Traditional Methods
Spontaneous Reporting Systems (SRSs) remain the backbone of pharmacovigilance. These systems rely on healthcare professionals and patients to report adverse events. Traditional methods include:
- Individual Case Review:
- Experts analyze individual adverse event reports to identify potential drug-event relationships. This manual process is particularly valuable for rare or serious events that require clinical judgment.
- Aggregate Analyses:
- Simple case counts or exposure-adjusted reporting rates are calculated to detect patterns. For instance, an unusually high number of reports for a specific event can trigger further investigation.
While these methods have been successful, they are limited by issues like underreporting, inconsistent data quality, and a lack of standardized reporting across regions.
Emergence of Data Mining
In the late 1990s, statistical data mining emerged as a powerful complement to traditional methods. These techniques process large datasets to identify unexpected patterns. Key methods include:
- Disproportionality Analysis: Tools like Proportional Reporting Ratios (PRR) highlight drug-event combinations that occur more frequently than expected.
- Bayesian Algorithms: Used in systems like WHO’s Bayesian Confidence Propagation Neural Network (BCPNN), these algorithms provide probabilistic insights into potential ADRs.
- Machine Learning: Modern AI models analyze complex datasets, identifying subtle patterns that might be missed by traditional methods.
An Integrated Framework
Combining traditional and modern approaches creates a more robust pharmacovigilance framework. For example:
- Preliminary Screening: Statistical tools identify potential signals quickly.
- Expert Validation: Clinicians review flagged signals to assess their relevance and plausibility.
- Follow-Up: High-priority signals are investigated using both qualitative and quantitative methods.
This synergy ensures that signals are detected earlier and evaluated more thoroughly, ultimately enhancing public health safety.