Diagnostic Research, Sept 2013

8/11/2019 Diagnostic Research, Sept 2013

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PRINCIPLES OF

DIAGNOSTIC RESEARCH

Siti Setiati


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Objective

At the end of the lecture you will understand:

What is Diagnostic Research? Why Performing Diagnostic Research?

How to Apply Diagnostic Research in Practice?

How to Design Diagnostic Research?


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WHAT?

Type of Research

Question

Descriptive/Causal Aim

Diagnostic research Descriptive

Predict the probability of presence of target

disease from clinical and non-clinical profile

Prognostic research DescriptivePredict the course of disease from clinical an

d non-clinical profile

Etiologic research Causal

Causally explain occurrence of target disease

from determinant

Intervention research Causal & Descriptive

(1) Causally explain the course of disease as

influenced by treatment

(2) Predict the course of disease given

treatment (options) and clinical and non-

clinical profile

Major Types of

Epidemiological Research


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To assess the value of combinations of

diagnostic determinants in diagnosing a

particular disease

To assess thevalue of novel diagnostics tests

in addition to readily available tests (such as

signs and symptoms)

WHAT?

Aim of Diagnostic Research


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Challenges of Diagnostics in Practice Physicians are faced with multiple diagnostic

challenges

Diagnostic approach is always done when dealingwith patient complaints (symptoms and signs)

The aim is to interpret the symptoms, signs, andresults of other diagnostic test so that a diagnosis

can be established

Diagnosis will directs the physician in makingdecisions for appropriate management

WHAT? WHY?


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Why diagnose? Basic medical handling

Determines treatment choice

Gives information about prognosis

Physician should:

Quicklyand efficiently determine the correctdiagnosis

WHAT? WHY?


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Steps in Making Diagnosis in Practice

WHAT? WHY?

Assess which diagnostic determinants (and inwhat order) best predict the presence ofdisease?

Starts with patient with clinical problem(presenting a complaint symptom and/orsign)

Symptom/sign --> suspicion of particulardisease

Eg: a 27 years old man with acute feverand ptechiae DHF?


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Steps in Making Diagnosis in Practice

(2)

WHAT? WHY?

Starts with a patient presenting a complaint

suggestive of a certain disease to be diagnosed

The subsequent work up is a multivariableprocess involving multiple diagnostic

determinants

Ruling in or out a diagnosis is a probabilistic

action, the probability is continuously updated

based on subsequent diagnostic results


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Common Diagnosis Process:

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Diagnosis Processmultivariable concern

= continuous adding info + updating probability

History + physical

examination

Simple lab

(blood/urine)

Electrophysiology

(ECG/EEG)

imaging

DIAGNOSIS

WHAT? WHY?


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Why not performing all tests?

WHAT? WHY?

Invasive (for patient and budget)

Unnecessary: different test results give same info

However: In practice often more tested thannecessary!

What diagnostics truly necessary ?

Thats why we need scientific

DIAGNOSTIC RESEARCH!!!


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Diagnostic Research

WHAT? WHY?

The aim is to predict (not to explain why certain

symptoms/signs lead more to a certain diagnosis)the probability of presence of target disease fromclinical and non-clinical profile (diagnosticdeterminants)

Determinants: element of clinical and non clinical profiles (signs,symptoms, and possible test results)

Outcome : the diagnosis of the disease


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Example

WHAT? WHY?

Research question:

What is the value of

Procalcitonin (PCT)

in addition to signs and

symptoms when diagnosingsepsis in elderly presenting with

fever in primary care?

PCT GOLD STANDARD

Sepsis+ Sepsis -

Sepsis

+

a c

Sepsis

-

b d

Question: what are Determinants and Outcome?

Sepsis in elderly = f ( PCT, body temperature, symptoms, signs...... )


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DIAGNOSTIC RESEARCH DIAGNOSTIC TEST

Start with a patient come withsymptoms & signs suggesting the

disease Multivariable (involve multiple

tests)

Domain: patients suspected of aparticular disease based on

symptoms and signs presented

Presence of hierarchy of diagnostictesting in practice

Start with non-symptomaticindividual

Univariable, to assess whethera single diagnostic test able todiscriminate the presence orabsence of certain disease

Does not includerepresentatives of relevant

patients domain (patient withsymptoms and signs suggestiveof disease)

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WHAT? WHY?


