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Author(s): Rajesh Mangrulkar, MD, 2009License: Unless otherwise noted, this material is made available under the terms oft...
Citation Key                          for more information see: http://open.umich.edu/wiki/CitationPolicyUse + Share + Ada...
M1 Patients and Populations  Medical Decision-Making: Uncertainty            Rajesh S. Mangrulkar, M.D.Fall 2008
Pierre Louis (1787-1872)Inventor of the numeric method and the method of                   observation                    ...
Bloodletting:standard of carefor hot, moistdiseases                   Medieval Cookbook, wikimedia commons
Louis Study of BloodlettingDay of 1stbleeding                                     Averages Duration of             Number ...
Pierre Louis (1787-1872)Inventor of the numeric method and the method of                   observation            Discover...
The CAST Study•  Class I Antiarrhythmics: standard of   care for asymptomatic ventricular   arrhythmias in the 1980 s in t...
HERS and Women s Health•  Standard of care prior to 2000  –  Promotion of hormone replacement     therapy for post-menopau...
Course Objectives•  To understand, appreciate and begin to   develop tools that handle the uncertain world   within which ...
Housekeeping: Grading•    Stated in the syllabus•    Assignments (35%)•    Attendance (15%) – Despite•    Final Exam (50%)
Housekeeping: Recommended Textbook                         • Compiled from JAMA series                         • Created a...
Objectives of today s session•  By the end of this lecture, you will   (hopefully)…	  –  have a better understanding of ho...
Thread 1: Information RetrievalLec 2 – Mon 8/11ComputerSessions1&2                     AskFri and                         ...
An Analogy to provide relevance      The Odyssey: A Tale•  The case: A 1998 Honda Odyssey with   68,000 miles, no signific...
The Odyssey: Mechanic Intake•  He asks you about other things you may   have noticed about the car.•  Other symptoms:  –  ...
The Odyssey: First steps•  What is the   mechanic thinking?    –  He generates a       differential diagnosis    –  Series...
The Odyssey: First Steps•  What does the mechanic tell you?  –  The most likely problem is the trunk latch. It is     unde...
The Odyssey: First Steps•  Diagnostic reasoning defects  –  failure to entertain all     possibilities, tendency to do    ...
The Odyssey: What happens next•  One hour after driving the minivan, the   inappropriate buzzer returns.•  Place yourself ...
The Health Care Environment•  Patients and physicians confront similar   uncertainty daily with clinical decisions  –  Tru...
An MDM Paradigm* on Uncertainty                                         • Trust                Physician                • ...
The Solution: A Toolbox                          Physician                                      • Knowledge gaps          ...
WARNINGMATHAHEAD
A Clinical Tale•  20 year-old woman presents for genetic   testing•  Mother had breast and ovarian cancer,   likely has th...
The Tale Continues…FFwd•  At age 75 she has not been diagnosed   with breast or ovarian cancer.•  Is her probability of ha...
Diagnostic Reasoning: Probabilistic              ReasoningProbability: The likelihood of the   occurrence of an event.•  P...
Prior Probabilities•  Based on many factors:  –  Clinician experience  –  Patient demographics  –  Characteristics of the ...
Conditional Probabilities•  What is the probability of event B, given   an event A? Written as P(B | A).  –  Example: P (B...
Basic Probabilistic Rules        Examples of types of Events•  Dependent events: occurrence of 1 depends   to some extent ...
Combining Probabilities of Events•  Pr (A   B) = Pr (A) + Pr (B)  –  If A and B are mutually exclusive events•  Pr (A   B)...
Back to our story75 yo woman whose mother very likely   had the BRCA gene, but who has not   herself been diagnosed with b...
Considering both sides…•  Step 1:   P (BRCA and no breast cancer)       = P(BRCA) * P(no breast ca | BRCA)       = 0.5    ...
But that doesn t tell the full story…•  Joint probabilities  –  P (BRCA    and no breast ca) = 0.15  –  P (no BRCA and no ...
WARNINGCONFUSING  MATH  AHEAD
Step 3: Bayes Theorem•  Conditional probability is the relative proportion of the   relevant joint probability to the sum ...
Applying Bayes Theorem•  P (BRCA | no breast ca) =                0.15         ------------------- = 26%         0.15 + 0....
Why is this important?•  Illustration of changing probabilities,   and shifting uncertainty…  …because of test results  …b...
Final tale: Diagnostic Reasoning•  The case: A 56 year old man without heart   disease presents with sudden onset of   sho...
