Cpp 1 introduction to clinical pathophysiology
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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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Cpp 1 introduction to clinical pathophysiology

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


Cpp course format

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


Cpp examinations

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


Cpp how to get the most out of it

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


Cpp prior class results

CPP: Prior Class Results

Number (%)

Grade2002–20032003–20042004–2005

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)


Cpp course coordinator

CPP: Course Coordinator

  • Susan O’Keefe

  • Assistant to Associate Dean,Undergraduate Medical Education

  • Phone: 312–996–9030

  • Email: [email protected]


Cpp course director

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: [email protected]


When physicians make a diagnosis

When Physicians “Make a Diagnosis”

After the chief complaint50%

After the history is completed80%

After the physical is completed90%

After test95%


How do they do it

How Do They Do It?

  • Listen to the patient

  • Trust what you are hearing

  • Know the basic sciences

  • Know clinical pathophysiology

  • Think backwards!


The chief complaint

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


The basic sciences

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


What puts it all together

What Puts It All Together?

Clinical Pathophysiology


Case one

Case One

  • Chief Complaint

    • 22 year–old woman: “I’m eating a ton but losing weight”


Case one1

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


Case one2

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


Case one3

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


Case two

Case Two

  • Chief Complaint

    • 67 year–old man: “My pants and shoes don’t fit any more”


Case two1

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


Case two2

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)


Case two3

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


Types of testing

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


Cpp 1 introduction to clinical pathophysiology

A perfect test

A real test


Diagnostic test possibilities

Diagnostic Test Possibilities

Disease

Test ResultPresentAbsent

Positive TP FP

Negative FN TN

TP = True positive

FP = False positive

FN = False negative

TN = True negative


Sensitivity

Sensitivity

Disease

Test ResultPresentAbsent

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


Specificity

Specificity

Disease

Test ResultPresentAbsent

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


Positive predictive value

Positive Predictive Value

Disease

Test ResultPresentAbsent

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


Negative predictive value

Negative Predictive Value

Disease

Test ResultPresentAbsent

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


Should i order this test

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?


Terms describing the frequency of a finding

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”


Bayesian analysis pre and post test probabilities

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.


Using bayesian analysis for a diagnostic test

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%)


Using bayesian analysis diagnosing acute intermittent porphyria

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 ResultPresentAbsentTotal

    Positive

    Negative

    Total


Using bayesian analysis diagnosing acute intermittent porphyria1

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 ResultPresentAbsentTotal

    Positive

    Negative

    Total 100 <– 999,990 <– 1,000,000


Using bayesian analysis diagnosing acute intermittent porphyria2

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 ResultPresentAbsentTotal

    Positive 36,996

    Negative 962,904

    Total 100 <– 999,900 <– 1,000,000

x .963


Using bayesian analysis diagnosing acute intermittent porphyria3

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 ResultPresentAbsentTotal

    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


Using bayesian analysis diagnosing acute intermittent porphyria4

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 ResultPresentAbsentTotal

    Positive

    Negative

    Total 500,000 <– 500,000 <– 1,000,000


Using bayesian analysis diagnosing acute intermittent porphyria5

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 ResultPresentAbsentTotal

    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


Using bayesian analysis diagnosing acute intermittent porphyria6

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 ResultPresentAbsentTotal

    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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