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CPP #1: Introduction to Clinical Pathophysiology

CPP #1: Introduction to Clinical Pathophysiology. August, 16 th , 2005. Fred Arthur Zar, MD, FACP Director, M2 Clinical Pathophysiology Course Professor of Clinical Medicine University of Illinois at Chicago. CPP Course Format. Two Semesters Lectures, small group, labs

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CPP #1: Introduction to Clinical Pathophysiology

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  1. CPP #1: Introduction toClinical Pathophysiology August, 16th, 2005 Fred Arthur Zar, MD, FACP Director, M2 Clinical Pathophysiology Course Professor of Clinical Medicine University of Illinois at Chicago

  2. CPP Course Format • Two Semesters • Lectures, small group, labs • Locations posted outside 221 CMW • All changes on medclass2008 listserv • Faculty of MDs • 2 changes • Review sessions before each exam

  3. CPP Examinations • One per quarter • Questions derived from • Lecturers • Pool items. • Final compilation by course director. • NOT comprehensive • Weighted based on hours of lecture • Blueprint is only an approximation! • Pass if weighted total > MPL • Otherwise one make–up exam which is comprehensive

  4. Approach to Learning Getting and handling the info Come to classes and review sessions Take notes Save handouts Get coop notes Compile all into your relearnable notes Philosophy Try to learn the material not pass the test Study ~ daily (note complilation) Seek to understand not memorize Do not use practice questions Learning Resources Lecture handouts Your notes from class Coop notes I review them all Review sessions No recommended textbook CPP: How To Get The Most Out Of It

  5. CPP: Prior Class Results Number (%) Grade 2002–20032003–20042004–2005 Honors 40 (21) 31 (18) 31 (18) Satisfactory 143 (73) 136 (79) 143 (78) Unsatisfactory 11 (6) 6 (3) 8 (3) Failed 0 0 3 (1)

  6. CPP: Course Coordinator • Susan O’Keefe • Assistant to Associate Dean,Undergraduate Medical Education • Phone: 312–996–9030 • Email: sokeefe@uic.edu

  7. CPP: Course Director • Fred Arthur Zar, MD, FACP • Professor of Clinical Medicine • Course Director, Clinical Pathophysiology Course • Chief, Inpatient Medicine, University of Illinois Medical Center • Vice Head of Education, Department of Medicine • Program Director, Internal Medicine Residency • Office: 440 CSN • Phone: 312–996–5014 • Email: fazar@uic.edu

  8. When Physicians “Make a Diagnosis” After the chief complaint 50% After the history is completed 80% After the physical is completed 90% After test 95%

  9. How Do They Do It? • Listen to the patient • Trust what you are hearing • Know the basic sciences • Know clinical pathophysiology • Think backwards!

  10. The Chief Complaint • Structure • Age • Sex • Why are they seeking medical attention (complaint) • (Duration) • Utility • Only 120 unique complaints • Know the diagnosis • Focuses you on further questions to ask • Focuses your physical exam

  11. M1 Year Anatomy Brain and Behavior Biochemistry Microbiology Physiology Tissue Biology Genetics Nutrition Human Development M2 Year Pathology Infection and Immunity Pharmacology Psychopathology The Basic Sciences

  12. What Puts It All Together? Clinical Pathophysiology

  13. Case One • Chief Complaint • 22 year–old woman: “I’m eating a ton but losing weight”

  14. Case One • Chief Complaint • 22 year–old woman: “I’m eating a ton but losing weight” • Your Thoughts • Increased appetite with weight loss has two general causes • increased catabolism of calories • increased loss of calories

  15. Case One • Chief Complaint • 22 year–old woman: “I’m eating a ton but losing weight” • Your Thoughts • Increased appetite with weight loss has two general causes • increased catabolism of calories • increased loss of calories • Illnesses Possible: Relevant Questions • Increased catabolism • Hyperthyroidism: Tremor, heat intolerance, hypertension, sweating • Pheochromocytoma: Similar • Increased exercise: Increased exercise • Increased loss of calories • Bowel malabsorption: Diarrhea • Urinary losses (Diabetes mellitus): Polyuria, polydipsia, weakness