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Diagnostic Test VS Diagnostic Research No information for doctor

or patient

No predictive value

Not important in clinicalpractice

The aim is to estimate the

probability of disease from the

diagnostic results, Predictive

Values is important.

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WHAT? WHY?

Sensitivity

If the disease present, what is the probability that the test result will bepositive

Specificity

If the disease is absent what is the probability that the test result will benegative

Predictive value

If the test positive, what is the probability that the disease will be present

S

OMEIMPORTANT

TERMS


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Before designing Diagnostic Research

One should ...

Understand clinical practice

Know clinical practice

Preferably both

WHAT? WHY? HOW TO APPLY?

Understanding application of diagnostic research in clinical

practice is important requirement before designing one


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Clinical scenario

A 27-year old male came with acute fever and

ptechiae in both arms.

What are the most possible diagnosis for this

patient?



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Differential diagnosis

- Dengue Hemorrhagic Fever

- Malaria

- Chikunguya

- Disseminated Intravascular Coagulation

- Idiopathic Thrombocytopenic Purpura

What is the most important diagnosis?

Which one does the physician does not want to miss?



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Most important diagnosis:

DENGUE HEMORRHAGIC FEVER

If missed: often fatal



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Suppose: 20% of all patient on the ER withptechiae have DHF 20% with disease in that population = prevalence

Prior-probability : probability of the presence of the targetdiagnosis (ie. DHF) before any tests (incl. Symptoms & signs)are performed.

What is your decision for the patient in thiscase?



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Decision for patient in case

Prior-probability too low to treat

Prior-probability too high to send home

Decision: reduce uncertainty diagnostics

What is the best test?



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Best test:

VIRUS CULTURE



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Diagnostics in Practice

Gold standard: Expected to detect the true disease status; as the truth (to

detect the disease correctly) However, the 24 carat status is not exist

Used as the referrence standard/test

How to perform a reference test for

everybody (=every patient in ER withptechiae)?



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Reference test for everybody?Reference test for everybody?Unethical too invasive/risky

Inefficient too expensiveDo not perform unnecessarily

How should we then determine theprobability of disease presence and whatwould be ideal?



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How then? Simpler diagnostics: Usually anamnesis, physical exam, simple lab

tests, imaging, etc.

Ideal: diagnosis without reference test

Diagnostic process in practice: Stepwise process: less more invasive

Not one diagnosis based on 1 test Each item: separate test



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Suppose:

after anamnesis & physical examinations: 10% probability of DHF

Probability of disease given test results = posterior-probability

The bigger the difference between prior and posterior probability, thebetter the diagnostic value of the tests

Our decision for patient in case: probability is too high tosend home --> next step?



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Next step

Additional research, e.g.

blood tests (leucocytes,

CRP, sedimentation, etc.)



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Suppose: 1% posterior-probability after anamnesis, PE+

simple lab tests posterior probability low enough to

send home

Ideal diagnostic process: simple tests reduce posterior probability

to 0 or 100% (without reference)

Most often physician continues testing until sufficiently sure

(approximation of 0 or 100%)

Choose when sufficiently sure: depends on prognosis of disease if

untreated + risks/costs treatment



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Diagnostics in Practice

To Summarize:

What does diagnosing involve in practice?

Estimation of probability of disease presence based on test

results of the patient

HOW TO


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Study design: three components

1. Theoretical design

2. Design of data collection

3. Design of data analysis

WHAT? WHY? HOW TO APPLY?HOW TO

DESIGN?

HOW TO


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Three Components of Research Design

Theoretical design:

Design of the occurrence relation Design of data collection:

design of the conceptual collection of data

Design of data analysis:a description of the data and quantitativeestimates of association


DESIGN?

HOW TO


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1. Theoretical Design

The theoretical design of a study starts from a researchquestion

Formulating the research question is of critical importance asit guides the theoretical design

The occurrence relation is central to the theoretical design, isthe object of research and relates one or multipledeterminants to an outcome

The occurrence relation of diagnostic research:

P(D)= f(T1,T2,T3,----Tn)P(D): the probability of the disease

T1 to Tn: the multiple diagnostic test results


DESIGN?

