Comparison of algorithms that detect drug side effects using electronic healthcare databases

TL;DR

This study compares four algorithms for detecting adverse drug reactions in the THIN electronic healthcare database, highlighting their strengths and limitations, especially in identifying rare side effects.

Contribution

First comparison of MUTARA, HUNT, and other algorithms on the THIN database, establishing a benchmark for ADR detection performance.

Findings

No single algorithm was universally superior.

Algorithms vary in sensitivity and specificity.

All algorithms struggle with rare ADR detection.

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.