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Adverse reactions in ten years' general practice, computer analysed.

R A Johnson

    Journal of the Royal Society of Medicine
    |March 1, 1986
    PubMed
    Summary

    Computer analysis of 167,264 symptoms identified 1535 adverse drug reactions. Antibiotics and an oral contraceptive were prominent, highlighting the need for computational pharmacovigilance to detect unpredictable side effects.

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    Area of Science:

    • Pharmacovigilance
    • Drug Safety Monitoring
    • Computational Medicine

    Background:

    • General practices record numerous patient symptoms over time.
    • Identifying adverse drug reactions (ADRs) from recorded symptoms is crucial for patient safety.
    • Previous methods may not efficiently detect all ADRs.

    Purpose of the Study:

    • To analyze recorded symptoms to identify adverse drug reactions.
    • To quantify the frequency of ADRs associated with specific drug classes and individual medications.
    • To explore the utility of computer analysis in detecting unpredictable drug side effects.

    Main Methods:

    • Retrospective analysis of 167,264 recorded symptoms over a ten-year period.
    • Utilizing computer analysis to identify patterns indicative of adverse drug reactions.
    • Calculating the rate of specific adverse reactions per prescription for certain drugs.

    Main Results:

    • 1535 adverse drug reactions were identified through computer analysis.
    • Antibiotics accounted for 13% of all reported reactions, primarily gastrointestinal symptoms.
    • Ampicillin caused rashes at a rate of 5.2 per 1000 prescriptions.
    • An oral contraceptive (Minovlar) showed the highest frequency of reactions to a single drug (25.9% of its prescriptions).

    Conclusions:

    • Computer analysis is effective in identifying adverse drug reactions from general practice symptom data.
    • Specific drug classes like antibiotics and certain oral contraceptives are associated with notable adverse events.
    • Computational pharmacovigilance offers a promising approach for detecting unpredictable drug side effects.

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