[EXTERNAL] Re: [IMPROVEDX] Why is the Bayesian method not employed for diagnosis in practice

Edward Hoffer ehoffer at GMAIL.COM
Thu May 31 20:50:17 UTC 2018


Bob said: "We need to think of three ranked possible diagnoses or even more
with a given history, signs and symptoms. These to be routinely modified as
the work-up continues.
Cannot technology help us here, or does this already exist?"
It appears that Jason Maude, Art Papier and I need to be louder.  Yes, the
technology does exist, in the form of computer-based diagnostic decision
support systems, that have been proven in multiple trials to improve
doctors' differential.  It is just a question of increasing awareness and
use!
Ed
Edward P Hoffer MD

On Thu, May 31, 2018 at 1:43 PM, ROBERT M BELL <
0000000296e45ec4-dmarc-request at list.improvediagnosis.org> wrote:

> With Tom’s frequency comments about diagnosis and questions such as, "what
> else could it be” from patients, do we not, at the outset, need to think of
> three ranked possible diagnoses or even more with a given history, signs
> and symptoms. These to be routinely modified as the work-up continues.
>
> Cannot technology help us here, or does this already exist?
>
> Rob Bell
> > On May 31, 2018, at 4:22 AM, Tibbits, Paul A. <Paul.Tibbits at VA.GOV>
> wrote:
> >
> > Tom,
> > What wiki entry? Is there a link? Tnx. PT
> >
> >
> >
> > Sent with Good (www.good.com)
> >
> > ________________________________
> > From: Tom Benzoni
> > Sent: Wednesday, May 30, 2018 1:08:48 PM
> > To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> > Subject: [EXTERNAL] Re: [IMPROVEDX] Why is the Bayesian method not
> employed for diagnosis in practice
> >
> > I think there may be sone fundamental misunderstandings of Bayes'
> theorem and its application.
> > The Wiki entry on the topic is quite good.
> > Tom
> >
> > On Wednesday, May 30, 2018, Jain, Bimal P.,M.D. <BJAIN at partners.org
> <mailto:BJAIN at partners.org>> wrote:
> > 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<mailto:pheski69 at GMAIL.COM>]
> > Sent: Sunday, May 27, 2018 12:12 PM
> > To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IM
> PROVEDX 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<mailto: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<mailto:
> james.oldham at HEALTH.NSW.GOV.AU>>
> > Sent: Sunday, May 27, 2018 1:49 AM
> > To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IM
> PROVEDX 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<mailto:
> 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<mailto:
> 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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Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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