stroke misdiagnosis disproportionate in the young says Washington Post

Doctor Will dr.will at FUSE.NET
Wed Jul 9 15:22:01 UTC 2014


THANK YOU BOB!
The problem using EMR systems designed for billing, NOT quality or knowledge
management.

So when do we physicians and nurses say “enough is enough”! “Doing what is
right” is difficult, but we need to for the patient’s sake, not the billing
department.

 

Dr.Will Sawyer

513-769-4951(O)

 

  _____  

From: Swerlick, Robert A [mailto:rswerli at EMORY.EDU] 
Sent: Tuesday, July 08, 2014 11:37 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Here lies the crux of the problem. Our tools provide probabilities but we
operate in a world where our jobs are defined by yes and no, embedded in a
workflow with increasing production pressures. If one attempts to use tests
without an appreciation of their probabilistic nature, it increases the
likelihood of diagnostic error. If one attempts to operate using
probabilistic tools, it becomes increasingly difficult to function in a
world which demands yes or no answers. 

 

Bob Swerlick

 

From: Ely, John [mailto:john-ely at UIOWA.EDU] 
Sent: Tuesday, July 08, 2014 8:35 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Thanks David and Bimal.  Lots of great information in your emails.

 

from  the primary care perspective, the question is not whether to cath the
65 year-old man, but whether to admit him.  He would definitely be admitted,
even if his ECG was normal.  We don’t use Bayesian reasoning at the bedside
because

 

1.  We don’t know the pretest probability for the individual patient, and
even if we did, the post test probability is just a probability, not a yes
or no.  Our job is yes or no.

2.  Other factors are not captured by Bayesian calculations (consequences of
missing the diagnosis, malpractice worries, patient expectations, recency of
most recent disaster, rationalizations based on fatigue, closeness to
quitting time, pressure from office staff; i.e., things we never talk about
and therefore never understand.)  Good article on this in JAMA (Kowey PR. A
piece of my mind:  the silent majority. JAMA. 2011;306(1):18-9.)

 

John Ely

 

From: David Newman-Toker [mailto:toker at JHU.EDU] 
Sent: Monday, July 07, 2014 3:23 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Well, Bimal, there are certainly test results (if not tests) that are
‘diagnostic’, largely independent of pre-test probability. For instance, if
you get an MRI brain on a patient and it shows a gigantic enhancing lesion
with mass effect, you know a few things, independent of whether the patient
is asymptomatic or symptomatic, and whether symptoms cohere (headache) or
not (toe pain) (i.e., independent of factors that affect pre-test
probability). Merely looking at the scan (and assuming it belongs to the
patient in question), knowing nothing else about the patient, you know


 

a)      the patient has a brain [LR roughly infinite]

b)      the brain is not normal [LR roughly infinite]

c)      there is a lesion that indicates a disease [LR roughly infinite]

d)      that disease is probably a brain tumor (but might be something else
– e.g., stroke, demyelination, infection) [LR probably 10-30 for brain
tumor, depending on the precise radiographic appearance]

e)      the patient probably has symptoms related to the lesion location in
the brain [LR probably 2-5]

 

Let’s take your low prior probability STEMI case and assume, for the sake of
argument, that you won’t go to the cath lab if your expectation is <10% the
patient has an MI, but will >=10%. I don’t know enough about the ECG
findings you describe, but if they never occur in patients who lack MI
(i.e., 100% specificity), then their PLR (=sensitivity / [1-specificity]) is
infinite. For findings with infinite LRs, pre-test probability is irrelevant
--- your post-test probability with a positive test will always be greater
than 10% (and always ~100%). You say, however, that the PLR is only 13. If
the LR+ is only 13 (i.e., specificity is <100%), then the pre-test
probability matters if it is below ~0.9%. Below that, the ECG findings you
describe (if they really have a PLR of 13), only raise your post-test
probability to a max of <10%. It may be that you would never order an ECG in
someone with a pre-test probability <1%, but you still have to make some
judgment about whether to do the ECG at all (based on a tacit prior
probability estimate).

 

This is all by way of saying that you have constructed the problem space in
such a way that Bayesian calculations are unnecessary --- if you only ever
consider test results with PLR>10 and NLR<0.1 (i.e., you ignore completely
anything less than a ‘slam dunk’ finding); and, on the PLR side, if you only
apply the test in patients with a pre-test probability >1% and have a
threshold for action of >10%, then residual judgments about pre-test
probability in patients with positive test results will never be relevant
(you still have to judge pre-test probability to be >1%... which you do
without much mathematical reasoning using simple heuristics). You have
chosen examples of test results and clinical problems that are relatively
straightforward and result in simple, go/no-go, binary decision rules based
on the presence or absence of specific results (slam dunk STEMI by ECG) of a
particular test appropriate to a particular type of patient. Within this
frame, you are correct --- pre-test probability is not really relevant
 but
only because the problem is constructed that way, not because there aren’t
tacit probabilistic decisions built into the decision-making.

 

I am less clear on your 65yo with multiple risk factors and typical chest
pain (LR ~113 for coronary atherosclerosis) and a pre-test probability of
90% (maybe closer to 99%?). If the pre-test probability is actually 90%,
then (a) you shouldn’t need to order an EKG --- you should just take him to
the cath lab [if the 90% probability is for the type of MI for which
appropriate treatment is the cath lab]; and (b) you shouldn’t let the
non-specific T wave changes dissuade you (both because of the high pre-test
probability in the patient and, by your own admonition/logic not to bother
with results that have NLR > 0.1, which I assume is the case for nonspecific
T wave changes). If you really wouldn’t treat this patient as acute MI, then
there is something you’re not telling us in your 90% estimate (e.g., 90% for
any MI, but only 20% for a STEMI or cath lab-appropriate MI).

 

As for the issue of how doctors think
 in my opinion, you are correct ---
many physicians do not understand probability or test results interpretation
--- they often think in absolutes, rather than in uncertainties. So they
assume, for many binary tests, that a negative test means the disease is
absent and a positive test means it is present. NPV and PPV, as we all know,
however, are both usually <100%, because tests are generally imperfect. 

 

Best,


David

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


Confidentiality Notice: The information contained in this email is intended
for the confidential use of the above named recipient. If the reader of this
message is not the intended recipient or person responsible for delivering
it to the intended recipient, you are hereby notified that you have received
this communication in error, and that any review, dissemination,
distribution, or copying of this communication is strictly prohibited. If
you have received this in error, please notify the sender immediately by
telephone at the number set forth above and destroy this email message.
Thank you.

