DIAGNOSTIC TESTS

Modul Riset

Laboratory study :

- Evaluation of diagnostic tests- Experimental study : - In vitro - In vivo

References:- Pusponegoro HD, et al. Uji Diagnostik. In: S. Sastroasmoro & S.Ismael (Ed.). Dasar-dasar Metodologi penelitian klinis. Edisi ke-2. Jakarta: CV Sagung Seto, 2002.- Warren S, et al. Designing a New Study III: Diagnostic Test. In: SB Hulley and SR Cummings (Eds). Designing a Clinical Research Baltimore: Williams and Wilkins, 1988.

QUESTIONS

Is Real Time-PCR useful in the diagnosis of dengue infection?

Among patients with hypertension, is a serum creatinin level useful in the diagnosis of renovascular disease?

How good can USG predict breast cancer in patients with breast tumor?How well a diagnostic test can differentiate the presence or absence of a disease.

Purpose of diagnostic tests

1. Diagnosis or exclusion of disease- The tests must be: sensitive and specific

2. Screening of disease among asymptomatic persons. Additional test will be needed to confirm early diagnosis.

The tests are useful when:- Prevalence of disease is high- Significant morbidity/mortality without treatment- Effective therapy is available- Early treatment gives better outcome.

Purpose of diagnostic test (contd.)

3. Part of the treament- To monitor disease/ treatment progress- To identify complication`- To monitor drug level - Determine prognosis- To confirm indeterminate tests.- Characterization of causative microorganism (e.g. drug resistance, genotype) Important : reproducibility

4. Epidemiology study

Structure

They have : - Predictor variable (the test results) - Outcome variable (presence or absence of disease)

The test results as the predictor variable dichotomous (positive or negative) ordinal (++++, +++, ++, + or -) interval (mg of glucose/ mL)Note: ordinal and interval scales must be changed to dichotomous scale by using a cutoff pointThe disease as outcome variable Absence or presence of disease Determined by gold standard

Disease statusBreast cancer benign noduleTest resultPositif 65 30 Negative 35 70 Analysis with 2x2 p< 0.001 Statistically the positive result is highly correlated with presence of disease.But this test cannot well differentiate absence or presence of disease. 2x2 table100100

Sensitivity Is the proportion of subjects with the disease who have positive test.A test is called highly sensitive if it shows positive results in all patients with the disease.So, it indicates how good a test is at identifying the diseased

SpecificityIs the proportion of subjects without the disease who have a negative test.A test is called specific if it shows negative results in all patients without the disease.So, it indicates how good a test is at indicating the nondiseased

Positive predictive value (PV+) = the probability that a person with a positive result actually has the disease. - Negative predictive value (PV-) = the probability that a person with a negative result actually doesnt have the disease

Disease statusBreast cancer Benign noduleTest resultPositif 65 30 Negative 35 70 True positive : the test is positive & the patient has the disease (65) False positive : the test is positive but the patient doesnt have the disease (35) True negative : The test is negative &the patient doesnt have the disease (70) False negative : The test is negative but the patient has the disease (30)

DISEASEYESNOTOTALTEST RESULTYESTRUE POSITIVEFALSE POSITIVETP + FPNOFALSE NEGATIVETRUE NEGATIVEFN + TNTOTALTP + FNFP + TNTP + FP + FN + TN

Sensitivity = A : ( A+C) = 90%Specivicity= D : (B + D)= 80%Positive predictive value= A : (A + B)= 82%Negative predictive value = D : (C + D)= 89%

GOLD STANDARDPOSITIVENEGATIVETOTALTEST RESULTPOSITIVEA(45)B(10)A + B(55)NEGATIVEC(5)D(40)C + D(45)TOTALA + C(50)B + D(50)A + B + C + D

Disease statusBreast cancer benign nodule TotalTest resultPositif 70 25 95 Negative 30 75 105 Sensitivity = ?Specivicity= ?PV+= ?PV- = ?Total 100 100 200

Disease statusBreast cancer benign nodule TotalTest resultPositif 70 25 95 Negative 30 75 105 Sensitivity = A : ( A+C) = 70%Specivicity= D : (B + D)= 75%PV+= A : (A + B)= 73.7%PV- = D : (C + D)= 71.4%Total 100 100 200

Steps in diagnostic test researchIdentify why a new diagnostic test is necessary- How good is the present available diagnostic test? Any weakness? Can a new method overcome the weakness of the old one?

