Why is the Bayesian method not employed for diagnosis in practice

Xavier Prida dr.xavier.prida at GMAIL.COM
Wed May 30 21:00:14 UTC 2018


Lynn,
             You are on point. It is about iterative testing and when
things don't fit-* challenge your inferences.*

Read:  Snakes on a Dock(attached)

XEP

* praesent superare odio  (r**ise above)*

Xavier E. Prida MD FACC FSCAI
Assistant Professor of Medicine
Program Director Cardiology Fellowship Training
USF Morsani College of Medicine
Department of Cardiovascular Sciences
2 Tampa General Circle
STC 5 th Floor
Tampa, Fl 33606
813 259 0992(O)


On Wed, May 30, 2018 at 2:51 PM, Lyn Behnke <lynbehnke at gmail.com> wrote:

> I have to think that Bayesian method is primary to the diagnostic
> process.  We develop an initial differential diagnosis and then select the
> tests to help refine that diagnosis.  In our case, a small rural hospital,
> it is not at all uncommon to “run over to see the Rad” to give them more
> information that would improve their diagnostic acumen and increase their
> sense of probability.  I come from the time when MRI first was used and all
> those FWA were actually plaques, but we didn’t know it then.  If we didn’t
> use a Bayesian method of interpretation, we may never have found out what
> those meant.
>
>
>
> If we are smart, we use a focused Bayesian method in most problem-solving
> situations.  For example; I had to clean my deck over the holiday weekend.
> I did all the reading and selected a product that purported to clean off
> mold, stains, and not harm plants or animals.  I purchased it, mixed it
> according to directions, and applied it to a test area as instructed.  The
> information that I gleaned from that was that the product didn’t work on
> mold stains.  Consequently, I had to change processes based on the data.
> Now, that doesn’t describe the gnashing of teeth, the trip to rent a power
> washer, the cramps in the hands and the total stripping of the deck.  If I
> had not used that information, (instead of the probability given the
> marketing) I would have happily put that stuff all over the deck, waited
> for it to work, and then have to wait until today to rent the power washer,
> thus causing a delay in treatment of the deck.
>
>
>
> I don’t mean to subjugate the importance of appropriate diagnosis and the
> gravity of the treatment delay.  I am only making the point that we must
> pay attention to data gleaned as the process moves along, and use that
> information as well as probability to improve our diagnostic processes.
>
>
>
> *From: *"Jain, Bimal P.,M.D." <BJAIN at PARTNERS.ORG>
> *Reply-To: *Society to Improve Diagnosis in Medicine <IMPROVEDX at LIST.
> IMPROVEDIAGNOSIS.ORG>, "Jain, Bimal P.,M.D." <BJAIN at PARTNERS.ORG>
> *Date: *Wednesday, May 30, 2018 at 11:44 AM
>
> *To: *<IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
> *Subject: *Re: [IMPROVEDX] Why is the Bayesian method not employed for
> diagnosis in practice
>
>
>
> The reservation I have about the Bayesian method is about considering a
> prior probability which is a frequency in a population to be prior evidence
> for a disease in a given patient. This notion may make us not suspect a
> disease if its prior probability is low leading to a diagnostic error.
>
> It is of interest that in the two examples given by Dr. Elias, the
> presence of a disease is assessed in terms of a likelihood ratio, and not a
> prior probability. For example, IBS being more likely than splenic artery
> aneurysm given the GI symptoms means that the likelihood ratio of IBS is
> high compared to aneurysm.
>
> Similarly, the likelihood ratio of viral illness is high compared to strep
> throat given the nasal and respiratory symptoms.
>
> What these likelihood ratios are, I do not know, but they may be high
> enough, that further testing may not be required.
>
> Another example of a high likelihood ratio  for a disease is that for
> herpes zoster, given unilateral, blistering skin lesions in a dermatomal
> distribution which does not require further tests for a diagnosis.
>
> In diagnosis, in which our goal is accurate determination of a disease in
> a given patient, evidence from which we diagnose a disease is represented
> by a likelihood ratio as it represents a change in probability ( odds ) of
> a disease in a given patient , locating evidence in this particular patient.
