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. 2015 Dec 16;6(4):728-47.
doi: 10.4338/ACI-2015-06-RA-0076. eCollection 2015.

Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

P Yildirim  1
Affiliations

Association Patterns in Open Data to Explore Ciprofloxacin Adverse Events

P Yildirim. Appl Clin Inform. .

Abstract

Background: Ciprofloxacin is one of the main drugs to treat bacterial infections. Bacterial infections can lead to high morbidity, mortality, and costs of treatment in the world. In this study, an analysis was conducted using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database on the adverse events of ciprofloxacin.

Objectives: The aim of this study was to explore unknown associations among the adverse events of ciprofloxacin, patient demographics and adverse event outcomes.

Methods: A search of FDA AERS reports was performed and some statistics was highlighted. The most frequent adverse events and event outcomes of ciprofloxacin were listed, age and gender specific distribution of adverse events are reported, then the apriori algorithm was applied to the dataset to obtain some association rules and objective measures were used to select interesting ones. Furthermore, the results were compared against classical data mining algorithms and discussed.

Results: The search resulted in 6 531 reports. The reports included within the dataset consist of 3 585 (55.8%) female and 2 884 (44.1%) male patients. The mean age of patients is 54.59 years. Preschool child, middle aged and aged groups have most adverse events reports in all groups. Pyrexia has the highest frequency with ciprofloxacin, followed by pain, diarrhoea, and anxiety in this order and the most frequent adverse event outcome is hospitalization. Age and gender based differences in the events in patients were found. In addition, some of the interesting associations obtained from the Apriori algorithm include not only psychiatric disorders but specifically their manifestation in specific gender groups.

Conclusions: The FDA AERS offers an important data resource to identify new or unknown adverse events of drugs in the biomedical domain. The results that were obtained in this study can provide valuable information for medical researchers and decision makers at the pharmaceutical research field.

Keywords: Data processing; adverse drug event; clinical care; clinical decision support; clinical informatics.

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Conflict of interest statement

Conflict of Interest

The author has no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Age-specific distribution of adverse event reports
Fig. 2
Fig. 2
The distribution of most frequent ten adverse events
Fig. 3
Fig. 3
Gender specific distribution of all adverse events
Fig. 4
Fig. 4
Interestingness measures in the association rule mining process
See this image and copyright information in PMC

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