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D IAGNOSTICS AT WORK : From the clinic to the community Nitika Pant Pai, MD, MPH., PhD Madhukar Pai, MD, PhD.

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Presentation on theme: "D IAGNOSTICS AT WORK : From the clinic to the community Nitika Pant Pai, MD, MPH., PhD Madhukar Pai, MD, PhD."— Presentation transcript:

1 D IAGNOSTICS AT WORK : From the clinic to the community Nitika Pant Pai, MD, MPH., PhD Madhukar Pai, MD, PhD

2 W HY SCREENING, DIAGNOSTIC AND PROGNOSTIC TESTS MATTER o Diagnosis is the first and important step in the pathway to correct treatment “Companion diagnostics” are emerging as a critical theme o Early and rapid diagnosis can reduce morbidity, improve patient outcomes, and reduce cost of care o Tests can o identify risk factors o predict prognosis o monitor therapy over time o promote healthy behaviours o tailor therapies (i.e. personalized medicine)

3 I N THE PAST, DOCTORS RELIED ON THEIR CLINICAL INTUITION … Source: The Hindu

4 A ND SOME BASIC TESTS LIKE M ICROSCOPE Antonie van Leeuwenhoek (1632–1723), the father of microbiology, is best known for his work to improve the microscope and use it for microbiology Source: Wikipedia

5 S TETHOSCOPE AND X-R AYS Rene Laënnec in Paris invented the first stethoscope in Wilhelm Rontgen detected x-rays in Source: Wikipedia

6 T ODAY, WE HAVE COME A LONG WAY …

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8 T HE OLD STETHOSCOPE COMES OF AGE !

9 T ESTS FOR TUBERCULOSIS : FROM MICROSCOPE TO RAPID MOLECULAR TESTS (<2 HOURS )

10 T ESTS FOR HIV: FROM THE LAB TO OUR HOMES

11 Source: U LTRASONOGRAPHY : F ROM SPECIALIST TO FAMILY DOC

12 Y OUR MOBILE SMARTPHONE HAS EMERGED AS A POWERFUL NEW DIAGNOSTIC TOOL ! Retina imaging Vital signs, oxygen saturation

13 Y OUR TABLET IS NOW A HEALTH TECHNOLOGY ASSISTANT This Slate is a book-sized machine; it wirelessly communicates with an Android tablet and includes a bag of plug-and-play sensors that measure blood pressure and levels of blood sugar and hemoglobin, conduct electrocardiography (EKG) tests, etc.

14 W EARABLE H EALTH T ECHNOLOGIES : Fitbit, iWatch, OMwear RECORD SLEEP, VITAL SIGNS

15 W EARABLE H EALTH T ECHNOLOGIES Real-time glucose monitoring

16 W E ARE SO READY FOR THE T RICORDER !

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18 I NNOVATION : HIVSmart! : I NTEGRATED SMARTPHONE APP AND INTERNET BASED HIV SELF TESTING STRATEGY

19 P ROBLEM : H EALTH FACILITY BASED TESTING o Barriers: o Stigma and Discrimination o Social Embarrassment o Fear of visibility o Lack of Confidentiality o Judgemental Attitudes o About 50% people WORLDWIDE and 25% DOMESTICALLY don’t know their HIV status!! 19

20 N EED a.Confidential, Anonymous, personalized, b.convenient, non invasive, cost effective c.Easy linkages, easy conduct d.Saves time and money

21 HIV S ELF -T EST A PP : A NDROID, iP HONE 21 Copyright protected. HIVSmart! is copyright protected authored by Dr Pant Pai and trainees (Roni Deli-Houssein, Sushmita Shivkumar, Caroline Vadnais) and owned by McGill University

22 22 S MARTER C ONDUCT & L INKAGES © Pant Pai and McGill University 2013

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24 S O, WE HAVE NOW ENTERED THE BRAVE NEW WORLD OF DIAGNOSTICS ! o Faster, Easier, time savings o Real-time o Personalized o Connectivity (from your mobile to your doctor’s laptop) o At point of clinical care, at home, any place, anywhere!

25 And the world of big data 1.Data: 1.Social, medical, genetic, daily caloric intake, daily sleep, vital signs. Step count, all will be available (in real time) 2.Anonymized, compliant with confidentiality guidelines 2.Sources: 1.from electronic medical records available on your phone, to your family physician and in your health facility 2.from smart wear and Fitbits- on the internet and phones 3.from personalized genomics testing are available on the cloud 3.What for? 1.For a personalized, plan for health promotion, targeted medication intake, monitoring, and better management.

26 This is all exciting, but…

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28 H OW DO WE KNOW THESE NEW TESTS ARE ACCURATE ? T HAT THEY ACTUALLY MAKE A DIFFERENCE ?? o This is where clinical epidemiologists enter the picture. o Diagnostic tests, just like drugs and vaccines, need adequate validation before they can be used on people. o Just like drug trials, we do diagnostic trials.

