Emerging Safety Science Workshop Summary (2008) / Chapter Skim
Currently Skimming:
8 Pharmacovigilance
8 Pharmacovigilance
Pages 74-92
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 74... ...
pharmacovigilance program called online signal management. This program combines a number of technologies into one tool that can help safety evaluators review information on marketed drugs more efficiently and in much greater detail than previously was possible.
|
From page 75... ...
One would then compare that result with the number of strokes reported for all drugs as a proportion of all the adverse events reported for all all other drug X drugs event of interest A C all other events B D A C if A+B C+D FIGURE 8-1 Disproportionality analysis calculation. SOURCE: Almenoff, 2007.
|
From page 76... ...
When safety evaluators log on to the system, they are provided with a primary review that includes • a listing of all serious adverse event reports for the drug they are responsible for monitoring in a particular time interval; • all events with a rising trend; and • all nonserious unlisted reports that have EBGM values above a defined threshold. OSM combines this filtering capability with a number of other tools that enable safety evaluators to follow up on a signal to determine whether it represents a problem.
|
From page 77... ...
Food and Drug Administration's (FDA's) primary method of collecting postmarket data and monitoring for adverse events is passive surveillance.
|
From page 78... ...
Since there are 3 million reports in the database and each entry typically includes several drugs, some 10 million drug names in the database had to be reviewed, one by one, and put in a standardized form. This was primarily a manual process, with little computer assistance, and took years to complete, but eventually Lincoln Technologies was able to reduce the 300,000 different names to about 3,000 ingredients in a standardized generic form.
|
From page 79... ...
Part of the answer is that recent computer and database advances have made it easier to perform this sort of analysis, but another part of the answer is that biostatisticians are sometimes hesitant to conduct formal statistical analyses on data collected outside of a controlled clinical trial environment. Only recently did scientists begin applying statistical models to spontaneously reported data.
|
From page 80... ...
The analysis is particularly useful because it provides a single number that can be graphed or plugged into other models. As an example of the usefulness of having a single number, DuMouchel showed a heat map of adverse events for a single drug (see Figure 8-2)
|
From page 81... ...
The biggest rectangles are the system–organ classes -- blood, cardiovascular, respiratory, renal, gastrointestinal, and so forth -- but all of the 10,000 or so MedDRA preferred terms are grouped into very small squares where the grouping respects the hierarchy of MedDRA. One can explore this heat map by moving the computer cursor over these squares; as this happens, information appears concerning where that square falls in the MedDRA grouping.
|
From page 82... ...
The process of looking for adverse events due to drug interactions is straightforward. A pair of drugs is treated as an additional "pseudodrug." If, for example, there is a report of a patient's taking three drugs and the three drugs are listed in the report, the analysis treats the case as though the patient were taking three drugs -- A, B, and C -- as well as three pseudo-drugs -- A + B, A + C, and B + C
|
From page 83... ...
With this analysis, the background noise rate can be estimated automatically and can be extended to estimate drug interactions. This is a time-consuming process as it is necessary to perform a multiple regression analysis for every adverse event; thus if 10,000 MedDRA terms are being considered, 10,000 regressions must be calculated.
|
From page 84... ...
Once risk has been assessed, case reports can be examined and medical judgments made. Active Surveillance for Anticipated Adverse Events Historically, postmarket monitoring for adverse events has been accomplished through passive surveillance.
|
From page 85... ...
Using Claims Databases for Surveillance A large percentage of Americans' medical records and history of prescription drug use can be accessed by using health care claims, making this an ideal platform for launching a national active surveillance system. The backbone database of such a system would comprise routinely collected administrative health care claims enhanced by supplemental information, such as links to full-text medical records in either electronic or paper form, laboratory results, and pharmacy records.
|
From page 86... ...
The Vaccine Safety Datalink (VSD) project, a CDC-supported program that operates in eight health plans of the HMO Research Network, quickly became involved and analyzed the risk using its database of 7 million health plan members.
|
From page 87... ...
While these are the systems used most often in the United States for surveillance purposes, however, they are insufficient for ensuring timely identification of new adverse events or timely follow-up on safety signals. Platt asserted that in addition, linked databases from Medicare Parts A, B, and D, Medicaid in most large states, and private health plans need to be accessible and included in a national surveillance network.
|
From page 88... ...
Data Ownership and Decision Making Robert Califf, of Duke University, urged caution in response to Platt's description of an integrated active surveillance network in which data would belong to individual health plans, companies, regulators, etc., and groups could opt in and out of specific uses of the data. He warned against every stakeholder having its own data sets, completing its own analyses, and making its own decisions about what drugs are dangerous or safe.
|
From page 89... ...
If the correct excess risk threshold is chosen, the test can be highly effective and verify a risk very quickly, but if the wrong risk threshold is chosen, it may mask real risks. Martin Kullforff, a statistician working with the VSD project, developed a variant of the SPRT called the maximized SPRT, which tests the null hypothesis (no excess risk)
|
From page 90... ...
Researchers will also need to develop rapid and effective ways of determining which signals represent real problems that require public health or regulatory action. The maximized SPRT currently used by the VSD project has many desirable properties, but other sequential analysis methods should also be tested to determine which works best.
|
From page 91... ...
Platt described the detection of a signal involving excess gastrointestinal bleeding associated with a new vaccine. After substantial time and effort, the signal was proven to be spurious, resulting from a change over time in the way the health plans' clinicians used certain diagnostic codes (more common use of codes that suggested gastrointestinal bleeding)
|
From page 92... ...
Almenoff suggested that an ideal way to approach this issue would be to include in electronic medical records a box that could be checked to indicate that the health care provider believed the occurrence was an adverse event, thereby flagging the event. Responding to this suggestion, Platt said his group is experimenting with "elicited surveillance," an electronic medical record system including a field designed to prompt clinicians to indicate when an event has occurred (diagnosis or laboratory result)
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.