Standardised MedDRA Query (SMQ)

SMQs are constructed using a logical hierarchy to ensure comprehensive retrieval of adverse event reports, regardless of how specifically the event was coded.

• Narrow Terms (Category A): Highly specific terms that definitively identify the condition of interest (e.g., 'Myocardial infarction').
• Broad Terms (Category B): Less specific terms that may indicate the condition but require further review (e.g., 'Chest pain').
• Algorithmic Logic: Cases are retrieved using Boolean logic (e.g., a narrow term OR a combination of broad terms).

Unlike simple flat lists, SMQs apply algorithmic categories to balance search sensitivity against retrieval precision.

• Category A (Narrow): Maximizes specificity; a single hit is often sufficient to identify a case.
• Category B (Broad): Maximizes sensitivity; used to catch potential cases coded with vague or non-specific terminology.
• Category C (Additional): Includes terms related to laboratory tests, investigations, or procedures that support the diagnosis but do not represent the condition itself.

SMQs are developed and maintained by the CIOMS Working Group on SMQs and released biannually by the MSSO (Maintenance and Support Services Organization) in sync with the MedDRA dictionary.

• Versioning: SMQs are updated to reflect new medical knowledge and changes in MedDRA terminology.
• Testing: Each SMQ undergoes rigorous testing against legacy safety databases to validate its retrieval performance before release.

The primary purpose of an SMQ is to facilitate case retrieval for signal detection and aggregate reporting, not to replace medical assessment.

• Standardized Case Series: SMQs allow safety scientists to pull a consistent, pre-defined cohort of cases across different products and databases.
• Triage Tool: They serve as a high-sensitivity filter to reduce the manual workload of searching for complex syndromes like anaphylaxis or acute pancreatitis across millions of records.

Beyond standard medical conditions, specialized SMQs exist to address specific pharmacovigilance needs.

• Anaphylactic Reaction (SMQ): A highly validated query combining narrow terms for confirmed anaphylaxis with broad terms for potential allergic manifestations.
• COVID-19 (SMQ): A rapidly developed SMQ to track adverse events in the context of the pandemic.
• Drug Abuse and Dependence (SMQ): Targets terms related to substance misuse, tolerance, and withdrawal syndromes.

While powerful, SMQs have inherent limitations that must be managed by the safety reviewer.

• Coding Variability: The accuracy of retrieval is entirely dependent on the quality and granularity of the original verbatim term coding.
• False Positives: Broad terms (Category B) can retrieve many irrelevant cases, requiring manual triage.
• Not Diagnostic: An SMQ hit does not confirm the diagnosis; it only identifies a case that warrants clinical review.

The systematic process of identifying new or previously unknown potential causal relationships between a drug and an adverse event. SMQs serve as a critical input to this process by:

• Aggregating related terms to increase statistical power
• Reducing noise from irrelevant individual Preferred Terms
• Enabling disproportionality analysis on clinically coherent groupings rather than isolated terms

A quantitative methodology that identifies drug-event combinations reported more frequently than expected. SMQs enhance this analysis by defining the event grouping. Key metrics include:

• Proportional Reporting Ratio (PRR) : A frequentist measure comparing observed to expected reporting rates
• Reporting Odds Ratio (ROR) : The odds of an event being reported with a specific drug versus all others
• Empirical Bayes Geometric Mean (EBGM) : A Bayesian score that applies shrinkage to adjust for sparse data

The foundational unit of pharmacovigilance data. Each ICSR contains detailed information about a single adverse event experienced by a patient in relation to a medicinal product. SMQs are applied against ICSR databases to retrieve cases of interest. The structured exchange of ICSRs follows the ICH E2B (R3) electronic transmission standard, using XML-based messaging for automated processing between stakeholders.

The systematic evaluation of the likelihood that a suspected drug caused an observed adverse event. Once SMQs retrieve a cohort of potential cases, causality assessment applies criteria such as:

• Temporal relationship between drug administration and event onset
• Dechallenge/Rechallenge : Whether the event abated upon withdrawal and recurred upon re-administration
• Confounding by indication : Distinguishing whether the underlying disease, not the drug, caused the event