Development of a predictive Antibiotic Burden Index for Primary Immunodeficiency – an explorative study

Detta är en Master-uppsats från Uppsala universitet/Institutionen för farmaceutisk biovetenskap

Författare: Milan Al-naqshbande; [2020]

Nyckelord: Immunodeficiency; Immunbrist;

Sammanfattning: Introduction: Primary immunodeficiency is a medical condition characterized by frequent infections as a result of the immune system not functioning properly. Patients are usually treated with antibiotics and immunotherapy. Since there is no mutual database for patient records between primary care and healthcare, a communication gap arises. The idea behind the project is to investigate if there is a possibility to build a warning system that can act as an indicator for healthcare if antibiotics are being prescribed too often for these patients by primary care. The aim of this study was to examine whether it is possible to develop a predictive Antibiotic Burden Index (ABI) based on data generated from primary care. Methods: In the study two models were designed and tested to see if they can describe how patients’ antibiotic use is related to levels of IgG. The correlations were evaluated to see if they could be used to design a warning system that would fire a signal if the patient is using a lot of antibiotic because that would be an indication of their treatment not being effective. Each individual antibiotic was given a value and from that combined with the patients’ actual prescription fulfillment an ABI could be calculated. Two models for calculating ABI were evaluated, the first model is based on number of prescriptions collected from the pharmacy. The second one based on number of prescriptions plus an antibiotic score calculated using an antibiotic ranking system provided by Huddinge Hospital. These calculations were made using historical patient data from the last two decades. The data was extracted from the National Quality Registry for primary immunodeficiency. Results:The results show that both models were applicable. The two models differed slightly in the percentage values, but both follow the same pattern. In non-Stockholm regions, the antibiotic use was higher during the six months up to the lowest recorded IgG and lower during the same time up to the highest recorded IgG. When evaluated in Stockholm only, it was strangely the opposite. Discussion: One reason among others for the deviating results of Stockholm’s region could be a change in treatment recommendation within the region . Since this is an explorative study and the results seemed promising enough, it is recommended that the project is taken into the next step. That would be a more profound study including more variables. Conclusion: There seems to be a correlation between the use of antibiotics and IgG-levels.  

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