 

From: Jain, Bimal P.,M.D. [mailto:BJAIN at PARTNERS.ORG] 
Sent: Monday, July 07, 2014 12:23 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Dear John,David,

 

Thank you both for your insightful comments.

 

1.       I am neither a frequentist nor a Bayesian exclusively. I consider
them both as valid interpretations of probability as they both fulfill the
rules of probability calculus.

2.       My main interest is in studying clinical diagnosis as it is done in
actual practice as this will make us appreciate real life constraints which
modify the method we use.

3.       I agree , a Bayesian approach, on face value, appears reasonable
and is elegant, but the fact remains, it is only an application of a
mathematical theorem which does not take account of some real lfe
constraints on diagnosis as I discuss below.

4.       It is not surprising, therefore, it is not employed in actual
practice as seen from following examples. Even though I am not a
cardiologist, all my examples are from cardiology as they best serve my
purpose. Please excuse me , John, if I have made errors in my interpretation
of how diagnosis is actually performed in these patients.

5.       The first example is of an actual patient, who was discussed in a
clinical problem solving exercise (Pauker, NEJM 1992).  A 40 year old,
healthy woman, without any any cardiac risk factor presents with highly
atypical chest pain and is found to have acute Q wave and ST elevation EKG
changes (acute EKG changes). The pretest probability of acute myocardial
infarction (MI) is estimated to be 7 percent. It is combined with the known
likelihood ratio of acute EKG changes of 13 by Bayes’ theorem to generate a
post test probability of 50 percent. It seems to me the Bayesian diagnosis
from this post test probability should be of acute MI being indeterminate in
this patient. But the discussing physician diagnosed acute MI with near
certainty from acute EKG changes alone which he considered to be strong
evidence for it. Clearly, he did not diagnose in a Bayesian manner.

6.       Let us consider another patient, a 65 year old man with multiple
cardiac risk factors who presents with highly typical chest pain. His EKG
reveals non specific T wave changes. The pretest probability of acute MI is
very high, let us say , 90 percent while the LR of nonspecific T wave
changes is about 1. By combining them we get a post test probability of 90
percent, from which the Bayesian diagnosis would be of near certain presence
of acute MI in this patient. I doubt however if this diagnosis would be made
in actual practice.

7.       EKG reading physicians routinely diagnose acute MI definitively
from acute EKG changes alone without knowledge of clinical presentations in
these patients.

8.       All patients with acute EKG changes seen in ER are diagnosed to
have STEMI and sent to cardiac cath. Lab regardless of pretest
probabilities.

9.       In all these examples,diagnosis is performed in a non-Bayesian
manner because, I suggest it accords with our experience. Thus in the 40
year old woman, we would need to have encountered a number of patients like
her with acute EKG changes to experience a frequency of acute MI of 50
percent tin these patients which would validate our indterminate diagnosis
of acute MI in the given patient. It is well known from experience we are
not likely to encounter one, let alone a number of such patients. On the
other hand, we would have encountered a number of patients with varying
pretest probabilities in whom acute EKG changes diagnosed acute MI correctly
(Rude, Am J Card 1983). It is this experience , I suggest, the dicussing
physician calls upon to diagnose acute MI with near certainty.

10.   In the 65 year old man, our experience of non specific T wave changes
in other patients leads us not to diagnose acute MI definitively in the
given patient even though the psot test probability is very high.

11.   I believe it is the wide variation in pretest probability of a given
disease in different patients which prevents us from having experience which
would justify a Bayesian diagnosis in a given , individual patient.

12.   I think, we need to observe, analyse and experiment with clinical
diagnosis in actual practice to learn how it is actually performed which may
be quite different from our theoretical preconceptios, however reasonable
they may appear.

13.   An example from history of science is quite instructive in this
regard. The entirely reasonable appearing beliefs that heavy bdies fall
faster than lighter ones and that every motion requires a mover or force
were held for a long time.. Both these beliefs were shown to be erroneous
when Galileo observed, analysed and experimented on actual motion of bodies.

 

Thank you all for amost enjoyable and important discussion on clinical
diagnosis.

 

Bimal

 

Pulmonary-Critical Care

NorthShore Medical Center

Lynn, MA 01904

 

 

From: John Brush [mailto:jebrush at ME.COM] 
Sent: Friday, July 04, 2014 10:10 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

I’ll wander back into this discussion because I can’t help myself. I think
we established in a previous email exchange that Bimal is a frequentist and
I am a Bayesian. 

                The essence of our dilemma is: “How do we apply statistics
regarding the operating characteristics of tests and regarding experience
from populations of patients to the care of an individual patient?” In the
emergency room, we generally get one shot at making a correct diagnosis. How
do we apply our tools and experience to give that encounter the best chance
of success?

                As we all know, the frequentist argues that the notion of
probability can only be applied to populations of patients. The frequency
notion of probability describes the rate at which something occurs over the
long run. The personal notion of probability is different in that it gives
us a way to think about a single patient or event. The personal notion of
probability is our degree of belief in some proposition (such as a
diagnostic category). It is a way to quantify whether we feel hot or cold
about some idea. For conditional probability, the Bayesian approach does
require that we assign an estimation of prior probability. 

                As Bimal points out, the frequentist argues that assigning a
prior probability is too subjective, and therefore, when used to calculate a
posterior probability, the whole thing falls apart. But the frequentist asks
you to make other assumptions that are just as conceptually difficult. The
frequentist asks you to imagine having your situation repeat itself and it
asks you to imagine, if the situation is repeated, how something would turn
out over the long run. For a diagnosis, which is a categorical variable, you
would have to imagine taking repeated samples from an imaginary population
to create an imaginary binary distribution that would describe how things
would turn out over the long run. To me, that is more of a stretch in
thinking than the notion of a subjective prior probability.

                I think it is more reasonable to try, based on available
information and intuition, to assign a point estimate of prior probability.
Granted, there is ambiguity around that estimate, so you could imagine a
distribution of prior probability estimates. If you know very little about
the patient, the distribution would be broad. If you have detailed
information, the distribution would be narrow around a specific point
estimate of prior probability. To be clear, I am describing a conceptual
distribution of probability estimates, not a distribution of rates from
imaginary repeated samples.

                The test, itself, is not perfect. We have sensitivity and
specificity to estimate how imperfect a test is, but there is ambiguity
around those estimates as well, so that the predictive value of a test
result should be viewed as a distribution that can have a variable effect on
our prior probability. 