Determine the main purpose of the new test. - To screen? --- high sensitivity - To confirm diagnosis? --- high sensitivity and specificity - To exclude? ---- high specificity

Select subject population.- Screening / Case finding/ Diagnosis- Location- Sample number- Inclusion criteria

Select gold standard- The best available diagnostic testDo the test- BlindedData analysis and report- Sensitivity, Specificity, PV+, PV-. With confidence interval- ROC for continuous data

GOLD STANDARD Standard method to determine presence or absence of disease Ideally : always positive for diseased person, and always negative for non-diseased person ---- rare, if any ----- use the best method available One or combination of methods Its sensitivity and specificity should not lower than the new method to be tested.

Cutoff When data are in ordinal or numeral (continuous) scale, we have to decide the point that differentiate normal and abnormal. Depends on the purpose of the test, need high sensitivity or high specificity. E.g : for screening : high sensitivity. To decide whether a patient has to undergo a high-risk surgery : high specificity.

Sensitivity1 - Specificity0 0.2 0.4 0.6 0.8 1.00.20.60.80.41.0xxxxxReceiver operator curve A graph that show the bargain between sensitivity and specificity when we determine a cut-off point. Increase sensitivity - decrease specificity, vice versa. Points closer to diagonal line worse result Selection of cut-off point depends on the purpose of the test.ABCD

Receiver operator curve (ROC) for serum alanine aminotransferase (ALT)Level (U/L) in the diagnosis of hepatitisSensitivity1 - Specificity0 0.2 0.4 0.6 0.8 1.00.20.60.80.41.0x20010025xx40050

The value of diagnostic test also depends on prevalence of the disease in the population being tested.

Prevalence decrease less likely that someone with a positive test is actually has the disease the more specific a test must be in order to be clinically useful.

Prevalence = (A+C) : (A+B+C+D) = 50%Sensitivity = A : ( A+C) = 90%Specivicity= D : (B + D)= 80%PV+= A : (A + B)= 82%PV- = D : (C + D)= 89%

GOLD STANDARDPOSITIVENEGATIVETOTAL

TEST RESULTPOSITIVEA(45)B(10)A + B(55)NEGATIVEC(5)D(40)C + D(45)TOTALA + C(50)B + D(50)A+B+C+D(100)

Prevalence = 20%Sensitivity = A : ( A+C) = 90%Specivisity= D : (B + D)= 80%Nilai duga positif= A : (A + B)= 55%Nilai duga negatif = D : (C + D)= 97%

GOLD STANDARDPOSITIVENEGATIVETOTALTEST RESULTPOSITIVE181634NEGATIVE26466TOTAL2080100

Likelihood ratio

= the likelihood that a person with a disease would have a particular test result divided by the likelihood that a person without the disease would have that result.This especially useful when a test result is categorical or continuous.

Positive Likelihood ratio = a/(a+c) : b/(b+d) = sensitivity : (1-specificity)

Negative Likelihood ratio = c/(a+c) : d/(b+d) = (1-sensitivity) : specificity

Limitations1. Random error- by chance- quantifiable ---- confidence interval.

Systematic error2.1. Sampling bias :- When thestudy sample is not representative of the target population in which test will be used.2.2. Measurement bias :- Increase when the person determine the test result have already known the outcome of gold standard.- borderline result --- determine in advance how to treat this result.2.3. Reporting bias- Unpromising results usually go unreported. ------ enough samples, so negative results can be meaningful and reported.

SummaryA diagnostic test study determines the usefulness of a test in the diagnosis of a disease. Good tests are those that distinguish the diseased from the non-diseased, and are safe, quick, simple, painless, reliable, and inexpensive. Randomized blinded trial and usual clinical practice as model is important in diagnostic test study.In diagnostic test study there is a predictor variable (test result) and an outcome variable (the disease, determined by gold standard). The goal is to describe how strong the association is, in terms of its sensitivity and specificity.

The investigator should determine the sensitivity, specificity, predictive value of positive test, predictive value of negative test. A cutoff point must be determined for calling a result positive.Studies of diagnostic tests are subject to several biases; the most important are sampling bias, measurement bias, and reporting bias.Steps in planning diagnostic test study : a) Identify why a new diagnostic test is necessary; b) Determine the main purpose of the new test; c) Select subject population; d) Select gold standard; e) Apply the test and the gold standard bilndly; f) Analyse and report data in terms of sensitivity, specificity and predictive value.

Thank you

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