EJERCICIOS PROPUESTOS PARA NIVEL
MÁS CUESTIONES BIOÉTICAS
After a signal is prioritised, other sources of data should be systematically assessed to determine whether sufficient evidence of “causality” exists, and what further action, if any, may be required. The sources of evidence can include [112]:
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Other ICSRs with similar event terms identified (e.g. by using Standardized MedDRA Queries (SMQs)).
Scientific literature and/or systematic reviews
Clinical trial and pre-clinical data (i.e., SmPCs and IBs)
Epidemiological data.
The use of SMQs is recommended in order to retrieve and review similar cases of interest when potential signals are identified within a database. In practice many signals can be accessed on the strength of the ICSRs that triggered the signal in the first place. Depending on the case load (number/volume of cases), the data may be stratified according to age, gender, ethnicity, concomitant medication or disease. This may identify populations at highest risk for the event and also reduces confounding. A judgment about whether a signal is validated depends on the number and quality of case reports, the nature of the reaction, type of drug and the population exposure.
The evaluation stage of a signal is often a resource intensive and time consuming process. For example, in one study [189] investigating the use of the high- strength pancreatin supplement Nutrizyme for patients with cystic fibrosis, there were reported causes of sub-acute intestinal obstruction due to a fibrotic stricture of the ascending colon in a child with cystic fibrosis. Though, more recent similar cases suggest that this new pathology is linked to the use of enteric-coated high strength pancreatin microspheres, which resulted in a drug safety update in 1998 from the UK’s committee on safety of medicines advising on the dosage of the treatment.
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Once a signal has been evaluated there are three possible options following the decision making stage:
Close signal: The signal was refuted based on the available evidence and no further action is required. The decision and rationale for closing a signal should be documented. However, if further evidence becomes available the signal can be re-assessed.
Continue monitoring: In some circumstances a decision cannot be made until the evidence supporting the signal is strengthened. Except for situations of extreme risk, these signals are monitored until sufficient evidence becomes available to either confirm or refute the signal. The decision and rationale to justify monitoring a signal should be documented.
Take further action: After a signal is validated further action is required. The decision and rationale to take further action for a signal should be documented. The actions may include the following; notify the Qualified Person for PV (QPPV), enhance monitoring or follow-up techniques, consult internal or external experts, targeted clinical investigations, comparative observational studies, active surveillance schemes and clinical trials.
6.6 Discussion
The development, testing and deployment of SDAs represent a quantum jump in PV. Although there is currently no scientific or regulatory basis to claim that
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SDAs are a required element of good PV practice, they are an intuitively appealing solution to the operational challenges of screening steadily enlarging safety databases [109]. Higher-order phenomena, such as complex drug-drug interactions or drug-induced syndromes, may be especially difficult to identify through manual review of AE line listings, and it is this type of phenomena which might be most amenable to detection through the use of SDAs.
Retrospective applications indicate that SDAs can highlight some medically significant associations in a timely manner, often in advance of the published literature and traditional methods. As a result SDAs have been incorporated into routine signal management frameworks for most major national and transnational drug safety monitoring centers, including the MHRA (PRR), the WHO (BCPNN) and the FDA (GPS) [52]. However, SDAs and DPA methods may fail to highlight legitimate associations for various reasons; they often have an unclear opportunity cost associated with false alarms (false discoveries); and have yet to prospectively detect new drug hazards.
There are formidable challenges to validating SDAs beyond those already mentioned, such as the choice of appropriate reference AEs (true positive and false negative signals) for assessing SDA performances in the absence of perfect gold standards for adjudicating causality [174]. However findings of a disproportionality ratio for a drug should lead to a new reinvestigation of data from experimental pharmacology and RCTs. It should also stimulate specific case-control or cohort analysis to strengthen the generated hypothesis.
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Accordingly, signal detection should be considered as one of many potentially performance-enhanced options in the toolkit for detecting safety signals that need to be assessed by each institution on an individual basis. They should only be considered potential supplements to, and not substitutes for, a comprehensive signal detection programme based on multiple approaches and data sets. In this chapter we have clearly underlined some of performance related issues with the SDAs when analyzing harms data and suggestions for improvements have been made. In chapter 7 we will explore the use of SDAs further to investigate their ability to detect signals in clinical trial databases of a smaller scale.
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