Using Probability in Medical Diagnosis: A Headache Example

 Using Probability in Medical Diagnosis: A Headache Example

Experienced clinicians begin the process of making a diagnosis upon first laying eyes on a patient, and probability is one of the main tools they use in this process. A glimpse "behind the scenes" from the point of view of a diagnosing physician might help to explain an otherwise mysterious process.

The diagnostic process can begin even before setting eyes on the patient. As an exercise (and to prove a point) I ask medical students who are with me in the office to diagnose the patient we haven't seen yet who is still in the waiting room. Of course, people stare at me like I'm insane. But I tell them that we already know a lot about the patient and can make some educated guesses. For example, we might already know that the patient is a 34-year-old woman referred by a family doctor because of headaches.

So what have other women in their thirties referred to me for headaches ended up having as their diagnosis? In my neurology practice, as well as in those of most other headache specialists, about a third (33%) have migraine, another third have medication-overuse headaches (in which the treatment has become the problem instead of its solution), and the remaining third fall into a "everything else" category that includes tension-type headaches, arthritis of the neck or jaw-joints, sinus disease, tumors, etc. So before seeing the patient I'm already able to identify the two most likely diagnoses and assign an initial probability for each.

"Anchor" probabilities describe these initial chances. During the subsequent history, examination and supplemental testing (if necessary) the anchor probability will undergo a series of upward and downward revisions based to what the patient has to say and what does or does not turn up on her physical examination and tests. The physician individualizes the questions asked and items examined so that the outcome of each query forces one diagnosis to be more likely and another to be less likely. Thus, diagnosis is a dynamic and sequential process.

We invite the woman into the examining room and listen to her story. In the headache example presented, one essential piece of data is how many days per month she takes an as-needed medication - for example, aspirin, acetaminophen or a prescription pill. If she uses as-needed medicine more days than not and has been doing so for a handful of months, then the initial 33% anchor probability of medication-overuse headaches is modified upward and the initial anchor probability of simple migraine moves downward. This, of course, is just a single differentiating trait, and cannot be relied upon to reveal the complete narrative. The clinician obtains numerous such data points to refine the diagnosis.

The physical examination gives another source of evidence to select among still-viable prospects. If my patient has migraine or medication-overuse headaches, she might have sore muscles in her scalp and neck but should not have a blind spot in her visual fields, slurring of her speech or awkwardness on only one side of her body. These findings, if present, would cause the likelihood of migraine and drug overuse headaches to be lowered downward. However, a brain disease's (such as a tumor's) initial low anchor probability would be revised upwards.

Again, the underlying assumption is that any imaging or blood tests ordered will be tailored to distinguish between possible diagnoses and re-adjust their relative likelihoods.

There is an important concept in medical diagnosis called Bayes' theorem. In a nutshell, Bayes' theorem argues that the likelihood of a diagnosis after a new fact is added relies on what its probability was before the new fact was included. The same "yes" response on a history-taking form, a reflex result on a physical exam, or a dark spot on an MRI scan can mean different things to different people. The meaning of each depends on its context. Yet another aspect of Bayes' theorem is that one can't go over the history and examination by ordering a test in isolation and expect it to produce an accurate diagnosis. A test is an answer to a query. If there was no question, how could the exam constitute an answer?

Let's imagine that at a specific point in time we have completed the diagnostic process for a patient. So what happens next? In certain instances, the doctor may wind up with a diagnosis that is very certain; yet, in others, the working diagnosis (number one choice) may only be 70% or 80% probable, and a second choice, albeit less likely, may still be considered. Doctors wouldn't be helping their patients by continuing to analyze beyond the conclusion that the current information points to, even though it could be uncomfortable for some patients to accept that the diagnostic procedure does not always lead to 100% certainty.

If a diagnosis is not immediately apparent after the initial evaluation, further information about the patient's symptoms over time can help change the diagnostic odds. Thankfully, when there is uncertainty, the doctor and patient can still make acceptable decisions by reducing the list of possible diagnoses to a manageable amount.

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