stroke misdiagnosis ... Washington Post [CB]

Jain, Bimal P.,M.D. BJAIN at PARTNERS.ORG
Wed Jul 2 12:53:19 UTC 2014


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


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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: David Newman-Toker [mailto:toker at JHU.EDU]
Sent: Thursday, June 26, 2014 1:48 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

I am inclined to agree with Bob & David that re-engineering workflows around diagnostic decision making, with an emphasis on symptom-focused data-gathering is a promising approach… yet entirely underutilized. Last year we published some work on using automated interviewing for diagnostic purposes… I think that having the computer go directly to the patient is one way to solve the multifarious problem of (1) clinicians not having time to gather more data; (2) clinicians not wanting to be the computer’s ‘lackey’; and (3) inconsistency in the way that question are asked of patients, depending on the clinician’s experience and expertise (which increases the variance around decision support software accuracy).

http://aci.schattauer.de/en/contents/archive/issue/1729/manuscript/19834/show.html

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


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From: Swerlick, Robert A [mailto:rswerli at EMORY.EDU]
Sent: Tuesday, June 24, 2014 8:55 AM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

I could not agree more and your references are spot on. When we employ workflows which create incentives to shortcut data collection and/or data synthesis (use of system one over system two), we end up with cognitive errors and bad decisions. Couple that with the lack of feedback loops (except those measuring $’s), it becomes almost impossible to learn from any mistakes we might make.  I believe that one of the reasons that education and CME activities fail to address these issues is we place clinicians right back in the flawed workflows which allow for neither adequate data collection, data synthesis,  appropriate reflection, or feedback.

The current production model of health care delivery poses a huge challenge because even if various tools might be available to improve decision making, there is huge resistance to adoption if they negatively impact the one thing we appear to be good at measuring, that being billing and collections.  There will also be resistance from virtually all parties involved because optimizing the workflows will require fundamental changes in how all of the players interact. It will require different responsibilities from patients and members of the health care delivery teams and redefinition of these roles will happen incrementally over time.  My belief is there are limits as to how much you can improve decision making by focusing on training individuals and the real gains are to be garnered by changing the systems they work in to encourage behaviors linked to optimal decision making.

Bob Swerlick

From: David Hallbert [mailto:david.hallbert at GMAIL.COM]
Sent: Monday, June 23, 2014 9:46 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] stroke misdiagnosis ... Washington Post [CB]

   I'd be interested in hearing more about this.  In our practice for years we've used Lawrence Weed's PKC.  Patients are asked to enter their symptoms by answering an extensive set of relevant questions about a given syptom into a computer so that their input gets heard first before the physician gets to form his initial opinion and thereafter guide the discussion to positives and negatives that support his hypothesis. (discussed in Dr Groopman's book, "How Doctors Think").   The computer "couples" the information and tallies "points" in favor of this or that diagnosis.  The physician gets to review the options and refine their meaning with the patient, but then looks over ALL the results and adds indicated elements of the physical exam (guided by the computer).  With the patient in collaboration they review the findings that may go with each possible explanation for their symptoms--the pros and cons for each diagnosis with a link to the medical literature for each possibility.  They would together be forced to confront the possibility and all the supporting evidence for stroke--even in a younger patient--before accepting or rejecting that hypothesis.  They may also review management options guided by this software program. I think this method jibes much more closely to solving the inherent problem of the human brain alone outlined in Daniel Kahneman's book, "Thinking Fast, Thinking Slow" in that it uses the computer to do a much better job at what it does best--combing the vast amount of medical literature to generate possibilities-- and leaves humans to use their intuitive senses for iterative work.  I read Clive Thompson's book, "Smarter Than You Think" and see a parallel with the superiority of human plus computer over either one alone in major chess competitions.

