CPP #1: Introduction toCPP #1: Introduction to Clinical PathophysiologyClinical Pathophysiology

Fred Arthur Zar, MD, FACPFred Arthur Zar, MD, FACPDirector, M2 Clinical Pathophysiology CourseDirector, M2 Clinical Pathophysiology Course

Professor of Clinical MedicineProfessor of Clinical MedicineUniversity of Illinois at ChicagoUniversity of Illinois at Chicago

August, 16August, 16thth, 2005, 2005

CPP Course FormatCPP Course Format– Two SemestersTwo Semesters– Lectures, small group, labsLectures, small group, labs• Locations posted outside 221 CMWLocations posted outside 221 CMW• All changes on medclass2008 listservAll changes on medclass2008 listserv

– Faculty of MDsFaculty of MDs• 2 changes2 changes

– Review sessions before each examReview sessions before each exam

CPP ExaminationsCPP Examinations– One per quarterOne per quarter– Questions derived from Questions derived from • LecturersLecturers• Pool items. Pool items.

– Final compilation by course director.Final compilation by course director.– NOT comprehensiveNOT comprehensive– Weighted based on hours of lectureWeighted based on hours of lecture• Blueprint is only an approximation!Blueprint is only an approximation!

– Pass if weighted total Pass if weighted total >> MPL MPL– Otherwise one make–up exam which is Otherwise one make–up exam which is

comprehensivecomprehensive

CPP: How To Get The Most Out Of ItCPP: How To Get The Most Out Of It

• Approach to LearningApproach to Learning– Getting and handling the infoGetting and handling the info

• Come to classes and review sessionsCome to classes and review sessions• Take notesTake notes• Save handoutsSave handouts• Get coop notesGet coop notes• Compile all into your relearnable notesCompile all into your relearnable notes

– PhilosophyPhilosophy• Try to learn the material not pass the Try to learn the material not pass the

testtest• Study ~ daily (note complilation)Study ~ daily (note complilation)• Seek to understand not memorizeSeek to understand not memorize• Do not use practice questionsDo not use practice questions

• Learning ResourcesLearning Resources– Lecture handoutsLecture handouts– Your notes from classYour notes from class– Coop notesCoop notes

• I review them allI review them all– Review sessionsReview sessions– No recommended textbookNo recommended textbook

CPP: Prior Class ResultsCPP: Prior Class Results

4031 31

143136

143

11 6 80 0 30

20406080

100120140160

HonorsSatsifactoryUnsatisfactoryFailed

2002–2003 2003–2004 2004–2005 Number (%) Number (%)

GradeGrade 2002–20032002–2003 2003–20042003–2004 2004–20052004–2005

HonorsHonors 40 (21) 31 (18) 40 (21) 31 (18) 31 (18)31 (18)

Satisfactory 143 (73) 136 (79) 143 (78)Satisfactory 143 (73) 136 (79) 143 (78)

Unsatisfactory 11 (6) 6 (3)Unsatisfactory 11 (6) 6 (3) 8 (3) 8 (3)

FailedFailed 0 0 0 0 3 (1) 3 (1)

CPP: Course CoordinatorCPP: Course Coordinator• Susan O’KeefeSusan O’Keefe• Assistant to Associate Dean,Assistant to Associate Dean,

Undergraduate Medical EducationUndergraduate Medical Education• Phone: 312–996–9030Phone: 312–996–9030• Email: sokeefe@uic.eduEmail: sokeefe@uic.edu

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

When Physicians “Make a Diagnosis”When Physicians “Make a Diagnosis”

After the chief complaintAfter the chief complaint 50%50%After the history is completedAfter the history is completed

80%80%After the physical is completedAfter the physical is completed 90%90%After testAfter test

95%95%

How Do They Do It?How Do They Do It?• Listen to the patientListen to the patient• Trust what you are hearingTrust what you are hearing• Know the basic sciencesKnow the basic sciences• Know clinical pathophysiologyKnow clinical pathophysiology• Think backwards!Think backwards!

