gms | German Medical Science

14. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie

Gesellschaft für Arzneimittelforschung und Arzneimittelepidemiologie

15.11. - 16.11.2007, Frankfurt am Main

Prevention of hospitalisation by identification of subjects at risk using a medication-based algorithm: a feasibility study

Meeting Abstract

Suche in Medline nach

  • corresponding author S. Harder - Institute for Clinical Pharmacology, University Hospital Frankfurt, Frankfurt am Main
  • M. Kurepkat - MedicalContact AG, Essen
  • J. Fleckenstein - MedicalContact AG, Essen

Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie e.V. (GAA). 14. Jahrestagung der Gesellschaft für Arzneimittelanwendungsforschung und Arzneimittelepidemiologie. Frankfurt am Main, 15.-16.11.2007. Düsseldorf: German Medical Science GMS Publishing House; 2007. Doc07gaa25

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Veröffentlicht: 12. November 2007

© 2007 Harder et al.
Dieser Artikel ist ein Open Access-Artikel und steht unter den Creative Commons Lizenzbedingungen ( Er darf vervielfältigt, verbreitet und öffentlich zugänglich gemacht werden, vorausgesetzt dass Autor und Quelle genannt werden.



Context: Inappropriate prescriptions and medication misadventures are risk factors for hospitalisation of chronically ill, polymedicated patients. However, whilst a large number of inappropriate medications and potentially harmful drug combinations are known, only a few of these contribute significantly to the hospitalisation rate. It is assumed that a population based approach aiming for individualised prevention of hospital admission by case management need to focus on these combinations to be cost-effective.

Aim of the study: An algorithm was developed which should identify patients at risk for medication induced hospitalisations. The entire project will identify patients (age >55y) with a variety of health risks e.g. for malnutrition or cardiovascular risk, these then should be scheduled for a “tailored” case management (Casaplus ®). Patients qualifying for this program will be pre-selected by their overall risk profile for hospitalisation within the next 12 months. Thereby the individual risk score will be calculated by multivariate predictors for an early hospitalisation comprising variables like sex, age, previous hospitalisations, kind of diagnosis, number of prescriptions, treatment expenses in the previous year.

Material and Method: The algorithm consists of combinations of certain ATC codes and their prescription history (e.g. repeated prescription over the last 6 months). A set of rules applied (see results table), these rules based either on existing evidence of increased hospitalisation rates or unanimous opinion regarding harmfulness. This algorithm was tested with prescription data (BKK) from a cohort of 14027 patients fulfilling the entry criteria for the Casaplus program (see above). Prescriptions covering a period of 12 months were analyzed according these rules.

Results: Overall, 2020 patients (14.4% of all pts.) fell at least in one of the groups shown in table 1 [Tab. 1].

Conclusions: It is concluded that a considerable proportion of patients with a high risk profile for hospitalisation had additional risk-prone pharmacotherapy. The largest cluster of potentially harmful combinations involves NSAR and drugs which might interfere with potassium levels. It needs to be shown whether (and which type of) an intervention (e.g. case management, academic detailing of GPs) will be appropriate to prevent drug induced hospitalisations.