  16. Case One • Chief Complaint • 22 year–old woman: “I’m eating a ton but losing weight” • Your Thoughts • Increased appetite with weight loss has two general causes • increased catabolism of calories • increased loss of calories • Illnesses Possible: Relevant Questions • Increased catabolism • Hyperthyroidism: Tremor, heat intolerance, hypertension, sweating • Pheochromocytoma: Similar • Increased exercise: Increased exercise • Increased loss of calories • Bowel malabsorption: Diarrhea • Urinary losses (Diabetes mellitus): Polyuria, polydipsia, weakness • Testing • Blood sugar markedly elevated

  17. Case Two • Chief Complaint • 67 year–old man: “My pants and shoes don’t fit any more”

  18. Case Two • Chief Complaint • 67 year–old man: “My pants and shoes don’t fit any more” • Your Thoughts • Total body edema (anasarca) commonly caused by two pathophysiologic processes • Increased salt and water retention –> increased hydrostatic pressure • Decreased oncotic pressure

  19. Case Two • Chief Complaint • 67 year–old man: “My pants and shoes don’t fit any more” • Your Thoughts • Total body edema (anasarca) commonly caused by two pathophysiologic processes • Increased salt and water retention –> increased hydrostatic pressure • Decreased oncotic pressure • Illnesses Possible: Relevant Questions • Increased salt and water retention –> increased hydrostatic pressure • Renal failure: Diabetes, hematuria, family history, drugs • Congestive heart failure: prior MI, orthopnea, PND • Decreased oncotic pressure (low albumin) • Bowel malabsorption: Diarrhea • Liver failure: Alcohol consumption, chronic viral hepatitis (B or C)

  20. Case Two • Chief Complaint • 67 year–old man: “My pants and shoes don’t fit any more” • Your Thoughts • Total body edema (anasarca) commonly caused by two pathophysiologic processes • Increased salt and water retention –> increased hydrostatic pressure • Decreased oncotic pressure • Illnesses Possible: Relevant Questions • Increased salt and water retention –> increased hydrostatic pressure • Renal failure: Diabetes, hematuria, family history, drugs • Congestive heart failure: prior MI, orthopnea, PND • Decreased oncotic pressure (low albumin) • Bowel malabsorption: Diarrhea • Liver failure: Alcohol consumption, chronic viral hepatitis (B or C) • Tests • Hepatitis C antibody (+), liver Bx shows cirhhosis

  21. Types of Testing • Diagnostic Test • A test performed on a person suspected of having a specific disease to determine if they have that specific disease • e. g. A biopsy of a breast mass • Screening Test • A test performed on a healthy person to determine if they have a specific disease or disease risk factor • e. g. A serum cholesterol level in a 50 year old man • Prognostic Test • A test performed to assess the prognosis of a known disease. • e. g. An HIV viral load assay in a person with HIV infection • Monitoring Test • A test performed to assess a response to treatment • e. g. An erythrocyte sedimentation rate in a patient on antibiotics for osteomyelitis • Confirmatory Test • A test performed to complement a previously abnormal test and increase the specificity of a diagnosis • e. g. A Fluorescent Treponemal Antibody (FTA) antibody assay after a Rapid Plasma Reagin (RPR) antibody test is positive in a person suspected of syphilis

  22. A perfect test A real test

  23. Diagnostic Test Possibilities Disease Test Result Present Absent Positive TP FP Negative FN TN TP = True positive FP = False positive FN = False negative TN = True negative

  24. Sensitivity Disease Test Result Present Absent Positive TP FP Negative FN TN Sensitivity • % positive tests in persons with a disease = TP/(TP + FN) • Positive in Disease (PID) • A highly sensitive test is (+) in “everyone” with a disease • A highly sensitive test if (–) “rules out” a disease • Not dependent on disease prevalence

  25. Specificity Disease Test Result Present Absent Positive TP FP Negative FN TN Specificity • % negative tests in persons without disease = TN/(TN + FP) • Negative in Health (NIH) • A highly specific test is (–) in “everyone” without a disease • A highly specific test if (+) “rules in” a disease • Not dependent on disease prevalence

