Abstract
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare database containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families. This is the first comparison of ADR signalling algorithms that includes MUTARA and HUNT and enabled us to set a benchmark for the adverse drug reaction signalling ability of the THIN database. The drugs were selectively chosen to enable a comparison with previous work and for variety. It was found that no algorithm was generally superior and the algorithms’ natural thresholds act at variable stringencies. Furthermore, none of the algorithms perform well at detecting rare ADRs.
Highlights
It is an unavoidable consequence that prescription drugs frequently cause unwanted side effects due to the unpredictability of how a drug will interact with all the body functions [23]
MUTARA does not have a lower confidence interval calculation, so the natural threshold implemented is that the unexpected-leverage is greater than 0 and for Highlighting UTARs Negating TARs (HUNT) the top 10% of medical events were signalled
The algorithms all performed well on the calcium channel blockers, with average precision (AP) scores ranging from 0.0236 − 0.1988, but the ROR05 performed worse for all the calcium channel blockers investigated
Summary
It is an unavoidable consequence that prescription drugs frequently cause unwanted side effects due to the unpredictability of how a drug will interact with all the body functions [23]. When a negative side effect has been associated with a drug, it is referred to as an adverse drug reaction (ADR). Outside of a clinical trial it is common practice for a patient to be taking multiple prescription drugs but not all co-prescriptions can be monitored during clinical trials. It is common for certain subpopulations such as children or pregnant women to be underrepresented in trials due to ethical reasons. Post marketing surveillance is constantly required to identify any previously undiscovered ADR throughout the time a drug is actively prescribed. Once a tentative signal is generated, it is further evaluated to confirm causation between the drug and adverse drug reaction, if causation is shown, we refer to the signal as a true signal, if causation is not shown, it is a false signal
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