The Chief ComplaintThe Chief Complaint• StructureStructure– AgeAge– SexSex– Why are they seeking medical attention (complaint)Why are they seeking medical attention (complaint)– (Duration)(Duration)

• UtilityUtility– Only 120 unique complaintsOnly 120 unique complaints– Know the diagnosis Know the diagnosis – Focuses you on further questions to askFocuses you on further questions to ask– Focuses your physical examFocuses your physical exam

The Basic SciencesThe Basic Sciences• M1 YearM1 Year– AnatomyAnatomy– Brain and BehaviorBrain and Behavior– BiochemistryBiochemistry– MicrobiologyMicrobiology– PhysiologyPhysiology– Tissue BiologyTissue Biology– GeneticsGenetics– NutritionNutrition– Human DevelopmentHuman Development

• M2 YearM2 Year– PathologyPathology– Infection and ImmunityInfection and Immunity– PharmacologyPharmacology– PsychopathologyPsychopathology

What Puts It All Together?What Puts It All Together?

Clinical PathophysiologyClinical Pathophysiology

Case OneCase One• Chief ComplaintChief Complaint– 22 year–old woman: “I’m eating a ton but losing 22 year–old woman: “I’m eating a ton but losing

weight”weight”

Case OneCase One• Chief ComplaintChief Complaint– 22 year–old woman: “I’m eating a ton but losing 22 year–old woman: “I’m eating a ton but losing

weight”weight”• Your ThoughtsYour Thoughts– Increased appetite with weight loss has two general Increased appetite with weight loss has two general

causescauses• increased catabolism of caloriesincreased catabolism of calories• increased loss of caloriesincreased loss of calories

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

• Your ThoughtsYour Thoughts– Increased appetite with weight loss has two general causesIncreased appetite with weight loss has two general causes

• increased catabolism of caloriesincreased catabolism of calories• increased loss of caloriesincreased loss of calories

• Illnesses Possible: Relevant QuestionsIllnesses Possible: Relevant Questions– Increased catabolismIncreased catabolism

• Hyperthyroidism: Tremor, heat intolerance, hypertension, sweatingHyperthyroidism: Tremor, heat intolerance, hypertension, sweating• Pheochromocytoma: SimilarPheochromocytoma: Similar• Increased exercise: Increased exerciseIncreased exercise: Increased exercise

– Increased loss of caloriesIncreased loss of calories• Bowel malabsorption: DiarrheaBowel malabsorption: Diarrhea• Urinary losses (Diabetes mellitus): Polyuria, polydipsia, weaknessUrinary losses (Diabetes mellitus): Polyuria, polydipsia, weakness

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

• Your ThoughtsYour Thoughts– Increased appetite with weight loss has two general causesIncreased appetite with weight loss has two general causes

• increased catabolism of caloriesincreased catabolism of calories• increased loss of caloriesincreased loss of calories

• Illnesses Possible: Relevant QuestionsIllnesses Possible: Relevant Questions– Increased catabolismIncreased catabolism

• Hyperthyroidism: Tremor, heat intolerance, hypertension, sweatingHyperthyroidism: Tremor, heat intolerance, hypertension, sweating• Pheochromocytoma: SimilarPheochromocytoma: Similar• Increased exercise: Increased exerciseIncreased exercise: Increased exercise

– Increased loss of caloriesIncreased loss of calories• Bowel malabsorption: DiarrheaBowel malabsorption: Diarrhea• Urinary losses (Diabetes mellitus): Polyuria, polydipsia, weaknessUrinary losses (Diabetes mellitus): Polyuria, polydipsia, weakness

• TestingTesting– Blood sugar markedly elevatedBlood sugar markedly elevated

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

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

• Your ThoughtsYour Thoughts– Total body edema (anasarca) commonly caused by two Total body edema (anasarca) commonly caused by two

pathophysiologic processespathophysiologic processes• Increased salt and water retention –> increased hydrostatic Increased salt and water retention –> increased hydrostatic

pressurepressure• Decreased oncotic pressureDecreased oncotic pressure

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

• Your ThoughtsYour Thoughts– Total body edema (anasarca) commonly caused by two Total body edema (anasarca) commonly caused by two

pathophysiologic processespathophysiologic processes• Increased salt and water retention –> increased hydrostatic pressureIncreased salt and water retention –> increased hydrostatic pressure• Decreased oncotic pressureDecreased oncotic pressure

• Illnesses Possible: Relevant QuestionsIllnesses Possible: Relevant Questions– Increased salt and water retention –> increased hydrostatic pressureIncreased salt and water retention –> increased hydrostatic pressure