                The Bayesians describe a “triplot,” which gives a visual
demonstration of how a prior probability (which is a distribution) is
changed by a test result (which is also a distribution) and the combination
of the two distributions yield a third distribution that visually represents
the posterior probability. Generally, the additional new information will
reduce ambiguity, so the shape of the posterior probability distribution
curve is typically more narrow than the shape of the prior probability
distribution curve, and the point estimate of probability is moved in either
direction, depending on whether the test result is positive or negative. I’m
not a statistician, but to me, the concept of a triplot gives me a way to
visualize the ambiguity around the probability estimates, and how the prior
probability and new information distributions combine to yield a posterior
probability distribution.

                Given the difficulty in precisely calculating this real time
in the real world, we “satisfice” to use Herbert Simon’s word. We use the
anchoring and adjusting heuristic to estimate the prior probability and
“calculate”  the posterior probability. We generally have some idea in our
heads of the point estimates and the ambiguity around those point estimates.
The thoughtful clinician is constantly thinking “What do I think, and am I
sure?"  The reason that this works in practice as well as it does is that
the estimates don’t have to be exact. We just need to know whether the
posterior probability estimate is on one side or the other of some
threshold.

                The challenge, in my mind, is to teach trainees these
concepts, and to assure that we all do this consistently, reliably, and most
effectively. How can we make our subjective estimates as objective as
possible, and how can we do this consistently? In some cases, the situation
may be simple enough or the test may be effective enough that you can
automate this with a computer to get maximum reliability. In most
situations, however, there is too much uncertainty, and we will have to rely
on the human mind and on intuition to make the necessary associations and
estimations, using heuristics. 

John

 

John E. Brush, Jr., M.D., FACC

Professor of Medicine

Eastern Virginia Medical School

Sentara Cardiology Specialists

844 Kempsville Road, Suite 204

Norfolk, VA 23502

757-261-0700

Cell: 757-477-1990

jebrush at me.com

 

 

 

On Jul 3, 2014, at 1:50 PM, David Newman-Toker <toker at JHU.EDU> wrote:

 

I’ve re-engineered the trail below to try to restore it to ‘togetherness’,
because I messed it up before (trying to keep it together)! :-)

 

Dear Bimal,

 

Thanks for sharing your interesting take on diagnosis. If I understand
correctly, you have squarely placed us into the ‘frequentist’ vs. ‘Bayesian’
statistical inference space
 a space that may be unfamiliar to many of our
ListServ readers. I commend to those unfamiliar two classic papers on
inference related to this issue written by Steve Goodman, who is one of the
most rigorous thinkers in the world on these issues:

 

Goodman SN. Toward evidence-based medical statistics. 1: The P value
fallacy. 
Ann Intern Med. 1999 Jun 15;130(12):995-1004. PubMed PMID: 10383371.

http://www.ncbi.nlm.nih.gov/pubmed/10383371

http://www.google.ae/url?sa=t
<http://www.google.ae/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=3&cad=rja
&uact=8&ved=0CDAQFjAC&url=http%3A%2F%2Fwww.perfendo.org%2Fdocs%2FBayesProbab
ility%2F5.3_GoodmanAnnIntMed99all.pdf&ei=h4y1U-ycIqef7AaO34HIDg&usg=AFQjCNEG
g_9Ue0WCuA9Lnoft9RwjIxDNdA&bvm=bv.70138588,d.bGE>
&rct=j&q=&esrc=s&frm=1&source=web&cd=3&cad=rja&uact=8&ved=0CDAQFjAC&url=http
%3A%2F%2Fwww.perfendo.org%2Fdocs%2FBayesProbability%2F5.3_GoodmanAnnIntMed99
all.pdf&ei=h4y1U-ycIqef7AaO34HIDg&usg=AFQjCNEGg_9Ue0WCuA9Lnoft9RwjIxDNdA&bvm
=bv.70138588,d.bGE

 

Goodman SN. Toward evidence-based medical statistics. 2: The Bayes factor.
Ann

Intern Med. 1999 Jun 15;130(12):1005-13. PubMed PMID: 10383350.

http://www.ncbi.nlm.nih.gov/pubmed/10383350

 

The first of these two articles outlines the problems inherent in trying to
make individual-case decisions about probability (short-run inference) based
purely on observed long-run data (long-run inference). The paper does so in
regard to assessing the strength of evidence from research studies, but the
argument is conceptually the same as that which would be applied to
short-run vs. long-run inferences in diagnosis.

 

As I understand the Neyman Pearson hypothesis test and your argument below,
your long-run data about EKGs for diagnosis of MI in chest pain patients
would support rejecting the (long-run) null hypothesis that EKG findings do
not predict MI, independent of any specific patient characteristics
 i.e.,
EKGs probably do predict MI, on average, most of the time, if we do them
repeatedly in a lot of patients over time. 

 

But, despite the appeal of using this approach to avoid the messy business
of assigning pre-test probabilities, one cannot meaningfully convert that
long-run inference into a specific short-run inference about an individual
patient whom you are trying to diagnose, without taking into account
pre-test probability for that specific patient (i.e., prevalence, judgments
about the extent of match between that single patient and the previous
patients from your long-run EKG/MI experience). You do that tacitly already
in your argument below by restricting discussion to patients with ‘chest
pain’
 but, by that I assume you mean ‘chief complaint chest pain presenting
for medical care’ rather than ‘incidental minor chest pain’ in a patient who
suffered trauma and has a broken leg or in a patient who just ate a spicy
meal and doesn’t seek medical care. In other words, the EKG PPV is 95% (or
whatever the numbers are) precisely because your patients have certain
characteristics that put their pre-test probability into the relevant
operating range for a test of sufficient LR (your >10 rule of thumb) to make
the correct diagnosis, rather than yield a false positive --- i.e., you
already know SOMETHING about pre-test probability. Same, presumably, in the
reverse, for ruling out a disease (with an NLR <0.1), rather than ruling it
in.

 

So you can use Neyman Pearson to have a high probability of being right, on
average, across patients, but not to have a high probability of being right
for the patient in front of you
 for that, you need Bayes theorem (i.e.,
pre-test probability plus likelihood ratios to derive post-test
probability). Neyman Pearson can help us favor one approach over another
(e.g., EKG >> reading sheep entrails for MI diagnosis), but it can’t tell us
whether a particular patient likely has a particular diagnosis (e.g., MI)
when only considered in a vacuum (i.e., without pre-test probability
estimates).

 

A more nuanced framing of your argument might be that we need not obsess
about assigning a highly specific pre-test probability (e.g., 10% vs. 20%)
if we already have sufficient prior knowledge of pre-test probability to
know that we are in the ‘sweet spot’ operating range where our test will
help us diagnostically. This is the ‘thresholds’ argument in my prior email
in this trail below, so I won’t reiterate here.

