Limits of the Possible: Clinical Reasoning of a Harrowing Extubation

"The only way of discovering the limits of the possible is to venture a little way past them into the impossible."  -  Clark's Second Law

In prior posts here and on the Medical Evidence Blog (here, here, here, and here), I have outlined my position that the only way you can really know if a patient can breathe on their own is to let them try - a "trial of extubation".  Prediction equations get you published, but their signal to noise ratio is often poor and ignored, to patients' peril.  Indeed the reason I'm obsessed with extubation is because I think being intubated unnecessarily is one of the worst things a patient can endure, and the best thing I can do as an intensivist is identify the earliest moment when a patient can breathe on his own and extubate him.

I faced a very harrowing extubation decision recently, and I admitted to the medical students that it was the most nail-biting of my career.  But I think analyzing it, both before and after the fact can be very instructive.

Root Cause Analysis: Dig Deeper, or the Weed Will Keep Growing Back

In a recent JAMA Performance Improvement piece, the authors describe the case of a man who presented to the emergency department with dizziness.  He was sedated for an MRI, his history of OSA (obstructive sleep apnea) may have been glossed over, and he arrested in the radiology department.  The subsequent "root cause analysis" traced the untoward outcome to a failure to recognize the OSA and the adverse effects that may follow sedation of a patient with this diagnosis.

The problem with this "root cause analysis" is that it assumed that the MRI, requested by a neurologist on-call, via telephone, was necessary.  It was not.  The root cause analysis got it wrong because it did not trace the roots to their deepest source:  glossing over the patient's chief complaint and considering it and its evaluation carefully and rationally.  Stroke is an uncommon cause of dizziness and the MRI was probably not indicated, especially in light of the other information provided in the case.

Here is the letter that I sent to JAMA which was not accepted/published.  It is a case of the distinction between rationality and intelligence.  Very intelligent people traced the "cause" or the "root" of the complication to a missed piece of information (OSA) and corollary ideas (he may have complications from sedation), but they failed to consider underlying assumptions:  namely that the MRI was necessary or would yield net benefit in the first place. 

Medicine is best played like chess, not like checkers.  "Intelligent people have superior performance when you tell them what to do."  A failure of a "root cause analysis" such as this will foment the regrowth of the weed.

Here is the letter:

I enjoyed the Performance Improvement case describing oversedation of a patient with obstructive sleep apnea¹.  I posit that the most proximate possible root cause of the complications described was ordering an MRI with low clinical yield², without pre-specifying what abnormality was being sought as well as its probability, and without delineating, a priori, how any resulting findings would change management³.  Presumably, the neurology consultant was looking for stroke.  What was its pre-test probability in a patient with dizziness?  Would management have changed if stroke were detected with imaging?  Were there contraindications to therapies for stroke?  Was the patient already receiving the indicated therapy for stroke?  What is the probability of a false positive finding (i.e., one that doesn’t explain the patients’ symptoms; an “incidentaloma”), and how might that finding lead to interventions which may yield net harm if stroke is not present?  What was the response to meclizine and odansetron, and how did this incremental information alter the prior probability of stroke?  Because decisions necessarily precede actions, they must always be considered as possible proximate causes of downstream complications.  Even if the other errors identified in the reported root cause analysis can be avoided in the future, injudicious testing may lead to other complications, including cascades of additional potentially harmful testing and intervention unguided by careful, rational, clinical decision making.

1. Blay E, Jr, Barnard C, et al. Oversedation of a patient with obstructive sleep apnea prior to imaging. JAMA 2018;319(5):495-96. doi: 10.1001/jama.2017.22004

2. Fakhran S, Alhilali L, Branstetter BFt. Yield of CT angiography and contrast-enhanced MR imaging in patients with dizziness. AJNR American journal of neuroradiology 2013;34(5):1077-81. doi: 10.3174/ajnr.A3325 [published Online First: 2012/10/27]

3. Pauker SG, Kassirer JP. The threshold approach to clinical decision making. The New England journal of medicine 1980;302(20):1109-17. doi: 10.1056/nejm198005153022003 [published Online First: 1980/05/15]