29 D IAGNOSIS V S S CREENING : THEY ARE DIFFERENT ! 29 o A diagnostic test is done on sick people patient presents with symptoms pre-test probability of disease is high (i.e. disease prevalence is high) o A screening test is usually done on asymptomatic, apparently healthy people healthy people are encouraged to get screened pre-test probability of disease is low (i.e. disease prevalence is low)

30 P ROCESS OF DIAGNOSIS : ALL ABOUT PROBABILITY ! Test Treatment Threshold Threshold 0% 100% Probability of Diagnosis No Tests Need to Test Treat 30

31 T HE P ERFECT D IAGNOSTIC T EST ☻☺ X Y Disease No Disease 31

32 V ARIATIONS I N D IAGNOSTIC T ESTS ☺☻ Overlap Range of Variation in Disease free Range of Variation in Diseased 32

33 S O, CUT - POINTS MATTER A LOT ! o Many tests produce continuous numbers, and doctors tend to use cut-points to make decisions o Cut-points are a compromise – they are not perfect o Same test can produce different results, based on cut-points used o Cut-points can change over time

34 There is no perfect test! All we can hope to do is increase or decrease probabilities, and Bayes’ theorem helps with this process 34

35 B AYES ' THEORY pre-test probability post-test probability Test Post-test odds = Pre-test odds x Likelihood ratio 35 What you thought before + New information = What you think now

36 pre-test probability LOW post-test probability HIGH Test o An accurate test will help reduce uncertainty o The pre-test probability is revised using test result to get the post-test probability o Tests that produce the biggest changes from pretest to post-test probabilities are most useful in clinical practice [very large or very small likelihood ratios] pre-test probability HIGH post-test probability LOW Test Bayesian approach to diagnosis 36

37 Steps in evaluating a diagnostic test 37 o Define gold standard o Recruit consecutive patients in whom the test is indicated (in whom the disease is suspected) o Perform gold standard and separate diseased and disease free groups o Perform test on all and classify them as test positives or negatives o Set up 2 x 2 table and compute: Sensitivity Specificity Predictive values Likelihood ratios

38 Evaluating a diagnostic test Diagnostic 2 X 2 table*: Disease +Disease - Test + True Positive False Positive Test - False Negative True Negative 38

39 Disease present Disease absent Test positive True positives False positives Test negative False negative True negatives Sensitivity [true positive rate] The proportion of patients with disease who test positive = P(T+|D+) = TP / (TP+FN) 39

40 Disease present Disease absent Test positive True positives False positives Test negative False negative True negatives Specificity [true negative rate] The proportion of patients without disease who test negative: P(T-|D-) = TN / (TN + FP). 40

41 Disease present Disease absent Test positive True positives False positives Test negative False negative True negatives Predictive value of a positive test Proportion of patients with positive tests who have disease = P(D+|T+) = TP / (TP+FP) 41

42 Disease present Disease absent Test positive True positives False positives Test negative False negative True negatives Predictive value of a negative test Proportion of patients with negative tests who do not have disease = P(D-|T-) = TN / (TN+FN) 42

43 Imagine a hypothetical population (some with disease and others without) Loong T BMJ 2003;327: ©2003 by British Medical Journal Publishing Group

44 If a test was positive in everyone, what would you make of this test? Loong T BMJ 2003;327: ©2003 by British Medical Journal Publishing Group

45 What about this scenario? Loong T BMJ 2003;327: ©2003 by British Medical Journal Publishing Group

46 In reality, most tests will produce these sorts of results Loong T BMJ 2003;327: ©2003 by British Medical Journal Publishing Group

47 Example: Ultrasonography for Down Syndrome

48 N UCHAL FOLD & D OWN S YNDROME Down Syndrome YesNo Nuchal foldPositive Negative Sensitivity = 75% Specificity = 98% LR+ = 36 LR- = 0.26 N Engl J Med 1987;317:1371

49 The interpretation of this test will depend a lot on the context (i.e. prior probability) o Scenario 1: Mrs. B, a 20-year old woman with a previous normal pregnancy, seen at a community hospital o Scenario 2: Mrs. A, a 37-year old woman with a previous affected pregnancy, seen at a high-risk clinic in a tertiary, referral hospital o Who has a higher pretest probability of Down syndrome?

50 T AKE HOME MESSAGES o Diagnostic tests are important and can help all of us o But, they are not perfect o Diagnostic and screening tests are very different and should not be confused o Doctors and patients need to understand that all tests have their inherent error (i.e. false positives and false negatives) o Tests should always be interpreted in context o Tests should be avoided unless there is a clear indication

51 T HANK YOU !!! We are grateful to: Prof Jim Hanley Prof Abe Fuks Mini-Med team: Elise, Gloria, Ibby & Kappy Department of Epidemiology, Biostatistics & Occupational Health Slide credits: Sehar Manji, Executive Assistant


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