 

At least that’s my understanding.


Best,


David

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


Confidentiality Notice: The information contained in this email is intended
for the confidential use of the above named recipient. If the reader of this
message is not the intended recipient or person responsible for delivering
it to the intended recipient, you are hereby notified that you have received
this communication in error, and that any review, dissemination,
distribution, or copying of this communication is strictly prohibited. If
you have received this in error, please notify the sender immediately by
telephone at the number set forth above and destroy this email message.
Thank you.

 

 

From: Jain, Bimal P.,M.D. [mailto:BJAIN at PARTNERS.ORG] 
Sent: Wednesday, July 02, 2014 8:53 AM
To: 'Society to Improve Diagnosis in Medicine'; David Newman-Toker
Subject: RE: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

 

Dear all,

 

I present below a realistic model of clinical diagnosis.

There are three features that any realistic model, that is, a model which
explains diagnosis as it is performed in real life needs to take into
account.. These three features are:

(a)    The clinical aim of diagnosing a disease correctly in each individual
patient.

(b)    The wide range of clinical presentations and therefore of pretest
probabilities of a given disease in different patients.

(c)    The validation of a diagnosis in a given patient by our experience.

 

1.       In developing this model, let us consider one particular disease,
acute myocardial infarction(MI) It is well known its presentations and
therefore its pretest probabilities , vary widely from characteristic chest
pain in a middlle aged man with multiple cardiac risk factors ( high PTP,
say around 80-90 percent) to uncharacteristic chest pain in a healthy 40
year woman with no cardiac risk factor (low PTP,7 percent) (Pauker, NEJM
1992).

2.       Let us now take the group of all patients with varying clinical
presentations and therefore varying PTPs in whom acute MI could be possibly
present. Each patient in this group can be looked upon, I suggest, as being
drawn from a hypothetical, infinite population in which the distribution or
frequency of acute MI corresponds to the pretest probability in the patient.

3.       The group of patients in whom we would suspect acute MI constitutes
therefore a heterogenous group with varying PTPs. I would like to point out
our experience of diagnosing acute MI would be gained solely from this
group.

4.       Our aim clinically is to diagnose acute MI if it is present
correctly in each individual patient. We find knowing the PTP in a given
patient does not help much as the given patient has been drawn from the
corresponding infinite population purely by chance. This is true whether PTP
is high or low. Therefore all we can do is to suspect acute MI in the given
patient from the presentation.

5.       The next step is to determine if acute MI is present or not, in our
given patient.

6.       For this purpose, we perform a test an EKG in our patient. Let us
suppose we observe acute Q wave and ST elevation changes (acute EKG
changes).

7.       We diagnose acute MI definitively from acute EKG changes which have
a LR of 13

8.       If we diagnose acute MI definitively from acute EKG changes
repeatedly in patients in our heterogenous group, we shall diagnose
correctly in 90 percent patients (Rude, Am J Card. 1983)

9.       The definitive diagnosis of acute MI from acute EKG changes in our
particular patient is validated therefore by our experience.

10.   The argument employed here is not probabilistic(Bayesian) as PTP does
not play any role in diagnosis of acute MI from acute EKG changes.

11.   Instead, the argument employed, I suggest, is Neyman’s confidence
argument.

12.   In the confidence argument, as is well known, a test result (usually
with values in an interval called confidence interval) is repeatedly sampled
from a heterogeneous population of patients with a certain disease with
varying PTPs. it diagnoses the disease correctly in about 95 percent
patients.

13.   An example of a confidence argument is employment of PSA level in
interval 0-4 which correctly diagnoses absence of prostate CA in 95 percent
persons in a heterogenous population.

14.   In clinical diagnosis, a confidence argument is modified slightly, in
that a test result like acute EKG changes may be a point(present or absent)
instead of an interval and the accuracy rate may be other than 95 percent.

15.   In clinical diagnosis it is customary to employ a test result with LR
of 10 or higher(Jaeschke 2002)  for definitive diagnosis of a disease as it
leads to about 90 percent accuracy in a heterogenous population. Thus LR of
acute EKG CHANGES IS 13, LR of positive chest CT angiogram for pulmonary
embolism is 21, LR of positive venous ultrasound for DVT is 19.

16.   An essential feature of the confidence argument is that PTP does not
play any role in it.

17.   That it is emplyed in actual practice is clearly seen from the
following examples:

18.   (a) EKG reading physicians  diagnose acute MI with near certainty from
acute EKG changes alone. Similarly, radiologists diagnose pulmonary embolism
from positive chest CT angiogram alone.

19.   (b)Acute MI was diagnosed with near certainty from acute EKG changes
alone in the 40 year old woman with uncharacteristic chest pain mentioned
above(Pauker 1992)

20.   In conclusion, I suggest, clinical diagnosis is performed by
suspecting a disease from a presentation in a given patient and the
suspected disease is diagnosed definitively from a test result with LR of 10
or higher by employing a confidence argument.

 

Please review and comment. Thanks

 

Bimal P Jain MD

Northshore medical center

Pulmonary-Critical Care

Lynn MA 01904

 

 

 

 

From: David Newman-Toker [mailto:toker at JHU.EDU] 
Sent: Thursday, June 26, 2014 4:11 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

 

I think there are some critically important concepts in this trail. I’ll
offer my take on a few without trying to address every point.

 

Note that I wrote this email before Frank’s email came through, but I pasted
here after his to keep the trail in one piece. I think Frank and I probably
agree on most of this
 since it looks like we independently came up with
several of the same arguments. 