• Renal failure: Diabetes, hematuria, family history, drugsRenal failure: Diabetes, hematuria, family history, drugs• Congestive heart failure: prior MI, orthopnea, PNDCongestive heart failure: prior MI, orthopnea, PND

– Decreased oncotic pressure (low albumin)Decreased oncotic pressure (low albumin)• Bowel malabsorption: DiarrheaBowel malabsorption: Diarrhea• Liver failure: Alcohol consumption, Liver failure: Alcohol consumption, chronic viral hepatitis (B or C)chronic viral hepatitis (B or C)

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

• Your ThoughtsYour Thoughts– Total body edema (anasarca) commonly caused by two pathophysiologic Total body edema (anasarca) commonly caused by two pathophysiologic

processesprocesses• Increased salt and water retention –> increased hydrostatic pressureIncreased salt and water retention –> increased hydrostatic pressure• Decreased oncotic pressureDecreased oncotic pressure

• Illnesses Possible: Relevant QuestionsIllnesses Possible: Relevant Questions– Increased salt and water retention –> increased hydrostatic pressureIncreased salt and water retention –> increased hydrostatic pressure

• Renal failure: Diabetes, hematuria, family history, drugsRenal failure: Diabetes, hematuria, family history, drugs• Congestive heart failure: prior MI, orthopnea, PNDCongestive heart failure: prior MI, orthopnea, PND

– Decreased oncotic pressure (low albumin)Decreased oncotic pressure (low albumin)• Bowel malabsorption: DiarrheaBowel malabsorption: Diarrhea• Liver failure: Alcohol consumption, Liver failure: Alcohol consumption, chronic viral hepatitis (B or C)chronic viral hepatitis (B or C)

• TestsTests– Hepatitis C antibody (+), liver Bx shows cirhhosisHepatitis C antibody (+), liver Bx shows cirhhosis

Types of TestingTypes of Testing• Diagnostic TestDiagnostic Test

– A test performed on a person suspected of having a specific disease to determine if they have that A test performed on a person suspected of having a specific disease to determine if they have that specific diseasespecific disease

– e. g. A biopsy of a breast masse. g. A biopsy of a breast mass• Screening TestScreening Test

– A test performed on a healthy person to determine if they have a specific disease or disease risk A test performed on a healthy person to determine if they have a specific disease or disease risk factorfactor

– e. g. A serum cholesterol level in a 50 year old mane. g. A serum cholesterol level in a 50 year old man• Prognostic TestPrognostic Test

– A test performed to assess the prognosis of a known disease.A test performed to assess the prognosis of a known disease.– e. g. An HIV viral load assay in a person with HIV infectione. g. An HIV viral load assay in a person with HIV infection

• Monitoring TestMonitoring Test– A test performed to assess a response to treatmentA test performed to assess a response to treatment– e. g. An erythrocyte sedimentation rate in a patient on antibiotics for osteomyelitise. g. An erythrocyte sedimentation rate in a patient on antibiotics for osteomyelitis

• Confirmatory TestConfirmatory Test– A test performed to complement a previously abnormal test and increase the specificity of a A test performed to complement a previously abnormal test and increase the specificity of a

diagnosisdiagnosis– e. g. A Fluorescent Treponemal Antibody (FTA) antibody assay after a Rapid Plasma Reagin (RPR) e. g. A Fluorescent Treponemal Antibody (FTA) antibody assay after a Rapid Plasma Reagin (RPR)

antibody test is positive in a person suspected of syphilisantibody test is positive in a person suspected of syphilis

A perfect testA perfect test A real testA real test

Diagnostic Test PossibilitiesDiagnostic Test Possibilities Disease Disease

Test ResultTest Result PresentPresent AbsentAbsentPositivePositive TP TP FP FPNegativeNegative FN FN TN TN

TP TP = = True positiveTrue positiveFP FP = = False positiveFalse positiveFN FN = = False negativeFalse negativeTN TN = = True negativeTrue negative