On Sun, Jun 22, 2014 at 9:11 AM, Robert Bell <0000000296e45ec4-dmarc-request at list.improvediagnosis.org<mailto:0000000296e45ec4-dmarc-request at list.improvediagnosis.org>> wrote:
That sounds that using computers that have more power. RB

Sent from my iPad

On Jun 19, 2014, at 6:42 AM, Charlie Garland - The Innovation Outlet<cgarland at INNOVATIONOUTLET.BIZ<mailto:cgarland at INNOVATIONOUTLET.BIZ>> wrote:
David (and Pat) - It is my opinion -- as a non-physician, but as someone with some expertise in anthropology and cognitive biases -- that we do, indeed, have a problem larger than most have imagined.  One of the issues you're referencing here is a physician's use of critical thinking, which may equate to their disposition to access meta-cognition.  This is a dynamic that I refer to as "cognitive buoyancy" -- the propensity (disposition) to raise one's level of conscious awareness and consideration of process, condition, context, and subsequent alternative choices (in real time), above the routine.  This may be a natural inclination in some; in others, it may require one or more stimuli (visual, audible, cognitive, rules-based, etc.) to elicit the cognitive-buoyancy response.

Neuroscience tells us that the brain is an energy-miserly organ -- it naturally functions, in part, to conserve its constrained energy resources.  Higher-order cognitive functioning (i.e. "executive reasoning," critical thinking, metacognition, etc.) require significantly more energy than the "hard-wired" routines that we assimilate by default, thanks to the brain's constant bias toward lower-energy processing.  So the default state of our minds (physicians included) is one of rote, un-conscious functioning.  Admittedly, medical training, certain CME, clinical practice, and hospital policy/culture do, themselves, tend to increase all healthcare providers' cognitive buoyancy.  But not sufficiently.  That's one reason where/why we have a much larger problem (in my opinion).

There are a number of resources we might look to, as ways of increasing cognitive buoyancy.  Some may include increasing those factors mentioned above, but my own research includes the utilization of a set of tools and methodologies that can be used to raise this disposition in clinicians (and others).  The tools are designed specifically to both trigger, and then help guide, an individual's metacognition...which then leads to consideration of alternative (non-routine) choices for subsequent contemplation and/or action.  It is at this point where a physician might be reminded of the existence/appropriateness/need of such CDRs, or other available resources that hadn't otherwise come to mind.

If this makes sense, and you would be interested to learn more details about what I am referring to, I'd be happy to elaborate further about the tool ("Cubie"), the methodology ("Explorative Inquiry"), and the state of my research.

Kind regards,
Charlie Garland

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The Innovation Outlet
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-------- Original Message --------
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the
young says Washington Post
From: David Newman-Toker <toker at JHU.EDU<mailto:toker at JHU.EDU>>
Date: Tue, June 17, 2014 6:23 pm
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
I agree, Pat. Do you (or others) think that clinicians are also less likely to use (or think of using) clinical decision rules related to stroke (e.g., Ottawa SAH rule or HINTS) in those same patients? If so, do we have an even bigger problem than we imagined --- i.e., that even if we convince people to ‘routinely’ use evidence-based diagnostic decision rules for stroke (or other similar diagnostic problems where errors are more frequent than we’d like), that their cognitive dispositions to respond will lead to selective non-use of these rules in more atypical (i.e., less representative) cases?

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: toker at jhu.edu<mailto:toker at jhu.edu>; 410-502-6270<tel:410-502-6270> (phone); 410-502-6265<tel:410-502-6265> (fax)
Web address: Johns Hopkins Neurology (David Newman-Toker)<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vestibular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/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: Pat Croskerry [mailto:croskerry at EASTLINK.CA]
Sent: Tuesday, June 17, 2014 3:23 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
Subject: Re: [IMPROVEDX] stroke misdiagnosis disproportionate in the young says Washington Post

Probably a number of factors involved, but principle among them are likely to be representativeness and ascertainment biases.
i.e. clinicians see what they expect to see, and expect stroke victims to be older.
Pat



From: David Newman-Toker [mailto:toker at JHU.EDU]
Sent: Tuesday, June 17, 2014 3:56 PM
To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto: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-decline-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: toker at jhu.edu<mailto:toker at jhu.edu>; 410-502-6270<tel:410-502-6270> (phone); 410-502-6265<tel:410-502-6265> (fax)
Web address: Johns Hopkins Neurology (David Newman-Toker)<http://www.hopkinsmedicine.org/neurology_neurosurgery/specialty_areas/vestibular/profiles/team_member_profile/516F40C024FCA3D4B4B633D0E080FE1B/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.


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