 

1)      HISTORY AND EXAM AS TESTS: we tend to talk about ‘pre-test’
probability before a laboratory-based or imaging diagnostic test, but all
elements of history and exam are also ‘tests’ (just often with poorly
studied sensitivity and specificity); this means that every piece of
information about vascular risk factors, age, symptom particulars, etc. has
an impact in shifting pre- to post-test probability
 it is not enough just
to stop at the population prevalence of a disease and call that pre-test
probability
 it is highly case specific --- which is why correct case
representation/problem formulation can so powerfully influence disease
probability estimates (Dr. Zamir’s point)

 

2)      THRESHOLDS FOR ACTION: it is important to separate discussions about
disease probability from discussions about what probabilities are low enough
to assume (i.e., act as if) the patient does not have the target disorder
(sometimes called threshold decision-making); diagnostic tests are only
useful for patients whose pretest probability is between one of two
boundaries --- at the low end, the ‘testing threshold’ (below which the
disease probability is so low that the test, of a fixed ‘rule in power’
[i.e., positive likelihood ratio] could not hope to increase the post-test
probability above a the level where treatment would be indicated) and, at
the high end, the ‘test-treatment threshold’ (above which the disease
probability is so high that the test, of a fixed ‘rule out power’ [i.e.,
negative likelihood ratio] could not hope to decrease the post-test
probability below a level where treatment would no longer be indicated)
(Pauker & Kassirer NEJM 1980) --- the importance of powerful tests (Dr.
Jain’s point about PLR >10 and NLR<0.1) is that they more often help us to
cross these thresholds
 but there is no specific level at which tests are
uniformly helpful or unhelpful, because it depends on pre-test probability
and thresholds for a specific symptom-disease-diagnostic test combination;
if you have a good enough test, the operating range may be quite wide ---
the NLR on HINTS is about 0.01 and PLR is about 25 in acute, continuous
dizziness, so it might be able to meaningfully influence your decision
making about stroke with a pre-test probability for stroke anywhere between
about 0.1% and 50%, depending on the patient’s personal preferences

 

3)      VALUE JUDGMENTS AND SHARED DECISION MAKING: threshold decisions are
‘value laden’ and should be based upon some assessment of the risks and
benefits of subsequent action (further testing or treatment) with an
emphasis on the patient’s personal valuation of various options and outcomes
(i.e., shared decision-making); there is no absolute link between disease
probability and action at some arbitrary, fixed threshold --- one patient
might be unhappy risking a 0.1% chance of missed stroke, while another might
be comfortable with a 10% risk of a missed stroke, based solely on personal
preference; the risks of treatment or harm from the disease and its long
term consequences might differ dramatically across patients --- e.g., a 30
year old in previously good health vs. a 99 year old with end stage cancer,
sepsis, and multi-organ failure; if society deemed that diagnostic tests
likely to produce ‘cost-effective’ benefits of resulting downstream
treatment (e.g., <$50-100K/QALY) should routinely be implemented, it might
mean that for some disease scenarios the testing threshold was at a pre-test
probability of 0.1% while for another it was at 1% and yet another it was at
10%; and so on
 

 

4)      CASE ATYPIA: as Pat Croskerry pointed out, atypical cases are
disproportionately associated with diagnostic error; in some cases, this
represents a low pre-test probability; in other cases, it represents a
failure of our existing mental models (symptom/illness scripts, heuristics)
to more accurately represent the problem and the spectrum of cases/causes,
including our awareness of findings that should (but don’t necessarily for
all clinicians --- Dr. Zamir’s point about superior diagnosticians with
superior mental models) properly influence our pretest probability (Dr.
Jain’s point about decision rules); sometimes, it is an amalgam of these
things --- for instance, the pre-test probability of stroke in an ED patient
with dizziness or vertigo as a chief complaint (given no other information)
is 3-5%; many people would consider this a ‘low’ pre-test probability, but
(unknown to most clinicians) if the patient has acute, continuous dizziness
or vertigo, the pre-test probability is ~25%... which most people would
consider a relatively high pre-test probability for a dangerous disorder
 so
the structure of the problem formulation (and the diagnostic expertise and
experience of the physician) can have a profound effect on a clinician’s
estimate of pre-test probability; it is also atypical for strokes to present
with dizziness or vertigo as a chief complaint (no more than 10-20% of
strokes present this way, and it is probably closer to 5-10% that do) --- a
typical mental model of stroke is that of a patient with a ‘hemi’-deficit
(weakness or numbness) or aphasia (i.e., a typical middle cerebral artery
territory syndrome), but most patients who present with dizziness or vertigo
as the lead symptom of stroke (i.e., a typical posterior circulation
syndrome) do not have such deficits (no more than 20% have any lateralizing
signs typically thought of as ‘focal’ neurologic deficits); another typical
heuristic for stroke is that patients are older and/or have vascular risk
factors
 but a large fraction of those presenting dizziness or vertigo have
vertebral artery dissections as the cause, and these patients are typically
younger and lack vascular risk factors; so dizzy strokes are atypical on
most counts, but not always low pre-test probability

 

5)      ROLES OF META-COGNITION, EDUCATION, AND DECISION SUPPORT TOOLS:
meta-cognition alone will probably not help us when our mental models fail
us and we underestimate disease probability based on how atypical a case
appears to be (even though someone more expert or experienced would more
accurately identify a higher disease probability); education in diagnostic
skills could certainly help, but it will require a commitment to educate
based on repetitive case examples that are progressively more “atypical” ---
i.e., we can’t just teach from “classic” cases and expect learners to be
able to identify atypical forms (experts become experts through extensive
experience with the boundaries around each condition, rather than just the
‘sweet spot’ of the ‘obvious’ case; decision support tools can help us
 but
probably only if we commit to use them routinely under pre-specified
circumstances --- required checklists, pathways, (stroke) risk calculators,
etc. for all patients with a particular symptom or problem (e.g., dizziness
or vertigo in the ED)
 rather than choosing to use them only if the case
seems representative enough for us to think of stroke in the first place 

 

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


Confidentiality Notice: The information contained in this email is intended
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Thank you.

 

From: Papa, Frank [mailto:Frank.Papa at unthsc.edu] 
Sent: Thursday, June 26, 2014 4:03 PM
To: Society to Improve Diagnosis in Medicine; David Newman-Toker
Subject: RE: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

 

A couple of comments regarding recent considerations offered by Drs. Jain,
Zamir and Newman-Toker 
.

 

Regarding Dr Jain’s suggestion that the rates of diagnostic error in the
PIOPED study, the Newman-Toker study, and the broader error estimates
produced by Dr Graber may be a reflection of a normalized distribution of
difficult to easy to diagnose cases – with erroneously diagnosed cases
representing those with  low,  objectively determined probability estimates

 

 

I believe that his suggestion that such objective probabilistic estimates
that a given case (with its particular set of signs and symptoms) is indeed
a representation of disease ‘x’, might be correlated with a clinician’s
subjective, pretest estimate (of the same case) as representing a low,
intermediate, high ‘match’ with disease ‘x’, is both insightful and
informative. While I am not aware of any research and evidence in support of
his hypothesis, awareness of prior, and the initiation of new research
involving these issues, would be very useful for those interested in further
understanding the factors underlying DDX error and accuracy.