Diagnostic Reasoning: Your Intake•  Q: What other symptoms were you   feeling at the time?•  A: He has had no chest pain, ...
Diagnostic Reasoning: Baby StepsPrior to Lectures on 8/18…•  What are you thinking may be going on   at this time? In othe...
Ask                Acquire              Apply                          AppraiseR. Mangrulkar
Additional Source Information                          for more information see: http://open.umich.edu/wiki/CitationPolicy...
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08.07.08: Uncertainty

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08.07.08: Uncertainty

  1. 1. Author(s): Rajesh Mangrulkar, MD, 2009License: Unless otherwise noted, this material is made available under the terms ofthe Creative Commons Attribution–Non-commercial–Share Alike 3.0 License:http://creativecommons.org/licenses/by-nc-sa/3.0/We have reviewed this material in accordance with U.S. Copyright Law and have tried to maximize your ability to use, share, andadapt it. The citation key on the following slide provides information about how you may share and adapt this material.Copyright holders of content included in this material should contact open.michigan@umich.edu with any questions, corrections, orclarification regarding the use of content.For more information about how to cite these materials visit http://open.umich.edu/education/about/terms-of-use.Any medical information in this material is intended to inform and educate and is not a tool for self-diagnosis or a replacement formedical evaluation, advice, diagnosis or treatment by a healthcare professional. Please speak to your physician if you have questionsabout your medical condition.Viewer discretion is advised: Some medical content is graphic and may not be suitable for all viewers.
  2. 2. Citation Key for more information see: http://open.umich.edu/wiki/CitationPolicyUse + Share + Adapt { Content the copyright holder, author, or law permits you to use, share and adapt. } Public Domain – Government: Works that are produced by the U.S. Government. (USC 17 § 105) Public Domain – Expired: Works that are no longer protected due to an expired copyright term. Public Domain – Self Dedicated: Works that a copyright holder has dedicated to the public domain. Creative Commons – Zero Waiver Creative Commons – Attribution License Creative Commons – Attribution Share Alike License Creative Commons – Attribution Noncommercial License Creative Commons – Attribution Noncommercial Share Alike License GNU – Free Documentation LicenseMake Your Own Assessment { Content Open.Michigan believes can be used, shared, and adapted because it is ineligible for copyright. } Public Domain – Ineligible: Works that are ineligible for copyright protection in the U.S. (USC 17 § 102(b)) *laws in your jurisdiction may differ { Content Open.Michigan has used under a Fair Use determination. } Fair Use: Use of works that is determined to be Fair consistent with the U.S. Copyright Act. (USC 17 § 107) *laws in your jurisdiction may differ Our determination DOES NOT mean that all uses of this 3rd-party content are Fair Uses and we DO NOT guarantee that your use of the content is Fair. To use this content you should do your own independent analysis to determine whether or not your use will be Fair.
  3. 3. M1 Patients and Populations Medical Decision-Making: Uncertainty Rajesh S. Mangrulkar, M.D.Fall 2008
  4. 4. Pierre Louis (1787-1872)Inventor of the numeric method and the method of observation Maurin, Wellcome Images
  5. 5. Bloodletting:standard of carefor hot, moistdiseases Medieval Cookbook, wikimedia commons
  6. 6. Louis Study of BloodlettingDay of 1stbleeding Averages Duration of Number of illness bleedings Source Undetermined
  7. 7. Pierre Louis (1787-1872)Inventor of the numeric method and the method of observation Discovered in 1828 that patients who were bled did worse than those who weren t • Died earlier • Recovered later
  8. 8. The CAST Study•  Class I Antiarrhythmics: standard of care for asymptomatic ventricular arrhythmias in the 1980 s in the U.S.•  Cardiac Arrhythmia Suppression Trial: discovered in 1989 that patients who were treated did worse than those who weren t.
  9. 9. HERS and Women s Health•  Standard of care prior to 2000 –  Promotion of hormone replacement therapy for post-menopausal women•  HERS study, Women s Health Initiative (2001, 2002) –  Use of estrogen replacement therapy led to higher rates of cardiovascular complications, early in treatment.
  10. 10. Course Objectives•  To understand, appreciate and begin to develop tools that handle the uncertain world within which medical facts, attitudes and decisions reside.•  To understand that skills development in this domain require nurturing and continuous application over time (usually a lifetime).•  To ask questions.
