An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance.

ABSTRACT

PURPOSE: The European Medicines Agency developed an algorithm to detect unexpected increases in frequency of reports, to enhance the ability to detect adverse events that manifest as increases in frequency, in particular quality defects, medication errors, and cases of abuse or misuse. METHODS: An algorithm based on a negative binomial time-series regression model run on 6 sequential observations prior to the monitored period was developed to forecast monthly counts of reports. A heuristic model to capture increases in counts when the previous 4 observations were null supplemented the regression. Count data were determined at drug-event combination. Sensitivity analyses were run to determine the effect of different methods of pooling or stratifying count data. Positive retrospective detections and positive predictive values (PPVs) were determined. RESULTS: The algorithm detected 8 of the 13 historical concerns, including all concerns of quality defects. The highest PPV (1.29%) resulted from increasing the lower count threshold from 3 to 5 and including literature reports in the counts. Both the regression model and the heuristic model components to the algorithm contributed to the detection of concerns. Sensitivity analysis indicates that stratification by commercial product reduces the PPV but suggests that pooling counts of related events may improve it. CONCLUSION: The results are encouraging and suggest that the algorithm could be useful for the detection of concerns that manifest as changes in frequency of reporting; however, further testing, including in prospective use, is warranted.