 

Regarding Dr Zamir’s comments that physicians can vary widely in their
respective subjective, pretest estimate of the probability that a given case
is disease ’x’, I believe that he is probably right when one physician’s
area of specialization is different from another physician’s. However, I
would suspect that physicians sharing the same area of specialization are
more likely to provide convergent rather than divergent pretest estimates
that a given patient with a given set of signs and symptoms is a
representation of disease ’x’.  

 

I’d like to add to this discussion the fact that cognitive models of the
factors contributing to diagnostic accuracy/error have been used to explore
the relationship between a given case’s ‘typicality’ and the probability
that a given case will be correctly diagnosed. The findings in this area of
research have demonstrated that diagnostic performance (accuracy) is a
function of a case’s typicality. That is, the more closely a given case both
approximates the prototypical portrayal of the disease for which it is a
representation, and, the degree to which the findings in the case at hand
make it distinguishable from the closest competing disease’s prototype, the
more likely that case will be correctly diagnosed. 

 

Research directed at exploring possible relationships/correlations between:
1) objective, probabilistic estimates that a given case is a representation
of a given disease class, 2) estimates of the degree to which that same case
is a ‘typical’ representation of a given disease class, 3) subjective
physician estimates (low, intermediate, high) that the same case is a
representation of a given disease class, and 4) the diagnostic performance
(accuracy) of a cadre of physicians against that same case (or set of
cases), would be a very rich and potentially useful area for DDX research.

 

Regarding comments form Dr Newman-Toker’s and others interested in
problem-specific workups and tools. If indeed diagnostic accuracy is a
function of a case’s typicality (as suggested via cognitive sciences
models), and, if typicality is expressible in terms of the degree to which a
case with its particular constellation of signs and symptoms both
approximates the prototypical portrayal of the disease for which it is a
representation, and, the degree to which the findings in the case at hand
make it distinguishable from the closest competing disease’s prototype, then
it makes lots of sense to adopt decision support tools that ensure the
pursuit and collection of those signs and symptoms associated with each of
the common and important differentials for the problem at hand (i.e.,
problem-specific data gathering protocols). Such findings, in conjunction
with objective and subjective information processing mechanisms (such as via
artificial intelligence tools, neural nets, Bayesian and other cognitive
science-based inferencing tools) would offer the clinician a variety of
perspectives with which a leading, and list of rank-ordered alternative
diagnosis could be offered at the bedside.

 

Attached is an article for those interested in a cognitive sciences based
exploration of the relationship between case typicality and diagnostic
performance.

 

Frank

 

 

Frank J Papa, DO, PhD

Professor, Medical Education and Emergency Medicine

Director, TCOM Academy of Medical Educators

Associate Dean, Curricular Design and Faculty Development

University of North Texas Health Science Center

 

From: Benbassat Jochanan [mailto:benbasat at JDC.ORG] 
Sent: Thursday, June 26, 2014 1:05 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Dear Ehud, 

 

How then do you propose identifying a competent diagnostician for whom every
fever is of known origin?

 

Jochanan Benbassat MD

  _____  

From: Ehud Zamir [ezamir at UNIMELB.EDU.AU]
Sent: Thursday, June 26, 2014 10:43
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

Dear Dr Jain 

Pre test probability is determined subjectively by the doctor. What
constitutes high pretest probability for one doctor with a high index of
suspicion for a condition will be judged as low pretest probability by
another. Therefore I would suggest that clinical competence and diagnostic
skill are the solution, rather than over investigation of patients with low
pretest probability. We should bear in mind that in the face of truly low
pretest probability, even positive results do not push the post test
probability very far, unless the test is diagnostic by itself. So I am not
sure I agree with your statement that "An incresed awareness thatn a
substantial proportion of patients with a given disease, about 10-15
percent, encountered by us  is likely to have low pretest probability."
Perhaps the fact that these 15% are CONSIDERED low test probability is
simply the root cause of the diagnostic error. It could be argued that a
more competent diagnostician would not have regarded these as low PTP, and
that the more competent the diagnostician, the more likely their "low
pretest probability" judgement is to be a true negative. 

It reminds me of the "fever of unknown origin" issue, to which my Professor
of Medicine in medical school used to refer to by asking "unknown to
whom?"...

Regards

Ehud Zamir

 

 

  _____  

From: Jain, Bimal P.,M.D. [BJAIN at PARTNERS.ORG]
Sent: Wednesday, 25 June 2014 9:58 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

Dear Drs. Newman-Toker,Kohn,Gordon,

 

I have followed your discussions about early diagnosis of stroke in ER with
great interest.  I would like to make the following comments about diagnosis
in general which may have relevance to diagnosis of stroke.

 

1.       Since introduction of am probabilistic  model of diagnosis by
Lesley and Lusted in 1959 (Science ’59), it has become customary to
represent pretest certainty about a disease by its pretest probability.

          

        2.A pretest probability depends upon a number of independent factors
such as symptoms, risk factors, patient’s age, sex etc. which together
constitute a clinical presentation. Therefore, a prêt

            est. probability, like any other measure such as height or
intelligence quotient, which depends upon a number of independent factors,
will tend to be distributed normally in patients with a given 

            ease encountered by us (Tao, Best Writing  in Mathematics 2013)


      

 3.This means most patients with a disease (68 percent) will have
intermediate pretest probability (20-79 percent), a few (16 percent) will
have low pretest probability (0-19 percent), and other few

             (16 percent) will have high pretest probability(80-100
percent).

 

         4. This trend towards normal distribution has been observed, for
example, in the PIOPED study about diagnosis of pulmonary embolism(JAMA
1990), 67 percent of 252 patients with pulmonary

              Embolism had intermediate pretest probability.

 

           5. Diagnostic error in general has been found to occur in 10-15
percent patients (Graber 2013).

                In Newman- Toker’s fine study in Diagnosis too, missed
diagnosis of stroke in ER was found to be about 13 percent.

 

          6. The closeness of these diagnostic error rates to the expected
percentage of patients with low pretest probability seems to suggest that
most if not all diagnostic errors occur in these  patients.

 

          7. A major cause of diagnostic error in these patients, I suggest,
is erroneus interpretation of a low pretest probability which is considered
to be minimal evidence for a disease which is ruled out wit

               hout testing in a given, individual patient.

 

           8. Its correct interpretation, I suggest, is as a distribution,
which only indicates a few patients with a disease in a series of similar
patients. It does not tell us anything at all about presence or absence 

                Of a disease in a given patient.

 

           9. The presence or absence of a disease in any patient regardless
of pretest probability can only be determined by a test result with a high
likelihood ratio ( 10 or higher) or a low likelihood ratio

               (0.1 or lower) respectively (Jaeschke 2002).