  11. 11. Housekeeping: Grading•  Stated in the syllabus•  Assignments (35%)•  Attendance (15%) – Despite•  Final Exam (50%)
  12. 12. Housekeeping: Recommended Textbook • Compiled from JAMA series • Created and compiled by leaders in clinical epidemiology, biostatistics, medical decision- making and medical education • An excellent reference tool for clinical practice • Will be referred to during all 4 years of medical school Source Undetermined
  13. 13. Objectives of today s session•  By the end of this lecture, you will (hopefully)… –  have a better understanding of how new medical knowledge is created and applied –  understand how common diagnostic testing can lead to uncertainty in diagnostic reasoning –  develop an appreciation for how uncertainty in diagnostic reasoning interacts with trust of the practitioner.
  14. 14. Thread 1: Information RetrievalLec 2 – Mon 8/11ComputerSessions1&2 AskFri and Apply8/15 or8/26 Thread 3: Diagnostic Reasoning Acquire Lecs 7&8 (8/18) SG 2 (8/19) and SG 3 (8/27) Appraise Thread 2: Clinical Epidemiology and Biostats Lec 3 (Fri) and Lecs 4&5 (Tues) R. Mangrulkar SG 1 (8/14)
  15. 15. An Analogy to provide relevance The Odyssey: A Tale•  The case: A 1998 Honda Odyssey with 68,000 miles, no significant past maintenance history, presents with a buzzer problem.•  Description of the problem: When driving, even when all doors and the trunk are closed, the door ajar buzzer (but not light) sometimes comes on. Only turning off the automatic sliding side door control will turn off the buzzer.
  16. 16. The Odyssey: Mechanic Intake•  He asks you about other things you may have noticed about the car.•  Other symptoms: –  Trunk latch sometimes stuck in the past, not now (active recall on the latch) –  Automatic side door control replaced as per recall 2 years ago.
  17. 17. The Odyssey: First steps•  What is the mechanic thinking? –  He generates a differential diagnosis –  Series of possibilities with associated#1: Trunk latch defect (recall probabilities pending)#2: Ajar sensing defect on side door#3: Side door not closing properly
  18. 18. The Odyssey: First Steps•  What does the mechanic tell you? –  The most likely problem is the trunk latch. It is under recall anyways, so let s fix it.•  What does he do? –  He replaces the trunk latch. He drives your car, and notices no triggering of the buzzer.•  What are the potential problems with his reasoning?
  19. 19. The Odyssey: First Steps•  Diagnostic reasoning defects –  failure to entertain all possibilities, tendency to do availability, what s convenient problem representation, –  failure to elicit and pay careful anchoring, attention to description of description detail, symptoms order effects* –  failure to perform specific *Elstein, Schwartz, BMJ. 2002 March 23; diagnostic tests 324(7339): 729–732. –  failure to inform customer
  20. 20. The Odyssey: What happens next•  One hour after driving the minivan, the inappropriate buzzer returns.•  Place yourself in my position: I turn around –  What do I do next? and –  Do I return to the mechanic? go back…
  21. 21. The Health Care Environment•  Patients and physicians confront similar uncertainty daily with clinical decisions –  Trust between practitioner and patient –  Fidelity of the information that the practitioner uses –  Accurate transmission of information between physician and patient –  Patient access to trustworthy information outside of the practitioner
  22. 22. An MDM Paradigm* on Uncertainty • Trust Physician • Communcation Patient • Knowledge gaps • Too much info • Inaccurate • Info requires • Patient variation interpretation Information*Adopted from a model by K. Skeff, PhD
  23. 23. The Solution: A Toolbox Physician • Knowledge gaps • Too much info • Info requires interpretation#1: Question generation#2: Searching skills#3: Biostatistics, clinicalepidemiology, critical appraisal Information#4: Diagnostic reasoning R. Mangrulkar
  24. 24. WARNINGMATHAHEAD
  25. 25. A Clinical Tale•  20 year-old woman presents for genetic testing•  Mother had breast and ovarian cancer, likely has the BRCA gene (autosomal dominant)•  With this assumption, the patient s likelihood of having the gene is… 50%•  She decides not to get tested.
  26. 26. The Tale Continues…FFwd•  At age 75 she has not been diagnosed with breast or ovarian cancer.•  Is her probability of having the BRCA gene different at age 75 than it was at age 20? –  Yes: it is lower –  How much lower?
