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:
D IAGNOSTICS AT WORK : From the clinic to the community Nitika Pant Pai, MD, MPH., PhD Madhukar Pai, MD, PhD
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)
I N THE PAST, DOCTORS RELIED ON THEIR CLINICAL INTUITION … Source: The Hindu
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
S TETHOSCOPE AND X-R AYS Rene Laënnec in Paris invented the first stethoscope in Wilhelm Rontgen detected x-rays in Source: Wikipedia
T ODAY, WE HAVE COME A LONG WAY …
T HE OLD STETHOSCOPE COMES OF AGE !
T ESTS FOR TUBERCULOSIS : FROM MICROSCOPE TO RAPID MOLECULAR TESTS (<2 HOURS )
T ESTS FOR HIV: FROM THE LAB TO OUR HOMES
Source: U LTRASONOGRAPHY : F ROM SPECIALIST TO FAMILY DOC
Y OUR MOBILE SMARTPHONE HAS EMERGED AS A POWERFUL NEW DIAGNOSTIC TOOL ! Retina imaging Vital signs, oxygen saturation
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.
W EARABLE H EALTH T ECHNOLOGIES : Fitbit, iWatch, OMwear RECORD SLEEP, VITAL SIGNS
W EARABLE H EALTH T ECHNOLOGIES Real-time glucose monitoring
W E ARE SO READY FOR THE T RICORDER !
I NNOVATION : HIVSmart! : I NTEGRATED SMARTPHONE APP AND INTERNET BASED HIV SELF TESTING STRATEGY
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
N EED a.Confidential, Anonymous, personalized, b.convenient, non invasive, cost effective c.Easy linkages, easy conduct d.Saves time and money
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
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!
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.
This is all exciting, but…
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.
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)
P ROCESS OF DIAGNOSIS : ALL ABOUT PROBABILITY ! Test Treatment Threshold Threshold 0% 100% Probability of Diagnosis No Tests Need to Test Treat 30
T HE P ERFECT D IAGNOSTIC T EST ☻☺ X Y Disease No Disease 31
V ARIATIONS I N D IAGNOSTIC T ESTS ☺☻ Overlap Range of Variation in Disease free Range of Variation in Diseased 32
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
There is no perfect test! All we can hope to do is increase or decrease probabilities, and Bayes’ theorem helps with this process 34
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
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
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
Evaluating a diagnostic test Diagnostic 2 X 2 table*: Disease +Disease - Test + True Positive False Positive Test - False Negative True Negative 38
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
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
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
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
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
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?
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
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