>
> A probability, being a frequency in a population locates evidence in a
> population. Therefore, an inference from a probability  is made in a field
> such as life insurance business in which the goal is long run accuracy with
> tolerance for errors in some individual persons.
>
> I have never come across a diagnosis being made purely from a probability
> in the absence of data with significantly high likelihood ratio in
> published case discussions or in practice.
>
> There seems to be a general impression that evidence in an uncertain
> situation can only be represented by a probability and an inference made
> from it alone. This is not true, as there is flourishing school of
> inference from likelihood alone as seen in books by AWF Edwards, Richard
> Royall, Y. Pawitan and many more authors.
>
> A positive CT study for appendicitis has a likelihood ratio of 19, about
> the same for positive CT angiogram for pulmonary embolism. In practice,
> this study should allow us to diagnose appendicitis definitively with a
> high degree of accuracy in any patient, regardless of prior probability.
>
> It would be of interest to know the diagnostic accuracy of this test
> result across patients with varying prior probabilities  similar to the
> diagnostic accuracy of acute Q wave and ST elevation EKG changes for acute
> myocardial infarction being 85 percent in patients with varying prior
> probabilities.
>
>
>
> Bimal
>
>
>
> *From:* Elias Peter [mailto:pheski69 at GMAIL.COM]
> *Sent:* Sunday, May 27, 2018 12:12 PM
> *To:* IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> *Subject:* Re: [IMPROVEDX] Why is the Bayesian method not employed for
> diagnosis in practice
>
>
>
> *        External Email - Use Caution        *
>
> I find the proposal that Bayesian analysis is not (or should not be) part
> of the initial diagnostic approach puzzling. The following examples
> illustrate why I feel that way:
>
>
>
>    - The 30-year old who presents with 2 years of intermittent left upper
>    quadrant pain and fluctuating bowel habits is more likely to have irritable
>    bowel syndrome than splenic artery aneurysm. One would not start evaluation
>    with imaging the LUQ.
>    - The 8-year old with 4 days of runny nose, cough and sore throat is
>    so much more likely to have a viral illness than a strep throat that a
>    throat culture is poor medical practice.
>
>
>
> Peter Elias, MD
>
>
>
>
>
> On 2018.05.27, at 8:53 AM, Bruno, Michael <mbruno at PENNSTATEHEALTH.PSU.EDU>
> wrote:
>
>
>
> Thanks Dr. Bimal for starting this interesting thread, and to Stefanie Lee
> for sharing that excellent 2006 paper from Blackmore, *et al.,* as well
> as to Dr. Oldham for his insightful comments.
>
>
>
> I think the Blackmore paper is really touching on the topic of "signal
> detection theory," which is a very useful tool to understand about how
> useful information is actually extracted from complex data sets (like CT
> scans) that have extremely high levels of uncertainty built-in. We
> radiologists practice in a milieu of extraordinarily high undertainty, as I
> noted in my 2017 review in our Society's official journal, *Diagnosis* (DOI
> 10.1515/dx-2017-0006), so this is particularly relevant to us.
>
>
>
> I've attached an excellent PDF discussion of signal-detection theory as
> applied to diagnostic radiology to this message for anyone who might be
> interested.  While radiologists and others may recognize the use of ROC
> curves, which are a feature of this theory, they may not be as familiar
> with the concepts of d', a quantitative measure of how well the
> alternatives can actually be discriminated from the test, and the concept
> of "criterion," whereby the interpreter decides how sensitive *vs*.
> specific they wish to be.  This was the thrust of the Blakemore article,
> and his meta-analysis suggested that radiologists by-and-large got it
> right.  (For David Meyers--the attached PDF is in the public domain,
> provided by Professor David Heeger of NYU).
>
>
>
> Dr. Oldham also got it right, of course, saying that Baysean reasoning is
> not everything in diagnosis, but I believe that it takes us *most of the
> way*.  I very much appreciate Dr. Oldham's analogy that the radiologist
> is acting as the "expert witness" in a courtroom setting while the treating
> physician and patient / patient's family serves as the judge and jury, who
