Data mining for detecting signals of adverse drug reaction of doxycycline using the Korea adverse event reporting system database

Abstract

Background

Doxycycline is one of the most prescribed antibiotics by dermatologists. However, the concern regarding adverse events of doxycyline has been rising.

Objective

To detect the adverse events of doxycycline using the Korea Adverse Events Reporting System (KAERS) database from January 2014 to December 2018 through a data mining method.

Methods

A signal was defined as one satisfying all three indices; a proportional reporting ratio, a reporting odds ratio, and an information component. We further checked whether the detected signals exist in drug labels in Korea and five developed countries, the United States, the United Kingdom, Germany, Canada, and Japan.

Results

A total of 3,365,186 adverse event-drug pairs were reported and of which 3,075 were associated with doxycycline. Among the thirty-seven signals, nineteen (malaise, ileus, confusion, malignant neoplasm, ectopic pregnancy, ovarian hyperstimulation, vaginal hemorrhage, bone necrosis, acne, rosacea, seborrheic dermatitis, folliculitis, skin ulceration, crusting, dry skin, paronychia, mottled skin, application site reaction, and application site edema) were not included on any of the drug labels of the six countries.

Conclusion

We identified nineteen new doxycycline signals that did not appear on drug labels in six countries. Further studies are warranted to evaluate the causality of the adverse events with doxycycline.

Keywords:

• Adverse drug reaction
• Doxycycline
• KAERS database
• signal detection

Acknowledgments

We sincerely thank the Korea Institute of Drug Safety and Risk Management for their cooperation in providing access to the Korea Adverse Event Reporting System Database.

Ethics approval

As this study used de-identified records, Institutional Review Board (IRB) review was not required.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Dong-Wha Research Grant 2020.