Key result
Automated backward stepwise removal identifies false-positive pharmacovigilance signals with up to ~78% positive predictive value.
Why the study?
False-positive signals of disproportionate reporting induced by co-prescriptions in spontaneous reporting databases complicate pharmacovigilance signal detection and require improved automated methods to reduce them.
An automated backward stepwise removal method may help eliminate false-positive signals caused by co-prescription bias in spontaneous reporting databases.
Automated co-prescription adjustment may reduce false-positive SDRs; leaves open its value for routine pharmacovigilance signal detection.
PURPOSE: To test an automated method to decrease the number of false-positive (FP) signals of disproportionate reportings (SDRs) generated by co-prescription. METHODS: Automated backward stepwise removal of reports concerning the drug associated with the highest ranked SDR for an event was tested for gastric and oesophageal haemorrhages (GOH), central nervous system haemorrhages and cerebrovascular accidents (CNSH), ischaemic coronary artery disorders and muscle pains (MP) using the reporting odds ratio in the French spontaneous reporting research database. After ranking SDRs detected in the complete dataset on the lower limit of the reporting odds ratio 95% confidence interval, reports concerning the drug with the highest ranked SDR were removed. In the dataset thus generated, SDRs were again identified, ranked and reports related to the drug involved in the newly highest ranked SDR removed. The process was repeated until no signal was detected. Initially detected SDRs eliminated using this technique were assessed regarding the summary of products characteristics and the literature to determine their FP nature. RESULTS: Seventeen SDRs were successively eliminated for GOH, 37 for CNSH, 15 for ischaemic coronary artery disorders, and 36 for MP. Four were FP for GOH, 29 for CNSH, 7 for ACI and none were FP for MP. The positive predictive value of the backward stepwise removal procedure in identifying FP SDRs ranged from 0% (MP) to 78.4% (CNSH). CONCLUSIONS: Although further adjustment is needed to improve the method presented herein, our results suggest that numerous FP signals because of co-prescription bias could be eliminated using an automated method.
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Avillach et al. (2013) studied Adverse drug reactions. Automated backward stepwise removal method was evaluated on Positive predictive value in identifying false-positive signals. An automated backward stepwise removal method identified false-positive pharmacovigilance signals due to co-prescription with a positive predictive value ranging from 0% to 78.4%.
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