Читать книгу Cobert's Manual Of Drug Safety And Pharmacovigilance (Third Edition) - William Gregory - Страница 54

The number of unreported AEs is, therefore, quite variable. This renders the numerator in the proportion quite suspect.

Denominator data are also incomplete.

The denominator, patient exposure, is often unknown in spontaneous reporting systems. Although one can obtain prescription data and one can know how many tablets, capsules, or tubes were sold, it is hard to know how many consumers actually took the product in the manner and for the length of time prescribed. Should one count the patient who took one tablet once in the same way as someone who took three tablets a day for a month or a year?

Ideally, denominator data would be reported in a number of ways including patients exposed (for risk estimates), patient-time exposed (number of patients times the length of time each patient took the drug such as patient-months or patient-years) for incidence rates estimates. But instead of accurate denominator figures, the only known data are total number of tablets sold, kilograms manufactured, prescriptions written or dispensed and so on.

Nonetheless, by obtaining these data, crude estimates of the number of ADR reports divided by a quantitative denominator, or reporting rates can be made. However, the numbers are often not terribly informative. One of the authors recalls one widely used antihistamine for which 12 cardiac arrhythmia reports were received for what was calculated to be 9 billion (9,000,000,000) patient-years of exposure. This works out to 12/9,000,000,000 or a reporting rate of 0.0000000013 cardiac arrhythmia events/patient-years of exposure, which is not a meaningful number. In fact, this number is so low that it is below the naturally occurring incidence of cardiac arrhythmias in the population, meaning marked under-reporting of the AEs. Using a different interpretation of these data, one could argue that the drug in question actually prevents cardiac arrhythmias, which is clearly not the case. This number is useless to a clinician who needs to make a decision on whether a particular cardiac patient should be given this drug.

The manufacturing data (kilograms manufactured) are available from the company producing the product, and the prescription information and the patient exposure data are obtained from various private companies that track such things. Confounders include generic products in which the denominator is not included in the calculation but in which the company reporting on the branded drug receives and includes AEs (numerator cases) as well as counterfeit drugs where the denominator and numerator are compromised. Nonetheless, drug utilization data can be broken down by gender, age, and other demographic characteristics. Trends over time in usage can be observed.

In summary, as has been said, “The numerator is bad, the denominator is worse, and the ratio is meaningless.” Hence, one cannot calculate quantitative measures of risk for a particular AE based on spontaneous data, only reporting frequencies. Period.

However, the large repositories of spontaneous data can be somewhat used to monitor unexpected patterns of reporting through innovative quantitative techniques looking at proportional reporting. These include proportional reporting ratios (PRR), gamma poisson shrinker (GPS), urn-model algorithm, reporting odds ratio (ROR), Bayesian confidence propagation neural network–information component (BCPNN-IC), and adjusted residual score (ARS), sequential probability ratio test (max-SPRT). The PRR is used as an example of the application of quantitative disproportionality method, and is described below.¹³