestimmter, interoperabler Datenaustauschformate datenschutzkonform für Forschungsfragen zu arzneimittelbezogenen Problemen nutzbar gemacht werden können. Schlussfolgerungen Als Zwischenstand in POLAR wird ein erstes vorläufiges Ergebnis einer Analyse gezeigt. Darüber hinaus werden allgemeinere technische, rechtliche, kommunikative Chancen und Herausforderungen dargestellt, wobei der Fokus auf dem Fall der Verwendung stationärer Behandlungsdaten als „Real World Data“ für die Forschung liegt.

Aims 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.

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 CredibleMedsDrugs listed on CredibleMedsOf the drugs currently listed on CredibleMedsA significant proportion of all drugs prescribed in the outpatient sector is associated with QT risks according to CredibleMeds