  26. Positive Predictive Value Disease Test Result Present Absent Positive TP FP Negative FN TN Positive Predictive Value (PPV) • % of positive results that are true positives = TP/(TP + FP) • If test is (+), the chance the patient has the disease • Dependent on disease prevalence • low prevalence –> low TP –> low PPV

  27. Negative Predictive Value Disease Test Result Present Absent Positive TP FP Negative FN TN Negative Predictive Value (NPV) • % of negative results that are true negatives = TN/(TN + FN) • If test is (–), the chance the patient does not have the disease • Dependent on disease prevalence • low prevalence –> low FN –> high NPV

  28. Should I Order This Test? • Will the sensitivity, specificity and predictive values allow it to provide clinically useful information? • Will the results change the diagnosis, prognosis or therapy? • What are the expected outcomes and why?

  29. Terms Describing the Frequency of a Finding • Prevalence • Proportion of a sample/population currently with a finding • “1 per 100,000 men have gene Q” • Incidence • Proportion of a sample/population that develops a finding within a specified period of time • “15 per 1000 developed AIDS in 5 years”

  30. Bayesian AnalysisPre– and Post–Test Probabilities • Pretest Probability • The probability of a diagnosis being present before the results of a diagnostic test are available. • Posttest Probability • The probability of a diagnosis being present after the results of a diagnostic test are available.

  31. Using Bayesian Analysis for A Diagnostic Test • Background • Acute intermittent porphyria (AIP) is autosomal dominant • Causes disabling abdominal pain, neuropathy and seizures • Low blood porphobilinogen deaminase can be used to attempt to diagnose the disease, low level = (+) test • 82% of AIP have a (+) test, sensitivity = 82% • 3.7% of normal persons have a (+) test, specificity = 96.3% • Prevalence of AIP in general population = 1/10,000 (0.01%)

  32. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient A • Is “screened” and has a positive test, does he/she have AIP? • Pretest probability = 0.01% • Filling in the blanks AIP Test Result Present Absent Total Positive Negative Total

  33. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient A • Is “screened” and has a positive test, does he/she have AIP? • Pretest probability = 0.01% • Filling in the blanks AIP Test Result Present Absent Total Positive Negative Total 100 <– 999,990 <– 1,000,000

  34. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient A • Is “screened” and has a positive test, does he/she have AIP? • Pretest probability = 0.01% • Filling in the blanks AIP Test Result Present Absent Total Positive 36,996 Negative 962,904 Total 100 <– 999,900 <– 1,000,000 x .963

  35. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient A • Is “screened” and has a positive test, does he/she have AIP? • Pretest probability = 0.01% • Filling in the blanks AIP Test Result Present Absent Total Positive 82 36,996 –> 37,078 Negative 18 962,904 –> 962,922 Total 100 <– 999,900 <– 1,000,000 • Positive Predictive Value • PPV = 82/37,078 = 0.22%! x .82 x .963

  36. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient B • Has a brother with AIP, does he/she have AIP? • Pretest probability = 50% • Filling in the blanks AIP Test Result Present Absent Total Positive Negative Total 500,000 <– 500,000 <– 1,000,000

  37. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient B • Has a brother with AIP, does he/she have AIP? • Pretest probability = 50% • Filling in the blanks AIP Test Result Present Absent Total Positive 410,000 18,500 –> 428,500 Negative 90,000 481,500 –> 571,500 Total 500,000 <– 500,000 <– 1,000,000 • Positive Predictive Value • PPV = 410,000/428,500 = 96%! x .82 x .963

  38. Using Bayesian AnalysisDiagnosing Acute Intermittent Porphyria • Background • Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000 • Patient C • Has Sx c/w a 30% chance of AIP, does he/she have AIP? • Pretest probability = 30% • Filling in the blanks AIP Test Result Present Absent Total Positive 246,000 26,000 –> 272,000 Negative 54,000 674,000 –> 728,000 Total 300,000 <– 700,000 <– 1,000,000 • Positive Predictive Value • PPV = 246,000/272,000 = 90% x .82 x .963

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