Probabilities in Clinical Medicine

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Probabilities in Clinical

Medicine

Mohammad Saifur Rohman, MD.PhD.Interventional Cardiologist

Department of Cardiology and Vascular Medicine

Faculty of Medicine, Brawijaya University/dr !aiful "nwar #ospital, Malang



Evidence based Medicine

The integration of: Best $esearc% &vidence ith

Clinical &'pertise and

(atient Values

Sac!ett et al., "###



Evidences

$esearc% &vidence: clinicall$relevant research, sometimes frombasic sciences, often from clinicalresearch studies e%amining

Diagnostic tests

Mar!ers of &rognosis

Safet$ and e'cac$ of treatment

Rehabilitative or &reventive regimens



Patient oriented

(atient Values: the uni(ueconcerns, e%&ectations and

&references each &atient brings tothat &articular clinical encounter.

)ncor&orate these into clinical*

decision ma!ing as &art of ourcollaborative treatment ith the&atient.



S!ills

Clinical &'pertise: using ourclinical s!ills and &ast e%&erience

to identif$ health states,diagnosis, ris!s and bene+ts forindividual &atients,

and integrate their )



Reasoning nder ncertaint$E%am&le: Medical Diagnosis

• ncertaint$ is inherent to medicalreasoning – Relation of diseases to clinical and

laborator$ +ndings is &robabilistic – Patient data itself is often uncertain ith

res&ect to value and time – Patient &references regarding outcomes

var$ – Cost of interventions and thera&$ can

change



-n E%am&le: Chest &ain

• &ressure, tightness, or heaviness, strangling,constricting, or com&ression

• burning

• Indigestion, *elc%ing, dyspnea• "ngina + - s$ndrome resulting from m$ocardial

ischemia. Demand and su&&l$ imbalance

• Careful histor$ ta!ing mode of onset, location,(ualit$ of &ain, duration, &reci&itating factors,&attern of disa&&earance, ris! factor,



Tro&onin in -M), relative to onsetand Disease Severit$



Patient &reference tooutcomes

• /verestimate

• nderestimate

• 0o&eful• 0o&eless



Cost E1ective Treatment

• Correct diagnosis

• Pro&er &atient

• Best choice• 2easible

• Ris!3bene+t



4hat is Diagnosis5

“The anatomic, biochemical,physiologic, or psychologic

derangement”

DIAGNOSIS

Labeling

Pathology



4hat is Diagnosis5

“Diagnosis is the term hich namesthe primary dys!"nction toard hich

the physical therapist directstreatment” 6Sahrmann, 7898

DIAGNOSIS

Planning

Treatment



Three Strategies of Clinical

Diagnosis

• Pattern recognition

• Com&lete histor$ and &h$sicale%amination

• 0$&othetico*deductivestrateg$



Pattern Recognition

• )nstantaneous reali;ation that the&atient conforms to a &reviousl$

learned &attern of disease• suall$ re<e%ive, not re<ective• suall$ cannot be e%&lained to

others• -rgued to be =learned> on &atients

and not =taught> in lecture halls



Com&lete 0istor$ and Ph$sical6E%haustion

• The &ain*sta!ing search for 6but

&a$ing no immediate attention toall the facts about a &atient.

• Method of a novice

•)m&ractical and ine'cient



0$&othetico*Deductive

Method• The formulation, from the earliest

clues of a =short list> of &otential

diagnoses.• Subse(uent tests are &erformed

hich ill most li!el$ reduce the

length of the list.• Re(uires an understanding of

&robabilit$.



?athering Diagnostic Data for a0$&othesis*Driven -&&roach

• Com&lete versus e%haustive datagathering

• Must !no hat is good data

• The im&ortance of con+rmator$ anddisconrmatory

data• Rarel$ is one test su'cient



-&&raising the @iterature RegardingDiagnostic Tests

• The e1ectiveness of a h$&othesis*

driven a&&roach hinges ona&&ro&riate selection andinter&retation of diagnostic tests.

• The clinician must be able toa&&raise the literature regardingdiagnostic tests.