SensitivitySensitivity Disease Disease Test ResultTest Result PresentPresent AbsentAbsentPositivePositive TP TP FP FPNegativeNegative FN FN TN TNSensitivitySensitivity– % positive tests in persons with a disease = TP/(TP + FN)% positive tests in persons with a disease = TP/(TP + FN)– Positive in Disease (PID)Positive in Disease (PID)– A highly sensitive test is (+) in “everyone” with a diseaseA highly sensitive test is (+) in “everyone” with a disease– A highly sensitive test if (–) “rules out” a diseaseA highly sensitive test if (–) “rules out” a disease– Not dependent on disease prevalenceNot dependent on disease prevalence

SpecificitySpecificity Disease Disease Test ResultTest Result PresentPresent AbsentAbsentPositivePositive TP TP FP FPNegativeNegative FN FN TN TNSpecificitySpecificity– % negative tests in persons without disease = TN/(TN + FP)% negative tests in persons without disease = TN/(TN + FP)– Negative in Health (NIH)Negative in Health (NIH)– A highly specific test is (–) in “everyone” without a diseaseA highly specific test is (–) in “everyone” without a disease– A highly specific test if (+) “rules in” a diseaseA highly specific test if (+) “rules in” a disease– Not dependent on disease prevalenceNot dependent on disease prevalence

Positive Predictive ValuePositive Predictive Value Disease Disease Test ResultTest Result PresentPresent AbsentAbsentPositivePositive TP TP FP FPNegativeNegative FN FN TN TNPositive Predictive Value (PPV)Positive Predictive Value (PPV)– % of positive results that are true positives = TP/(TP + FP)% of positive results that are true positives = TP/(TP + FP)– If test is (+), the chance the patient has the diseaseIf test is (+), the chance the patient has the disease– Dependent on disease prevalence Dependent on disease prevalence – low prevalence –> low TP –> low PPVlow prevalence –> low TP –> low PPV

Negative Predictive ValueNegative Predictive Value Disease Disease Test ResultTest Result PresentPresent AbsentAbsentPositivePositive TP TP FP FPNegativeNegative FN FN TN TNNegative Predictive Value (NPV)Negative Predictive Value (NPV)– % of negative results that are true negatives = TN/(TN + FN)% of negative results that are true negatives = TN/(TN + FN)– If test is (–), the chance the patient does not have the diseaseIf test is (–), the chance the patient does not have the disease– Dependent on disease prevalence Dependent on disease prevalence – low prevalence –> low FN –> high NPV 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 Will the sensitivity, specificity and predictive values allow it

to provide clinically useful information?to provide clinically useful information?• Will the results change the diagnosis, prognosis or therapy?Will the results change the diagnosis, prognosis or therapy?• What are the expected outcomes and why?What are the expected outcomes and why?

Terms Describing the Frequency of a FindingTerms Describing the Frequency of a Finding

• PrevalencePrevalence– Proportion of a sample/population currently with a findingProportion of a sample/population currently with a finding– ““1 per 100,000 men have gene Q”1 per 100,000 men have gene Q”

• IncidenceIncidence– Proportion of a sample/population that develops a finding Proportion of a sample/population that develops a finding

within a specified period of timewithin a specified period of time– ““15 per 1000 developed AIDS in 5 years”15 per 1000 developed AIDS in 5 years”

Bayesian AnalysisBayesian AnalysisPre– and Post–Test ProbabilitiesPre– and Post–Test Probabilities

• Pretest ProbabilityPretest Probability– The probability of a diagnosis being present before the The probability of a diagnosis being present before the

results of a diagnostic test are available.results of a diagnostic test are available.

• Posttest ProbabilityPosttest Probability– The probability of a diagnosis being present after the results The probability of a diagnosis being present after the results

of a diagnostic test are available.of a diagnostic test are available.

Using Bayesian Analysis for A Diagnostic TestUsing Bayesian Analysis for A Diagnostic Test

• BackgroundBackground– Acute intermittent porphyria (AIP) is autosomal dominantAcute intermittent porphyria (AIP) is autosomal dominant– Causes disabling abdominal pain, neuropathy and seizuresCauses disabling abdominal pain, neuropathy and seizures– Low blood porphobilinogen deaminase can be used to Low blood porphobilinogen deaminase can be used to

attempt to diagnose the disease, low level = (+) testattempt to diagnose the disease, low level = (+) test– 82% of AIP have a (+) test, sensitivity = 82%82% of AIP have a (+) test, sensitivity = 82%– 3.7% of normal persons have a (+) test, specificity = 96.3%3.7% of normal persons have a (+) test, specificity = 96.3%– Prevalence of AIP in general population = 1/10,000 (0.01%)Prevalence of AIP in general population = 1/10,000 (0.01%)

