On Capturing Information about Medical Conditions

In today's post, I discuss Medical Conditions in some detail, with a focus on an important question: how do we best capture information about medical conditions as they evolve over time? This is an important question because understanding how medical conditions change over time is key to understanding how medical interventions affect those conditions. As usual, I focus on subject level clinical data collected in clinical trials but I think this is equally valid for other use cases.

I define a Medical Condition as a disease, injury, disorder, or transient physiologic state that interferes or may interfere with well-being. A medical condition persists in time. Medical conditions also evolve over time. The practice of medicine focuses on minimizing the impact of medical conditions to one's health. 

So how do we best document the evolution of medical conditions over time? My thinking on this topic is heavily influenced by a very useful paper, which I encourage you to read. It's titled "Toward an Ontological Treatment of Disease and Diagnosis," by Richard H. Scheuermann, Ph.D., et. al. It provides precise definitions for common terms such as a Disorder, Pathological Process, Disease, etc. This precision the authors argue is important to enable automated analysis and reasoning across aggregated clinical data from multiple sources. 

Their definition of Disease is: A disposition to undergo pathological processes that exists in an organism because of one ore more disorders in that organism. A disorder in turn is something that is wrong with the body and is associated with a pathological process. For example, Epilepsy is a disease that disposes the individual to recurrent seizures (disorder/pathological process). As another example, consider Systemic Lupus Erythematosis, a disease that disposes the individual to multi-organ autoimmune damage that may be manifested by multiple disorders: dermatitis, arthritis, pericarditis, nephritis, etc. One has to understand the underlying disorders in order to fully understand the disease. 

Notice that my definition of a medical condition includes both diseases and disorders, because sometimes one doesn't know the disease, rather just the disorder that is manifest, but it's useful to draw that distinction when one can. Understanding the disease can help select the best treatment for the underlying disorder. For example, the disorder may be a bone fracture from an injury, but additional observations may disclose the disease Osteoporosis, which would affect the treatment plan.  

So the clinical data flow in my mind goes something like this:  clinical observations give rise to an assessment to identify/characterize one or more disorders, which may enable the identification of a disease. 

One begins to see how to organize the data for maximal use downstream. Clinical observations are grouped together during an assessment to identify and categorize a disorder and possibly a disease. Both disorders and diseases are events that persist in time, so each is associated with a start and end date. Each also has a diagnosis date, i.e. the date an assessment first identified the condition. Severity is an observation that can be associated with both disorders and diseases, and that changes over time. 

What are the implications for the SDTM? In a previous post I argue for a single Medical Condition domain that describes each medical condition, past and present, for each subject. Each record is a medical condition (e.g. a disease or disorder) and has standard attributes such as start date, end date, diagnosis date, etc. One needs to group and link all the disorders that pertain to a given disease. So for schizophrenia, one would list all the known psychotic episodes for that subject and link them to the schizophrenia disease record. Same thing with Multiple Sclerosis. The M.S. record would link to all the known relapses (disorders). There would also be links to the observations that were used to characterize the disorder, and an optional link to the assessment (i.e. adjudication) record that contains details of that assessment. Each disease/disorder should have standard outcome measures (clinical observations) and validated methods to observe and document severity at given points in time. For example, Parkinson's Disease has the UPDRS (Unified Parkinson's Disease Rating Scale). Diabetes has the Hemoglobin A1C and others. Once again we need universal resource identifiers (URI's) to facilitate linking all these data. 

What we have now is the ability to plot all of a subject's clinical observations in a clinical trial over time, i.e. the patient profile. But this lacks important information in how assessments were performed to link observations to disorders and diseases to assess changes to the disease over time. When one looks at the various Therapeutic Area User Guides each takes a different approach in documenting changes to medical conditions over time. This proposed single approach I think is applicable for all therapeutic areas. Once we have a clear, standard representation of the changes to a medical condition over time, then I think it will be easier to automate analyses that look at the effects of various interventions, including experimental interventions of course. 

As usual, I welcome your comments.