HOW TO


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Theoretical design

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Example:

Which simple tests contribute to the estimation of DHF

present in an adult coming to an ER with ptechiae

% DHF = F ( fever, myalgia, platelet count, haematocrite,etc)

No confounding! (causality is of no concern in diagnostic

research, the motive is not to explain the diseaseoccurrence, but only to predict)


DESIGN?


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Theoretical design outcomeOutcome

Define criteria presence/absence target disease

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Requires gold standardnone 24 carat ( none have 100% sensitivity and

specificity)

the one used when doubt remains (conform practice)better name: reference test (best available test in

current practice )

HOW TO


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Theoretical design outcome

Case: virus culture

Other: NS-1 Antigen

Blind observer of reference test

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DESIGN?

HOW TO


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Theoretical design domain

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Domain:

Pertains to the applicability/generalisability ofthe results targeted population (NOT the

study population)

The population in which the results of the

study will be applied


DESIGN?

HOW TO


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Domain

Implications for in-/exclusion criteria

how detailed (pragmatic research)?

Typically:

patients suspected of ........

patients presenting with..... (ie. Symptoms)

patients presenting with symptoms suggestive of ....in whom additional tests are being considered?

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Theoretical design

domain


DESIGN?

HOW TO


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2. Design of Data Collectiona. Proper study design

b. In-/exclusion criteria patients

c. Assessment determinants

d. Assessment outcome

e. Sample size

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DESIGN?

HOW TO


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Answer The Following Questions

1. What is the Study Design ?

2. What is the inclusion/exclusion criteria of thepatients?

3. What is the assessment of the determinants?

4. What is the outcome?5. Sample size?

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DESIGN?


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Design of Data Collection

a/. Proper study design: Cross sectional

Typical study design of diagnostic research

Diagnostic determinants and reference test aredetermined at t0 (same time)

Even when all tests and definitive diagnosis

(outcome) takes some time (interest is at t0 !)

Natural working method of doctors !

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3. Design of Data Analysis

Step 1: Estimate the Prior Probability

Step 2: Univariable Analysis(to keep in touch with data)

Step 3: Multivariable analysis

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Design of Data-Analysis

STEP 1 : Prior probability ?

200 patients with ptechiae at ER

Virus culture positive (DHF+) n=40

Virus culture negative (DHF-) n=160

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STEP 2: univariable analysis per test result

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10 C

40 160

80 D 90

200

30 A 80 B 110

DHF+ DHF- Total

Y(+)

ptechiae

N (-)

Predictive value ?

27%


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STEP 2: univariable analysis per test result

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10 C

40 160

80 D 90

200

30 A 80 B 110

BM+ BM- Total

Y(+)

ptechiae N (-)

Sensitivity ?

Specificity ?


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STEP 3: Multivariable analysis Logistic regression modeling (multivariate)

Sum of regression coefficients for each patient Leads to risk score (clinical score)

Calculate the risk score

- Which variables did the researchers select for the multivariate

analysis- For patients with mialgia and fever?

- For patients with headache ?

- Calculate predictive value for a patient with headache. (Use table 4

Rietveld paper)

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ROC (Receiver Operating Characteristic) area- the area under the the ROC curve reflects the overal discriminativevalue of the model,

exhibits the extent to which the model can discriminatebetween subjects with and without disease

- the more the ROC curve is in the left upper corner, the higherthe AUC

no clinical meaning, ROC area only reflects the overal

discriminative value of a model, no absolute disease probabilities

no info on individual probabilities of D (predictive values)

requires threshold (weighing falsepositives and negatives)

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Design of data-analysis

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ROC Curve

1 - Specificity

1,00,75,50,250,00

1,00

,75

,50

,25

0,00

*STARD initiative, Bossuyt etc. BMJ 2003


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WHAT? WHY? HOW TO APPLY?HOW TO TAKE HOME


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Take-Home Messages

Diagnosis = estimating probability disease presence

Absolute risk

Stepwise multivariable proces (none based on 1 test)

Added value of test is relevant

Diagnostic research is prediction (descriptive) research

Confounding is no issue

Subjects selected on symptoms or signs (suspicion)

Probability disease given combinations of test results

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WHAT? WHY? HOW TO APPLY?DESIGN? MESSAGE


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Thank You

Diagnostic Research, Sept 2013

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