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient APatient A– Is “screened” and has a positive test, does he/she have AIP?Is “screened” and has a positive test, does he/she have AIP?– Pretest probability = 0.01%Pretest probability = 0.01%

• Filling in the blanksFilling in the blanks AIP AIP

Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositiveNegativeNegativeTotalTotal

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient APatient A– Is “screened” and has a positive test, does he/she have AIP?Is “screened” and has a positive test, does he/she have AIP?– Pretest probability = 0.01%Pretest probability = 0.01%

• Filling in the blanksFilling in the blanks AIP AIP

Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositiveNegativeNegativeTotalTotal 100 <–100 <– 999,990 <– 1,000,000 999,990 <– 1,000,000

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient APatient A– Is “screened” and has a positive test, does he/she have AIP?Is “screened” and has a positive test, does he/she have AIP?– Pretest probability = 0.01%Pretest probability = 0.01%

• Filling in the blanksFilling in the blanks AIP AIP

Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositive 36,996 36,996NegativeNegative 962,904 962,904 TotalTotal 100 <–100 <– 999,900 <– 1,000,000 999,900 <– 1,000,000

x .963x .963

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient APatient A– Is “screened” and has a positive test, does he/she have AIP?Is “screened” and has a positive test, does he/she have AIP?– Pretest probability = 0.01%Pretest probability = 0.01%

• Filling in the blanksFilling in the blanks AIP AIP Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositive 8282 36,996 36,996 –>–> 37,078 37,078NegativeNegative 18 18 962,904 962,904 –> –> 962,922962,922TotalTotal 100 <–100 <– 999,900 <– 1,000,000 999,900 <– 1,000,000• Positive Predictive ValuePositive Predictive Value– PPV = 82/37,078 = 0.22%!PPV = 82/37,078 = 0.22%!

x .82x .82 x .963x .963

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient BPatient B– Has a brother with AIP, does he/she have AIP?Has a brother with AIP, does he/she have AIP?– Pretest probability = 50%Pretest probability = 50%

• Filling in the blanksFilling in the blanks AIP AIP

Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositiveNegativeNegativeTotalTotal 500,000 <– 500,000 <– 1,000,000500,000 <– 500,000 <– 1,000,000

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient BPatient B– Has a brother with AIP, does he/she have AIP?Has a brother with AIP, does he/she have AIP?– Pretest probability = 50%Pretest probability = 50%

• Filling in the blanksFilling in the blanks AIP AIP Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositive 410,000410,000 18,500 18,500 –>–> 428,500428,500NegativeNegative 90,000 90,000 481,500 481,500 –> –> 571,500571,500TotalTotal 500,000 <– 500,000 <– 1,000,000500,000 <– 500,000 <– 1,000,000• Positive Predictive ValuePositive Predictive Value– PPV = 410,000/428,500 = 96%!PPV = 410,000/428,500 = 96%!

x .82x .82 x .963x .963

Using Bayesian AnalysisUsing Bayesian AnalysisDiagnosing Acute Intermittent PorphyriaDiagnosing Acute Intermittent Porphyria

• BackgroundBackground– Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000Sens = 82%, spec = 96.3%, prevalence = 1 in 10,000

• Patient CPatient C– Has Sx c/w a 30% chance of AIP, does he/she have AIP?Has Sx c/w a 30% chance of AIP, does he/she have AIP?– Pretest probability = 30%Pretest probability = 30%

• Filling in the blanksFilling in the blanks AIP AIP Test ResultTest Result PresentPresent AbsentAbsent TotalTotalPositivePositive 246,000246,000 26,000 26,000 –>–> 272,000272,000NegativeNegative 54,000 54,000 674,000 674,000 –> –> 728,000728,000TotalTotal 300,000 <– 700,000 <– 1,000,000300,000 <– 700,000 <– 1,000,000• Positive Predictive ValuePositive Predictive Value– PPV = 246,000/272,000 = 90%PPV = 246,000/272,000 = 90%

x .82x .82 x .963x .963