 

           10. For widespread use, a test capable of generating such a
result needs to be simple and inexpensive. An example of such a test is EKG,
which is performed in practically every patient with chest 

                 pain seen in ER for evaluation of acute myocardial
infarction.

 

           11. HINTS appears to be such a test for evaluating for stroke in
patients with dizziness seen in ER, as suggested by Newman-Toker.

 

           12.In conclusion, I believe, the following measures could help
minimise diagnostic errors.

                

(a)    An incresed awareness thatn a substantial proportion of patients with
a given disease, about 10-15 percent, encountered by us  is likeky to have
low pretest probability.

(b)   A disease cannot be ruled out purely from its low pretest probability.

(c)    In a given patient with low pretest probability, a disease can only
be ruled out if a test result with low likelihood ratio (0.1 or lower) is
observed. 

 

 

 

Bimal P Jain MD

Pulmonary-Critical Care

Northshore Medical Center

Lynn, MA 01904

 

                              

 

 

 

From: David Newman-Toker [mailto:toker at JHU.EDU] 
Sent: Friday, June 20, 2014 10:50 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Thanks David. I’ve copied the ListServ because I think this sort of
discussion might be interesting to others --- it is the messy real-world
business of doing diagnosis in clinical practice! For those interested, see
David G.’s excellent points in the trail below.

 

In response to each of your four points:

 

1a) OTTAWA SAH RULE IN PRINCIPLE --- Personally, I wouldn’t tap every
patient over 20 with a new headache peaking in less than an hour – if they
had a very compelling migraine story (e.g., classic visual aura), and it
peaked progressively over 55 minutes (or anything over 30, probably), and
they were in the correct age group for migraine onset (e.g., 15-40), and
didn’t have a personal/family history of aneurysm/SAH, and had none of the
dangerous Ottawa SAH rule features, I wouldn’t even CT them.  Nevertheless

I totally understand your perspective, and doing CT-LP in all of the
patients in their series (using their entry criteria rather than their final
rule) might be slightly simpler than following their rule; it would,
however, increase the fraction of headache patients who got (presumably
unnecessary) CT/LP by ~15%, which, back of the napkin, is probably at least
30,000 excess CTs a year in the US at a cost of about $10M/year
 it may be a
drop in the healthcare bucket, but, for that amount, we could do some really
nice diagnostic research to refine decision rules to increase performance,
usability, and buy in. :-) 

 

1b) OTTAWA SAH RULE IN PRACTICE --- I think there is a wider
evidence-practice gap in average community ED practice than might be
imagined
 only 2% of US headache patients undergo an LP (Goldstein,
Cephalalgia, 2006) --- that includes all the suspected meningitis and SAH
cases; I think most people believe that at least 2% of the total (probably
more) have one or the other (meningitis or SAH) as a cause
 so 2% is
probably a lot fewer LPs than we should be doing, if we consider the
asymmetric risk associated with LP vs. missed meningitis/SAH. So I would
venture a guess that the average community ED physician is not being as
thorough about looking for missed SAH as you are being in your practice

unfortunately, probably none of them are reading this ListServ to benefit
from your thoughtful perspective.

 

2) HINTS --- Agree it is operator dependent, and some of this may go away
when devices become more ubiquitous
 BUT
 how should we respond ---
knowingly miss 35% of all the strokes using a bad decision-making approach
that is standard practice
 or seek out training to learn to do HINTS
properly? Maybe ‘ok’ HINTS is still better than ‘great’ ABCD2/vascular risk
stratification?  

 

3) WHEN TO APPLY DECISION RULES --- This is an under-discussed but critical
problem; one I spoke about in my commentary about JJ Perry’s SAH decision
rule; but these rules are developed with fairly strict entry criteria, so I
think that applying them in practice is mostly about pattern matching to the
study methods in the paper (which, unfortunately, is probably rarely done in
practice). I agree it can be tough, though, even if you try hard. I was
giving grand rounds at Cornell earlier this week, and they took me to see an
acute patient in the ED with dizziness --- it took some skill just to know
whether HINTS should be applied or not
 and you probably couldn’t have
acquired that skill simply by reading the article. I think the co-symptoms
issue is less problematic --- whatever the allowable co-symptoms were in the
study are what’s relevant, and the determination is made based on the
patient’s chief symptom/complaint --- I realize that this is not a perfectly
reliable measure (and we have done studies that prove that inter-observer
variation is more common than you’d like), but it is certainly a familiar
enough one to all physicians
 and, until computers are taking all of our
histories from patients for us, it will likely remain part of our ‘art.’

 

4) ISCHEMIC STROKE MECHANISMS IN THE YOUNG --- I believe that we are getting
fatter and more diabetic at a younger age, but, honestly, I’m not worried
about strokes being missed in patients who are 35 that have HTN, DM, high
cholesterol, chronic renal insufficiency, peripheral vascular disease, and a
history of 2 prior MIs. No one will ignore all that just because of age. I
remember a normal weight guy with no PMH who was 35 and presented with
episodic blurred vision and confusion
 he was sent home as suspected
migraine
 came back with turned out to be the proband for a family with
undiagnosed familial hypercholesterolemia. So there will be some cases where
stroke is the index event that discloses a patient’s (previously unknown)
vascular risk and are tricky (esp. those who are non-obese)
 but most of the
ones we miss will likely be dissections, cardiac emboli, and ‘cryptogenic’
--- I think we should focus our attention on solving the diagnostic problems
for those patients.

 

David

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


Confidentiality Notice: The information contained in this email is intended
for the confidential use of the above named recipient. If the reader of this
message is not the intended recipient or person responsible for delivering
it to the intended recipient, you are hereby notified that you have received
this communication in error, and that any review, dissemination,
distribution, or copying of this communication is strictly prohibited. If
you have received this in error, please notify the sender immediately by
telephone at the number set forth above and destroy this email message.
Thank you.

 

From: David Gordon, M.D. [mailto:davidc.gordon at duke.edu] 
Sent: Thursday, June 19, 2014 5:03 PM
To: David Newman-Toker
Subject: RE: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Hi David,

 

Thanks for highlighting these articles. So some specific and then general
comments:

 

1)      Ottawa SAH rule--- This is interesting because I have to say, while
this rule is a good teaching tool for highlighting the red flags of
headache, it would not personally impact my practice pattern.  If I have
anyone 20 or older ( the rule uses 15) with “new severe nontraumatic
headache reaching maximum intensity within 1 hour” that alone is enough for
me to cross over the diagnostic threshold  for ruling out SAH.  The other
variables in the rule don’t mean much at that point for testing purposes. I
would venture to say most emergency physicians would acknowledge this as the
culture of their training. So in the patients with missed SAH in your recent
study, it would be very interesting to know the specifics of their
presentation. Did they have an atypical presentation for SAH that not even
the rule would capture or would this rule if employed by the physician as a
diagnostic aid have appropriately steered them towards work-up they
neglected to pursue. Need to see this rule studied prospectively.