Screening in Disguise: You Can't "Unknow" that Troponin, But You Can Dismiss It After Careful Thought

During MICU rounds last month, there were a lot of troponins ordered, and most of them should not have been.  Invariably when abnormal troponin values are reported on rounds, there is no mention of whether the patient had anginal chest pain, whether there were ischemic EKG changes, or whether this information was sought at the time the troponin was drawn.  This is because troponins are being used as a screening test, rather than as a diagnostic test.  "Not so!" exclaims the resident, eager to convince me that he has not engaged in the kind of mindless testing he knows I loathe.  I am told that because the first troponin was mildly elevated in a little old lady with cirrhosis, overdose, right heart failure and urinary tract infection, that we need to follow it to see where it "peaks".

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.

Specious Ideas: Trending Troponins and Chasing Lactates

I want to use this post to discuss an article in this week's JAMA called Lactate in Sepsis, which I think is fatally flawed and misleading.  But first...

Several years ago on the Medical Evidence Blog I talked about cardiac troponins and how their use is often misguided.  Not long after this post a young woman e-mailed me to describe a diagnostic and therapeutic misadventure that ensued after an abnormal troponin was "discovered" during work-up for a urological problem.  This led to transfer to another facility via ambulance for a cardiac catheterization with multiple complications including stroke.  It was a sad and unfortunate tale, but I fear it is not too uncommon.

Troponin, like all tests, needs to be ordered on the basis of a clinical suspicion (prior probability) that, when combined with the likelihood ratio of the test using Bayes Theorem (see calculator on the right of the blog), results in a posterior probability of disease that crosses a decision threshold.  (Because of the woeful inadequacy of medical education in regards to basic decision theory, I would not be surprised if the majority of physicians cannot correctly describe priors, Bayes, posteriors, or decision thresholds.  But this is old news, and beyond the scope of this post.)  The low prior probability of acute coronary syndromes in critically ill patients with non-cardiac primary diagnoses (PE, AECOPD, sepsis, etc.) leads me to list "non-specific troponin increase in the setting of critical illness" as a problem (an artificially begotten one) in my assessments after colleagues regretfully order tests that should never have been ordered.  And I will defer discussion of all those d-dimers and the needless CT angiograms they engender, lest I descend into unmitigated belligerence.

Underperforming the Market: Why Researchers are Worse than Professional Stock Pickers and A Way Out

Here's an occasional cross post from the Medical Evidence Blog.  It's a bit more technical than what is usually posted on Status Iatrogenicus, but it gives a summary description of a pervasive problem with "evidence based medicine" that is so widely applicable to practice that I think it deserves some recognition here.

Underperforming the Market: Why Researchers are Worse than Professional Stock Pickers and A Way Out

Reflexes are for Knees! Geez! Why do you need so many ABGs? (An introduction to Bayesian Clinical Decision Making.)

I wasn't always like this.  Ask co-interns and they will tell you I was the most notorious minutiae-obsessed physiology manipulator west of the Mississippi. 

What changed?  Well, I grew up and realized that micromanaging physiology is most often a fool's errand.  Evolution was indeed a brilliant chemist (Max Perutz), and I recognize my impotence in one-upping him.  I can order zero ABGs or a dozen ABGs in a week and little changes but the volume of blood that is flushed down the drain.

So, using an example from earlier in the day, I'll lead you through a stream of consciousness explanation of why I can most often do without an ABG.

A man in his 30s is admitted for alcohol withdrawal (WD) for the sixth time in 12 months.  About half of these times, his WD has been severe and he has required ICU admission.  Overnight, during the administration of benzodiazepines for his WD symptoms, he has become progressively tachycardic and tachypneic and his oxygen needs have been steadily increasing.  His saturation on the monitor displays a good tracing at 95%.  BIPAP is applied.  I can hear his respiratory rate at about 25, and based on the flow I hear from the BIPAP machine, I can guess that his minute ventilation is about 15 liters per minute (these guesses could be confirmed with RT).   Knowing nothing else about his case, I am asked if an ABG should be ordered to assess his respiratory status.  Should it?