The causality assessment of adverse events, to determine the relationship or connection between the drug and adverse events, is an essential and complex approach in pharmacovigilance. The recognition of a potential safety issue for a drug requires adverse drug reactions to be readily differentiated from adverse events.
An adverse drug reaction is distinguished from an adverse event by the fact that in an adverse drug reaction, a causal relationship is suspected between a drug and an adverse event. Hence, all cases assessed by either the reporting healthcare professional or the sponsor as having a reasonable suspected causal relationship to the drug qualify as adverse drug reactions.
For the purposes of regulatory reporting, if an adverse event is reported spontaneously, even if the relationship is unknown, it meets the criteria of an adverse drug reaction. Hence, all spontaneous reports reported by healthcare professionals or consumers are considered suspected adverse drug reactions since they denote the suspicion of the primary sources, unless the reporters specifically mentions that a causal relationship can be excluded, or they consider the events to be unrelated.
Need for Causality Assessment
A fundamental issue in pharmacovigilance is that many cases concern suspected adverse drug reactions. In real-life situations, a very limited number of adverse reactions qualify as ‘certain’ or ‘unlikely’; most are usually in between, i.e., either ‘possible’ or ‘probable’. To address this issue, many methods have been developed to harmonize causality assessment. However, causality assessment has become a common routine activity in pharmacovigilance.
The advantages of causality assessment include the following:
- Provides uniformity and reduce disagreement between reviewers
- Provides likelihood of relationship
- Mark individual cases
- Improves case evaluation and benefit-risk assessment
How We Assess Causality
1. The Core Challenge
The fundamental question is: “Did this drug cause this adverse event?” However, answering this is difficult because:
- ADRs Mimic Natural Diseases: Adverse drug reactions can look identical to any other human illness.
- Incomplete Data: Reports from doctors or patients are often missing crucial details about timing, other medications, or the patient’s medical history.
- Underlying Suspicion: The report itself is usually based on a clinician’s suspicion, which can introduce bias from the start.
- Multiple Causes: An event can have several potential causes (other drugs, underlying disease, lifestyle), making it hard to pinpoint one.
2. Key Principles for Assessment
Most causality assessment methods are based on a few core principles, originally drawn from Koch’s postulates and later work. These are the critical data elements investigators look for:
- Timing: Did the event occur after the drug was taken in a logical time frame?
- De-challenge: Did the event improve or resolve when the drug was stopped?
- Re-challenge: Did the event reappear when the drug was taken again? (This is considered very strong evidence but is often unethical for serious reactions).
- Alternative Causes: Could other factors (other drugs, illnesses) explain the event?
3. The Three Main Methodologies
There are three primary approaches to applying these principles:
A. Expert Judgment / Global Introspection
- What it is: An expert (or a group of experts) reviews the case using their own knowledge and experience.
- Pros: Can incorporate deep, specialized knowledge.
- Cons: Highly subjective. Different experts may reach different conclusions based on their own biases and knowledge base. The results are often given as “Definite,” “Probable,” “Possible,” etc.
B. Algorithms
- What it is: A structured series of yes/no questions that guide the user to a causality conclusion. The most famous is the Naranjo Algorithm.
- How it works: Points are assigned based on the answers (e.g., +2 for a positive re-challenge, -1 if another cause is likely). The total score places the case in a probability category (e.g., Definite, Probable, Possible, Doubtful).
- Pros: More consistent and transparent than pure expert judgment.
- Cons: Can be rigid. Different algorithms use different questions and weightings, leading to inconsistent results between methods.
C. Bayesian (Probabilistic) Methods
- What it is: A complex mathematical approach that calculates the probability of drug causation by comparing two scenarios:
- The probability that the event occurred given the patient took the drug.
- The probability that the event occurred without the patient taking the drug.
- Pros: Very rigorous and explicit. It forces all assumptions to be quantified and shows how each piece of evidence affects the final conclusion.
- Cons: Data-intensive and requires statistical expertise. It is often impractical for routine assessment of large numbers of reports due to a lack of necessary data.
4. Comparison and Choosing a Method
- No “Gold Standard”: There is no single, universally accepted best method. Studies show that different methods often disagree on the same case.
- Most Common Factors: The most frequently used criteria across all methods are Time to Onset, Re-challenge, and De-challenge.
- How to Choose: The choice depends on the context:
- Purpose: Is it for a quick signal or a critical decision like stopping a clinical trial?
- Accuracy Needed: High-stakes decisions require more robust methods (like Bayesian).
- Volume: Regulators and companies need faster methods to handle thousands of reports.
- Who is Assessing: The training of the reviewer influences the outcome.
5. Applicability and Conclusions
- Who Uses These Methods? Drug manufacturers, regulators (like the FDA), and researchers use them throughout a drug’s life cycle.
- Value Debate: Some experts believe formal epidemiological studies are the only reliable way to assess causality and that assessing individual case reports is not valuable. Others argue that assessing single cases is crucial for identifying new safety signals early, especially when they are serious and rare.
- Key Takeaway: While imperfect, structured causality assessment provides a more consistent and transparent way to evaluate the link between a drug and an adverse event than unstructured opinion alone. It is a vital tool for protecting public health, even as the search for better methods continues.