2)      HINTS- compelling CDR but I would venture to say limited by operator
dependence—at least until the devices are widely available and employed.  I
personally would not feel confident in my current proficiency in assessing
eye movements. You alluded to this in the article, but it seems this has
been employed mainly by specialists to date. Would the sensitivity change in
the hands of less experienced practitioners? As you also alluded to, used
indiscriminately, this CDR runs the risk of MRI overuse given that a normal
physiologic response is a bad sign in this tool.  So I still think there is
more studies to be done before recommending for general use. 

3)      This has been echoed before, but applying such rules prospectively
in an undifferentiated population could be challenging owing to overlapping
spectrum of symptoms. Patients come in with headache alone, headache +
dizziness, headache and speech disturbance, no headache and speech
disturbance?  Which rule is one to apply? 

4)      This discussion of missing strokes in the young covers a broad array
of potential etiologies. What exactly is the pathophysiology being missed
here? Are we seeing accelerated atheroembolic disease in young patients due
to HTN and DM? Or do young people represent a separate physiology from older
patients with stroke owing to vertebral dissections and venothrombotic
events?  I think the more we understand the pathophysiology at play here,
the better we can advise clinicians to either adjust their age threshold for
the development of atheroembolic disease or to make sure to consider these
alternate disease processes for stroke-like symptoms in the young.

 

Thanks,

David G

 

 

David Gordon, MD
Associate Professor
Undergraduate Education Director
Division of Emergency Medicine
Duke University

 

From: David Newman-Toker [mailto:toker at jhu.edu] 
Sent: Thursday, June 19, 2014 7:37 AM
To: David Gordon, M.D.; Society to Improve Diagnosis in Medicine
Subject: RE: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

Thanks David. So I gather you think that these two CDRs below addressing
stroke diagnosis in patients with headache and dizziness, respectively, are
lacking some combination of good performance, ease of use, or buy in? (“rule
has good performance, easy to use, and is bought into by both emergency
physicians and neurologists”)

 

Perry JJ, Stiell IG, Sivilotti ML, et al. Clinical decision rules to rule
out subarachnoid hemorrhage for acute headache. JAMA : the journal of the
American Medical Association 2013;310:1248-55.

 

Newman-Toker DE, Kerber KA, Hsieh YH, et al. HINTS Outperforms ABCD2 to
Screen for Stroke in Acute Continuous Vertigo and Dizziness. Academic
emergency medicine : official journal of the Society for Academic Emergency
Medicine 2013;20:986-96.

 

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


Confidentiality Notice: The information contained in this email is intended
for the confidential use of the above named recipient. If the reader of this
message is not the intended recipient or person responsible for delivering
it to the intended recipient, you are hereby notified that you have received
this communication in error, and that any review, dissemination,
distribution, or copying of this communication is strictly prohibited. If
you have received this in error, please notify the sender immediately by
telephone at the number set forth above and destroy this email message.
Thank you.

 

From: David Gordon, M.D. [mailto:davidc.gordon at duke.edu] 
Sent: Wednesday, June 18, 2014 9:40 AM
To: Society to Improve Diagnosis in Medicine; David Newman-Toker
Subject: RE: [IMPROVEDX] stroke misdiagnosis disproportionate in the young
says Washington Post

 

David, 

 

A real challenge here is trying to separate the signal from the noise, and
when it comes to neurologic complaints, there is unfortunately a lot of
noise in emergency departments. Overcrowding and financial pressures further
compound the difficulty of who requires the full work-up.

 

I think risk stratification is key to this issue. We have imperfect but
overall good processes and tools in place for the risk stratification of ACS
and pulmonary embolism. As an emergency physician, I don't feel I have the
same cognitive tools available for independently risk stratifying
TIA/stroke. I am fortunate to work in a clinical environment where I have
ready access to neurology consultation to assist in the process and an
observation protocol for equivocal/intermediate cases, but I gather to say
this is far from the norm.

 

As far as the treatment of neurologic complaints in the emergency setting,
we need more evidence. It is going to take prospective analysis of
all-comers to the ED with stroke-like symptoms to better understand who
needs immediate work-up and who can be safely discharged. Perhaps we will
end up with 2 different stratification tools- one for the young and one for
the old.

 

As far as whether diagnostic aids will be utilized or ignored due to CDRs, I
think it depends. If the rule has good performance, easy to use, and is
bought into by both emergency physicians and neurologists, I do think it
would be readily employed - especially if the evidence becomes increasingly
convincing that the epidemiology of stroke is changing (or becoming better
understood) and young patient's are being misdiagnosed.

 

-David

 

David Gordon, MD
Associate Professor
Undergraduate Education Director
Division of Emergency Medicine
Duke University

 

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From: David Newman-Toker [toker at JHU.EDU]
Sent: Tuesday, June 17, 2014 2:56 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
Subject: [IMPROVEDX] stroke misdiagnosis disproportionate in the young says
Washington Post

Stroke is a major public health problem, and recent work suggests young
patients are having more strokes, with rates rising alarmingly in recent
years, according to an article in today’s Washington Post
 

 

http://www.washingtonpost.com/national/health-science/strokes-long-on-the-de
cline-among-the-elderly-are-rising-among-younger-adults/2014/06/16/f1f54538-
e5d9-11e3-a86b-362fd5443d19_story.html

 

They are also much more likely to be misdiagnosed (7-fold greater risk in
those 18-45 relative to those >75)


 

http://www.degruyter.com/view/j/dx.2014.1.issue-2/dx-2013-0038/dx-2013-0038.
xml

 

Thoughts?

 

David

 

 

David E. Newman-Toker, MD, PhD
Associate Professor, Department of Neurology
Johns Hopkins Hospital, Meyer 8-154; 600 North Wolfe Street, Baltimore, MD
21287

Email:  <mailto:toker at jhu.edu> toker at jhu.edu; 410-502-6270 (phone);
410-502-6265 (fax)
Web address:
<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vesti
bular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/David_Ne
wman-Toker> Johns Hopkins Neurology (David Newman-Toker)

 


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