-&&raising the @iterature Regarding

Diagnostic Tests

#ondition Present #ondition Absent

Test Positi$e

Test Negati$e

Tr"ePositi$e

Tr"eNegati$e

2alseAegative

2alsePositive




Diagnostic Tests

• Characteristics of ?ood

Studies: – )nde&endent ?old Standard

–/&erational De+nitions

–

Re&resentative Subects



Test Characteristics

Disease

Test result

Diseasepresent

Diseaseabsent

Total

Positive

True

positive(TP)

False

positive(FP)

TP+FP

Negative

Falsenegative

(FN)

Truenegative

(TN)

FN+TN

TP+FN FP+TN



Test PerformanceMeasures

• The gold standard test: the &rocedure that de+nes&resence or absence of a disease 6often, ver$ costl$

• The inde% test: The test hose &erformance ise%amined

•

Tr"e positi$e rate 6TP& Sensiti$ity: – P6Test is &ositive&atient has disease P6TD – Ratio of number of diseased &atients ith &ositive

tests to total number of &atient: TP36TP2A•

Tr"e negati$e rate 6TN& Speci'city – P6Test is negative&atient has no disease P6T*

D* – Ratio of number of nondiseased &atients ith

negative tests to total number of &atients: TA3

6TA2P



Sensiti$ity (Tr"e Positi$e

&ate)

– Proportion o! patients ith the

condition ho ha$e a positi$e testres"lt

– Tests ith high sensiti$ity ha$e!e !alse negati$es, there!ore a

negati$e res"lt rules out thecondition



Test Predictive Falues

• Positive &redictive value 6PF P6DT TP36TP2P

• Aegative &redictive value 6PF* P6D*T* TA36TA2A



Speci'city (Tr"e Negati$e &ate)

– Proportion o! patients itho"t thecondition ho ha$e a negati$e test

res"lt – Tests ith high speci'city ha$e !e

!alse positi$es, there!ore a positi$eres"lt rules in the condition*




Diagnostic Tests

• @i!elihood ratios combine the

information contained insensitivit$ and s&eci+cit$ values.

• Permits com&arisons among

com&eting tests.




• Positive @i!elihood Ratio:E%&resses the change in odds

favoring the disorder given a&ositive test.6Sensitivit$367*S&eci+cit$

• Aegative @i!elihood Ratio:E%&resses the change in oddsfavoring the disorder given anegative test.

667*Sensitivit$ 3S&eci+cit$




• 4hat characteri;es a good test5 – @arge @R 6GH.#

•

change the odds favoring the diagnosisgiven a test• hel&ful for ruling in the condition.

– Small *@R 6I#.J#• reduce the odds favoring the diagnosis

given a * test• hel&ful for ruling out the condition.



-n E%am&le from the@iterature

• -ll tests had higher s&eci+cit$ thansensitivit$, therefore each is better as

a rule in test.• The &osterior draer test has a high

@R, and small *@R, ma!ing it an

e%cellent diagnostic test



)ntegrating Diagnostic )nformationinto Practice

I! Data

+%ists

I! DataDoes Not

+%ist

FIND IT!!

COLLECTIT!!



)ntegrating Diagnostic )nformationinto Practice

• hat -o" Need To Do.

–Decide hat yo" are

diagnosing

–List all possible $ariables

–Decide on the “goldstandard”

–Measure Everyone !!



-n E%am&le

You are in charge of screeningresidents of a long-ter carefacility for those ho need thera"ydue to increased ris# of falling$hat are yo" diagnosing / &is0 o!

!alling

hat are the possible predictors1hat ill be the gold standard o! !all

ris01

2ollo/"p e$eryone



The Cut*o1 Falue Trade o1

• Sensitivit$ and s&eci+cit$ de&end on thecut o1 value beteen hat e de+ne asnormal and abnormal

•-ssume high test values are abnormalthen, moving the cut*o1 value to a higherone increases 2A results and decreases 2Presults 6i.e. more s&eci+c and vice versa

• There is ala$s a trade o1 in setting thecut*o1 &oint



Receiver /&erating Characteristic6R/C Curves: E%am&les



Ba$esian Diagnostic S$stem E%am&le:de DombalKs -bdominal*Pain S$stem 678L"

• Domain: -cute abdominal &ain 6L &ossible diagnoses• )n&ut: Signs and s$m&toms of &atient• /ut&ut: Probabilit$ distribution of diagnoses• Method: Aave Ba$esian classi+cation• Evaluation: an eight*center stud$ involving "H#

&h$sicians and 7N,LJL &atients• Results:

– Diagnostic accurac$ rose from ON to NH – The negative la&arotom$ rate fell b$ almost half – Perforation rate among &atients ith a&&endicitis fell b$ half – Mortalit$ rate fell b$ ""

• Results using surve$ data consistentl$ better than thecliniciansK o&inions and even the results using human

&robabilit$ estimatesQ



Than! ou

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Probabilities in Clinical Medicine

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