> are charged with ultimately deciding what "truth" is.
>
>
>
> Have a terrific Memorial Day Weekend, everybody!
>
>
>
> Mike
>
>
>
>
> ------------------------------
>
> *From:* James Oldham <james.oldham at HEALTH.NSW.GOV.AU>
> *Sent:* Sunday, May 27, 2018 1:49 AM
> *To:* IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> *Subject:* Re: [IMPROVEDX] Why is the Bayesian method not employed for
> diagnosis in practice
>
>
>
> Dear all
>
> There is a difference between the treating doctor who makes a
> probabilistic diagnosis and evidence providers such as radiologists who
> comment on phenomena the observe and interpret it - taking Bayesian prior
> probability calculations explicitly into their offerings. I think the
> testing physician and their patients and family as a collective Judge
> relying on evidence provided by expert witnesses. So this final stage will
> be informed by Bayesian logic but not ruled by it and certainly not trying
> to make a prediction- just best information to hand
>
> James Oldham
>
> Child Psychiatrist
>
> NSW
>
> From James Oldham - sent from my phone. (Please accept apologies odd
> sentence construction and creative additions courtesy of Apple autocorrect)
>
>
> On 27 May 2018, at 13:25, Stefanie Lee <stefanieylee at GMAIL.COM> wrote:
>
> An interesting paper from Blackmore and Terasawa, JACR 2006 on the
> complexity of CT interpretation and how clinical probability may be
> factored into the decision to call a test result positive or negative:
>
> "In this paper, we present an example of how health utility assessment can
> be used to guide the optimum interpretation of an imaging test."
>
>
> "Radiologists have the ability to alter the sensitivity and specificity of
> their interpretations. For objective positivity criteria, different
> thresholds can be chosen to consider test results positive. For example,
> with appendicitis, one important factor in CT interpretation is the size of
> the appendix. Using a lower size threshold (eg, 6 mm) to consider an
> appendix abnormal will result in higher sensitivity for appendicitis, at
> the expense of lower specificity. Using a higher size measurement to
> consider the appendix abnormal (eg, 8 mm) would result in lower sensitivity
> but higher specificity. For subjective criteria such as periappendiceal fat
> stranding, the process is the same but less explicit. Individual
> radiologists can alter how much increase in the density of the fat is
> necessary to be considered abnormal “stranding.”"
>
> "In general, when a disease is rare and there are substantial costs to a
> false-positive diagnosis, interpreting a test with high specificity will
> maximize patient benefit. In contrast, when a disease is common and there
> are substantial costs for a false-negative diagnosis, interpretation at
> high sensitivity will maximize utility."
>
> "Our prior meta-analysis indicated that radiologists interpret CT scans
> with approximately equal sensitivity and specificity. The current analysis
> indicates that this is an appropriate threshold at the intermediate
> probability of disease at which CT is commonly used today. If CT is to be
> used in populations at higher or lower probabilities of disease, then
> different imaging thresholds will be appropriate."
>
>
>
> On 15 May 2018 at 13:18, Jain, Bimal P.,M.D. <BJAIN at partners.org> wrote:
>
> In this attached paper, I discuss that the prescribed Bayesian method is
> not employed for diagnosis in practice because probability of a diseases is
> considered evidence for it in a given patient in this method, which is
> incorrect as a probability is a frequency in a population. This leads to
> all sorts of errors in practice which I discuss.
>
> The correct method of diagnosis, which is employed in practice, consists
> of hypothesis generation and verification in which evidence is assessed by
> a likelihood ratio which locates it in the given patient of interest.
>
> Please review and comment on this paper.
>
>
>
> Thanks
>
>
>
> Bimal
>
>
>
> Bimal P Jain MD
>
> Northshore Medical Center
>
> Lynn MA 01904.
>
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> <Signal Detection Theory.pdf>
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Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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