Modeling Adverse Event Information

What is an event? It's an occurrence; something that happens. There are countless examples: a baseball game, a wedding, a picnic. All are examples of an event. Let's also include adverse events. One common feature of events is that they persist in time. They have a beginning and an end. Are they observations? No. But we use observations to determine that an event is taking place: one makes numerous observations: the sunny day, the location in a park, the presence of a cooler full of food and drinks, a blanket to lie on the grass, a charcoal grill. Add them all up and you come up with a picnic. We then observe the absence of many of these observations to conclude the event is over. Pretty straightforward.

Adverse Events are no different. Symptoms or signs (observations) begin on a certain date and end on a certain date. One must interpret multiple observations over time to determine that an AE is taking place, or that an AE has resolved. This interpretation in clinical medicine is known as an Assessment, and it is the "A" in the medical encounter record known as as SOAP note. I wrote about modeling clinical data and the relevance of SOAP in a previous post. Although observations also technically have a beginning and an end (e.g. a venipuncture does not occur instantaneously), they should be considered for practical reasons to occur instantaneously. They are a "snapshot in time" of the subject's well being, or lack thereof.

Another thing to keep in mind is that Adverse Events are Medical Conditions (disease, disorder, injury, transient physiological state that can impair health) that are temporally associated with an intervention of some kind (e.g. drug administration), and if noted for the first time in a Subject, it is called a Diagnosis. It's also important to have a qualified Assessor to establish the presence of the correct Adverse Event. Sometimes the Assessor is the patient, who assesses, for example, their headache patterns and concludes they have tension headaches and self-administers an over the counter analgesic. Being able to self-assess your medical condition is in fact a regulatory requirement for a drug to be sold over the counter. Makes sense. But often one needs a trained Assessor, e.g. physician, nurse, to determine that the correct AE is present. Sometimes that assessment is not done properly (or not documented properly) and then problems occur, and second opinions (re-assessments by new assessors) are necessary. Assessments are often associated with Assessment Criteria. These are rules that describe how observations are analyzed and interpreted to determine the presence and severity of a medical condition. Another useful example is a simple blood pressure measurement that is abnormally high, say 150/100 mmHg. Does a single high BP measurement imply that the person has an underlying medical condition known as hypertension? The answer is clearly NO. The proper assessment requires that serial BP measurements are conducted over a period of time to establish the persistence of a clinical event (in this case a disorder) known as hypertension.

So currently, Adverse Event reporting, whether it's in clinical trials or post-marketing safety monitoring, is fraught with the fact the observations (that are used to assess the presence of an AE) and the AE itself are often mixed together, and the analyst must do his or her own Assessment after the fact. Take, for example, the following report of a patient who takes a dose of drug X and then 2 days later develops a sore throat, runny nose, nasal congestion, cough, sinus pain, and viral nasopharyngitis. Not all of these are AEs. The first five are in fact observations that support the presence of the sixth, the true medical condition at play here. Sometimes the observations don't clearly support the presence of a medical condition, in which case a "differential diagnosis" is developed, which is essentially a list of all the medical conditions that could possible cause the observations, followed by a systematic collection of more observations to identify the correct diagnosis.

There is a strong desire within FDA and elsewhere to automate the detection of adverse events. This is quite a challenging task, but it should be made clear that the following must take place before any system or tool can succeed in adverse event detection.

1. We need to distinguish observations from events
2. We need qualified assessors to analyze/interpret the observation results
3. As much as possible, we need to standardize the assessment process by documenting the assessment criteria necessary to identify an AE with high confidence. 

Adverse Event Identification and Characterization

Here is my proposal for a workable data model that can be used to automate AE detection some day. It should be made clear that it deviates from the SDTM and BRIDG notion of an event, as I don't believe these models have it quite right. Remember that observations must undergo an Assessment to determine if a medical condition / AE is present. Sometimes more than one Assesments are done (e.g. second opinions). Finally observations don't get treated, rather the medical condition(s) that are the cause of the abnormal observations are the targets of treatment.