  27. 27. Diagnostic Reasoning: Probabilistic ReasoningProbability: The likelihood of the occurrence of an event.•  P (X) = the probability of event X•  P(BRCA) = the probability that a patient carries the BRCA gene
  28. 28. Prior Probabilities•  Based on many factors: –  Clinician experience –  Patient demographics –  Characteristics of the patient presentations (history and physical exam) –  Previous testing –  Genetic knowledge (in this case)•  P(BRCA) = 50%
  29. 29. Conditional Probabilities•  What is the probability of event B, given an event A? Written as P(B | A). –  Example: P (BRCA | no breast cancer)•  Key concept: –  Conditional probabilities can be combined with prior probabilities to create joint probabilities
  30. 30. Basic Probabilistic Rules Examples of types of Events•  Dependent events: occurrence of 1 depends to some extent on the other –  Example: The same person passing step 1 of the boards and then passing step 2 of the boards 2 years later.•  Independent events: both can occur –  Example: 2 different people passing step 1 of the boards•  Mutually exclusive events: cannot both occur –  Example: A person getting >250 on step 1 of the boards, or the same person getting 220-250 on step 1.
  31. 31. Combining Probabilities of Events•  Pr (A B) = Pr (A) + Pr (B) –  If A and B are mutually exclusive events•  Pr (A B) = Pr (A) * Pr (B) –  If A and B are independent events•  Pr (A B) = Pr (A) * Pr (B|A) –  If A and B are dependent events (Joint probability) = OR = AND
  32. 32. Back to our story75 yo woman whose mother very likely had the BRCA gene, but who has not herself been diagnosed with breast cancer.•  Our patient wants to know: –  What is P (BRCA | no breast ca)?
  33. 33. Considering both sides…•  Step 1: P (BRCA and no breast cancer) = P(BRCA) * P(no breast ca | BRCA) = 0.5 * 0.3 (from studies) = 0.15•  Step 2: P (no BRCA and no breast ca) = P(no BRCA)*P(no breast ca|no BRCA) = 0.5 * 0.875(from studies) = 0.4375
  34. 34. But that doesn t tell the full story…•  Joint probabilities –  P (BRCA and no breast ca) = 0.15 –  P (no BRCA and no breast ca) = 0.4375•  The assumption is that these are NOT independent events.•  Again, our patient wants to know: –  What is P (BRCA | no breast ca)?
  35. 35. WARNINGCONFUSING MATH AHEAD
  36. 36. Step 3: Bayes Theorem•  Conditional probability is the relative proportion of the relevant joint probability to the sum of all the joint probabilities.•  P(BRCA | no breast ca) = P(BRCA) * P (no breast ca | BRCA) P (no breast ca)•  P (no breast ca) = sum of all the joint probabilities •  P (no breast ca & BRCA) •  P (no breast ca & NOT BRCA)
  37. 37. Applying Bayes Theorem•  P (BRCA | no breast ca) = 0.15 ------------------- = 26% 0.15 + 0.4375•  26% is significantly lower than 50% (our prior probability)
  38. 38. Why is this important?•  Illustration of changing probabilities, and shifting uncertainty… …because of test results …because of events …because of time•  Fundamentally, clinicians deal with probabilities and uncertainty with each patient they encounter
  39. 39. Final tale: Diagnostic Reasoning•  The case: A 56 year old man without heart disease presents with sudden onset of shortness of breath.•  Description of the problem: Yesterday, after flying in from California the day before, the patient awoke at 3AM with sudden shortness of breath. His breathing is not worsened while lying down.
  40. 40. Diagnostic Reasoning: Your Intake•  Q: What other symptoms were you feeling at the time?•  A: He has had no chest pain, no leg pain, no swelling. He just returned yesterday from a long plane ride. He has no history of this problem before. He takes an aspirin every day. He smokes a pack of cigarettes a day.
  41. 41. Diagnostic Reasoning: Baby StepsPrior to Lectures on 8/18…•  What are you thinking may be going on at this time? In other words, generate a differential diagnosis of possibilities…•  Assign likelihoods to each possibility•  Place the possibilities in descending order of likelihood
  42. 42. Ask Acquire Apply AppraiseR. Mangrulkar
  43. 43. Additional Source Information for more information see: http://open.umich.edu/wiki/CitationPolicySlide 4: Maurin, Wellcome images, http://images.wellcome.ac.uk/, CC:BY:NC http://creativecommons.org/licenses/by-nc/2.0/Slide 5: The Medieval Cookbook, Wikimedia Commons, http://commons.wikimedia.org/wiki/File:Blood_letting.jpg, PD-EXPSlide 6: Source UndeterminedSlide 12: Source UndeterminedSlide 14: Rajesh MangrulkarSlide 19: Elstein, Schwartz, BMJ. 2002 March 23; 324(7339): 729–732.Slide 22: Original source, K. Skeff, PhDSlide 23: Rajesh MangrulkarSlide 42: Rajesh Mangrulkar

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