If you work or have worked within the pharmaceutical industry, then you are likely familiar with MedDRA, the Medical Dictionary for Regulatory Activities. This dictionary makes it possible for drug and device companies to perform analyses of adverse events or medical history. First, MedDRA provides a way to consistently map verbatim terms described by a patient and subsequently reported by an investigator. For example, at a clinic visit, the physician may ask the patient if he or she experienced any ill effects since the patient’s exam. The patient may reply with any or all of the following: headache, head throbbing, pounding in head, head pressure, head pain, pain in head.
To perform an analysis on these verbatim terms would be difficult, since information provided to describe what most would call a “headache” is described in many different ways. Prior to analysis, these verbatim terms are coded so that similar terms are described in a consistent fashion. A company may maintain a list of synonyms that are applied to each new study to auto-encode verbatim terms as a first step. Terms that aren’t initially coded (such as those terms that may include misspellings) may have appropriate MedDRA terms applied by a clinician or other medical reviewer. These MedDRA-coded terms are referred to as Preferred Terms (PTs).
The second benefit of the MedDRA dictionary is that the terms are grouped within a medically relevant hierarchy. PTs are grouped within Higher Level Terms (HLTs), which are grouped within Higher Level Group Terms (HLGTs), which are grouped within System Organ Classes (SOCs), which essentially describe the major systems of the body. Lower Level Terms (LLTs) represent the greatest granularity and are generally thought of as synonyms contributing to the PTs. Most analyses within the pharmaceutical industry present the frequency and percentage of PTs summarized within SOCs, though the frequency and percentage of patients experiencing events within each SOC are also often summarized.
One facet of the MedDRA dictionary that is less frequently utilized is the Standardised MedDRA Queries (SMQs). SMQs are groupings of PTs and LLTs that describe a medical condition, syndrome or disease. For example, if you wanted to identify subjects with Hepatic Disorders, there are numerous PTs and LLTs that could identify an individual as having hepatic disease. Identifying these patients can help determine whether they are experiencing drug-induced liver injury (DILI) or if they had underlying liver disease when they entered the clinical trial. SMQs can exist individually, or they can describe disease areas complex enough to have their own hierarchies (Figure 1).
JMP Clinical 5.0 has new features to summarize MedDRA SMQs directly from the user’s set of MedDRA dictionary files. Available analyses allow the team to easily identify and present the distribution of SMQs occurring on trial (Figure 2), summarize the LLTs and PTs contributing to observed SMQs (Figure 3), as well as compare the incidence of SMQs between treatment arms (Figure 4). Further, summary data are provided to allow the analyst to present hierarchies like Figure 1 with frequencies and percentages of subjects by treatment, with values summarized at either the individual SMQ level or cumulatively considering whether a patient belonged to any sub-SMQs. Analyses can use Narrow sets of terms for specificity or Broad sets of terms for sensitivity. JMP Clinical can even handle the SMQs with more complicated algorithms. These analyses can provide greater insight into the underlying safety of patients within the clinical trial.