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.

On Observations in Clinical Trials, or, "Did I get that observation right?"

I live in Florida, a state almost surrounded by water. How long is its coastline? How does it compare with the coastline of other states? So, like many others, I turn to ... Google. In a few seconds, I find these results posted on Wikipedia:

You can predict my reaction. How can the method make such a big difference in the results? The web site provides detailed information about each method and it becomes a relatively easy, though highly manual, task to determine which method is more appropriate for one's use case. The take home lesson is clear: the method of observation may affect the results.

Then there is the famous Heisenberg Uncertainty Principle in Physics, which states that the position and velocity of an object cannot both be measured exactly at the same time, even in theory. For large objects, like an automobile, the uncertainty is negligible, but for sub-atomic particles, this is a big deal. The fundamental reason behind the uncertainty is due to in part to the act of making the observation, i.e. the method of observation. In other words, any attempt to measure precisely the velocity of an electron, for example, "will knock it about in an unpredictable way, so that a simultaneous measurement of its position has no validity."

Just so you don't think this concern is limited to physics and geography, consider this well-known medical school fact. A standard blood pressure cuff, when used on significantly obese individuals will typically provide a falsely high reading when compared to the same observation performed using an over-sized cuff. So take note:

The method of observation may affect the results.

This give rise to another "aha!" moment: Observations are Interventions. The observer must intervene in the subjects normal daily routine and execute a specific method of observation to obtain the observation result. Sometimes the method is innocuous like answering a question on a questionnaire, but sometimes can be quite invasive, like a cardiac catheterization to measure coronary artery diameters. Often times the observation and results are combined with an interpretation of the observation(s) (i.e. an Assessment), to establish the presence and severity of a Medical Condition (e.g. Coronary Artery Disease) and its severity. More and more an Intervention to make an observation is combined with an attempt to alter the natural history of the Medical Condition (i.e. a "Therapeutic Intervention") as in the case of a diagnostic cardiac catheterization during which a drug-eluting stent is inserted.

The bottom line is we need to recognize that observations are interventions whose main purpose is to measure the physical, physiological, or psychological state of an individual, and that the details of the method used to make the observation can be very important and may introduce bias in the results.

The take home message of this blog is: An observation doesn't just happen. Someone intervened to make it happen and the method of intervention can affect the results.

Both the SDTM and BRIDG consider observations (called "findings") as different than interventions. It's time to update that thinking. "Findings" are a type of interventions. Furthermore, SDTM considers findings, interventions, and events as different types of observations. I disagree. Events, for example, are not observations. This last statement is a topic of a future blog.

Thank you for your comments.