Bayesian Implications for Factitious Disorder

The next time you encounter a patient with an odd constellation of rare or poorly defined diseases (POTS and MCAS is a common duo), especially if a psychogenic one is on the list (e.g., PNES - not, I presume, intended to be sounded out, as it was proposed as a less offensive alternative to pseudoseizures), do yourself a favor and read one of Mark Feldman's excellent books on factitious disorder, such as the most recent, Dying to Be Ill.

These patients are wellsprings of iatrogenesis, but it is a special variety because the patients actively deceive physicians into unwittingly fostering complications through well-intentioned attempts to diagnose and treat disease. Several biases in medical decision making are exploited by patients with factitious disorder (and its close sibling, malingering, where the reward for the sick role is external - e.g., relief from work, military service or thwarting criminal justice proceedings - rather than internal), and I address some of them in this post.

The Truth Doesn't Always Need A Test: Thresholds for Medical Decisions

Jason Carr, MD didn't need a test to know what this is

Kassirer (and Pauker) got the idea for the Threshold Approach to Medical Decisions in part (as he tells it in his memoir Unanticipated Outcomes) because he had observed a pattern of irrational behavior among nephrologists (he was one) in the 1960s.  Evaluating a patient with nephrotic syndrome and knowing the diagnosis was very likely to be membranous glomerulonephritis, most nephrologists still insisted on a biopsy prior to starting corticosteroids.  Pauker and Kassirer realized that we never get to 100% certainty in medicine and that tests only modify the pre-test probability of disease.  They reasoned that there ought to be a threshold of pre-test probability of disease that is high enough to justify treatment without testing.  That is, sometimes, the pre-test probability of disease is so high as that it obviates testing, and we can just skip to treatment and avoid risky and invasive testing like a kidney biopsy.  Only if the disease fails to respond to therapy (thereby reducing the clinical pre-test probability below the treatment threshold) should we resort to the biopsy.

Almost 40 years after the publication of their article, we still are not consistently following the axioms of the threshold approach.  Here are several recent examples:

A 20-something black woman presents with cough, weight loss, massive hilar and mediastinal lymphadenopathy and interstitial parenchymal changes.  She is scheduled for a bronchoscopy and EBUS to confirm the diagnosis of sarcoidosis prior to initiating corticosteroids.

The Test is Not the Truth: One Week in the Lonely Life of a Bayesian Clinician

DAH from GPA or CPE/ESRD?

If there is one thing you should remember about clinical decision making it is this:  the test is not the truth.  A diagnostic test raises or decreases the prior or pre-test probability (PTP) of the disease under consideration.  The amount of increase or decrease in probability with a positive or negative test depends on the starting probability and the likelihood ratio of the test.  (LR+ = sensitivity/1-specificity; LR- = 1-sensitivity/specificity).  If we don't attend to the PTP of disease, serious diagnostic errors and therapeutic misadventures may result.  This is especially true when a low PTP disease is diagnosed on the basis of a test with poor sensitivity and specificity (and a LR not much greater than 1 or 2 or even 4 or 5).  Several examples of this came up a while back.

A woman presented with thunderclap headache and had recurrent seizures during initial evaluation.  A differential diagnosis was formulated and it included PRES (posterior reversible encephalopathy syndrome) with a PTP of about 20%.  Subarachnoid hemorrhage was excluded with CT and LP and the PTP of PRES rose to about 40% (since it occupied some of the probability space previously occupied by SAH once the latter was excluded.)  The subsequent MRI images were consistent with PRES.  Nonetheless, a vascular MRI was ordered to "exclude the possibility of cerebral vasculitis".  The problems are twofold.  First, the probability of PRES is now on the order of 70% if the sensitivity and specificity of MRI are on the order of 80%, and it is 85% if sensitivity and specificity are each 90%.  (Go ahead and plug some numbers into the calculator on the sidebar of the blog.)  This probability meets or exceeds the probability threshold to both consider the diagnosis made, and to take action based on it.  In this case inaction and supportive care are indicated.  Even if a vascular MRI were consistent with cerebral vasculitis, which has a PTP an order of magnitude or more less than PRES, the diagnosis is still PRES.  The truth is not in the test, the truth is in the rationally considered diagnostic process of which the test is one part.

Bayes' Theorem Explained, No Math Required

I was asked by a medical student to explain Bayes' Theorem.  This blog is about lack of common sense in medicine, so it follows that education about first principles will contribute to uncommon sense, and I will oblige.

Bayes' theorem is simply a long or holistic way of looking at the world, one which is more in keeping with reality than the competing frequentist approach.  A Bayesian (a person who subscribes to the logic of Bayes' Theorem) looks at the totality of the data, whereas a frequentist is concerned with just a specific slice of the data such as a test or a discrete dataset.  Frequentist hypothesis testing is where we get P-values from.  Frequentists are concerned with just the data from the current study.  Bayesians are concerned with the totality of the data, and they do meta-analyses, combining data from as many sources as they can.  (But alas they are still reluctant frequentists, because they insist on combining only frequentist datasets, and shun attempts to incorporate more amorphous data such as "what is the likelihood of something like this based on common sense?")

Consider a trial of orange juice (OJ) for the treatment of sepsis.  Suppose that 300 patients are enrolled and orange juice reduces sepsis mortality from 50% to 20% with P<0.001.  The frequentist says "if the null hypothesis is true and there is no effect of orange juice in sepsis, the probability of finding a difference as great or greater than what was found between orange juice and placebo is less than 0.001; thus we reject the null hypothesis."  The frequentist, on the basis of this trial, believes that orange juice is a thaumaturgical cure for sepsis.  But the frequentist is wrong.

The Medical History as an Exposure Narrative: A Didactic for Medical Students and Young Physicians

I just received an email from a medical student on the other side of the pond asking for my advice for junior doctors for learning the practice of medicine.  I will oblige.  When students are taught how to take a medical history, they are taught a rote sequence and its components, but are not taught what the point really is, why we ask certain questions, how we string the answers together, which components need more or less emphasis in a given case.  Here I will present a framework for that understanding, which may make history taking more meaningful and useful for those learning and refining it.

What we are really trying to do with history taking is to make a narrative of the patient's exposures in his or her environment.  This exposure narrative allows us to use Bayes' Theorem to determine the most likely causes for a given chief complaint.  Bayes Theorem should be reviewed for its own sake in order to understand its use here and elsewhere, but simply understanding that the base rate of a disease in a certain population is the "prior probability" of that disease in a patient will suffice for now.  So, if I asked you what mammal you are likely to see on your hike in the Rocky Mountains, you will list squirrel and deer and elk before mountain lion and badger.  It's just common to see deer and elk there.  In the jungle, or the desert, the answers would be different - because different animals have different probabilities in different environments.  Likewise, when a 70-year-old comes with pain in the joints, osteo- and rheumatoid arthritis are more likely than lupus and juvenile rheumatoid arthritis, which would have higher probabilities in younger patients.  Thus, age is an exposure, perhaps one of the most important ones, and this is why a student's presentation often begins with something like "Mr. Jones is a 78-year-old man..."  (It is also why I frequently interrupt physicians who call me to admit a patient or consult on one, because I can't begin to order the probabilities until I know the age of the patient, which they frequently omit because of indolence.)  The older person has been exposed to wear and tear on the body for a longer time, and this figures prominently in the probabilities of the diseases that s/he is likely to have.