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11 Diagnostic versus Classification Criteria: a continuum Hasan Yazıcı University of Istanbul.

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Presentation on theme: "11 Diagnostic versus Classification Criteria: a continuum Hasan Yazıcı University of Istanbul."— Presentation transcript:

1 11 Diagnostic versus Classification Criteria: a continuum Hasan Yazıcı University of Istanbul

2 I have no conflicts of interest.

3 3 Plan & Summary Pre-test disease odds Classification Diagnosis

4 4

5 Ottawa Ankle Rules IG Stiell et al. Ann Emerg Med 1992

6 66 EULAR/ACR Vasculitis Criteria Group (2008) “Perhaps the most robust criteria pertain to Behçet’s disease, where international collaboration has led to a validated proposal effective for both clinical and research purposes.” “The challenge of producing validated diagnostic criteria for these heterogeneous diseases that are suitable for use in clinical research as a classification tool, is a formidable one. However, as the international community has exemplified with Behçet’s disease, it is achievable.”

7 77 International Study Group Diagnostic Criteria (Lancet, 1990) Oral ulcers (~100%) Oral ulcers (~100%) + + Two of below: Two of below: a.Genital ulcers (80%) b.Skin lesions (80%) c.Eye lesions (50 %) d.Pathergy (50 %) Classification

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11 11 Methods 914 unselected (366 from Iran from France) patients with BS from 12 centers in 7 countries 28 BS patients without OU excluded Control group: 308 patients with connective tissue diseases, including 97 with OU only Study questionnaire based on previously published BS criteria sets Individual disease features of a random sample (60%) were compared with those in the control group. Sensitivities, specificities, log likelihood ratios and expected weights of evidence were calculated for each symptom or sign. Features were added to the criteria set up to the point at which there was no further contribution to the criteria by the expected weight of evidence of a particular disease feature. The new criteria set were further validated in the 40% sample.

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14 14 Turing’s expected weight of evidence It is the sum of the individual weights of evidence (log e LR). It represents the weight of a question while the answers make up individual weights of evidence. DJ Spiegelhalter Clin Gastroenterol 1985

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18 18 Issues apart from the – by definition- circular nature of the exercise The initial internal validation was, in fact, an internal check of the initial randomization. The questionnaire did not seek for the frequency of various features of diseases that made up the control group, among the BS patients. All work and validations were retrospective. No attempt at confidence intervals.

19 1919 ACR Vasculitis Criteria* CHCC Vasculitis Criteria ** Among 198 patients (51 with vasculitis) the positive predictive value of ACR criteria ranged 17% - 29% (JK Rao et al Ann Int Med 1998) CHCC criteria correctly identifed 8/27 patients with Wegener’s and 4/12 with MPA (SF Sorensen et al Ann Rheum Dis 2000)

20 2020 Hunder GG. The Use and Misuse of Classification and Diagnostic Criteria for Complex Diseases. Ann Intern Med 1998

21 21 ACR Classification criteria RA SLE Sjogren Vasculitis Fibromyalgia JRA Osteoarthritis Scleroderma

22 22 The “circular” logic in diagnostic/classification criteria for conditions which lack a specific laboratory or a histological feature JF Fries Arch Intern Med 1984 A perpetual motion machine, 1660 Wikipedia

23 23 Pre-test disease odds Classification Diagnosis

24 2424 Bayes theorem in c/d post-test odds = pre-test odds x LR LR = likelihood ratio

25 2525 Bayes theorem in c/d post-test odds = pre-test odds x LR LR = likelihood ratio

26 2626 Bayes theorem in c/d Pre-test odds post-test odds = pre-test odds x LR actual disease prevalance (when c/d) or frequency of disease manifestations in the disease studied vs. in diseases that come into the differential diagnosis (when formulating LR’s)

27 2727 Bayes theorem in c/d post-test odds = pre-test odds x LR LR = likelihood ratio

28 2828 Bayes theorem in c/d post-test odds = pre-test odds x LR LR = likelihood ratio c/d criteria

29 2929 Bayes theorem in c/d Likelihood ratio (LR) post-test odds = pre-test odds x LR + LR = sensitivity/1-specificity or % true positives/% false positives - LR = 1-sensitivity or % true negatives/% false negatives

30 30 The Circular Nature We use elements of pre-test odds (es) to estimate the LR’s. From these LR’s we estimate the classification LR (criteria). We use another (but related!) pre-test odds to estimate the post-test odds (c/d).

31 3131 Fulfilling the ISBD Criteria: What does it mean? (sensitivity = 90%; specificity =95%) Criteria + (+LR) : Criteria + (+LR) : 0.90/ = 18 x pre-test odds 0.90/ = 18 x pre-test odds Criteria - (-LR) : /0.95 = 0.10 x pre-test odds Criteria - (-LR) : /0.95 = 0.10 x pre-test odds

32 32 Sensitivity = 0.95 (0.90) Specificity = LR Sensitivity/1-Specificity = 19 (18) - LR 1- Sensitivity/Specificity = 0.05 (10)

33 33 Sensitivity = 0.90 Specificity = 0.97 (0.95) + LR Sensitivity/1-Specificity = 30 (18) - LR 1- Sensitivity/Specificity = 0.10 (10)

34 3434 Sensitivity constant Increasing specificity Specificity constant Increasing sensitivity

35 35 “Diagnostic criteria apply to individual persons rather than groups. In order to establish a correct diagnosis and to ensure that cases are not missed, such criteria should have a high sensitivity (especially for early cases of a particular disease, e.g. early ankylosing spondylitis) and will often include items that are present already early in the disease course. Such an approach will reduce specificity, which means that more false positives might be expected. Diagnostic criteria are mainly applied in clinical practice, an environment in which, at least for a limited follow-up period, a false-positive diagnosis is considered more acceptable than a false-negative rejection of a diagnosis.” Rheumatology 4 Ed. 2008

36 36 Classification vs. Diagnostic Criteria ??? Classification For groups High specificity Diagnosis For individuals High sensitivity Rheumatology 4 Ed. 2008

37 37 “With this adaptation the sensitivity increased from 76% for the original New York criteria to 83% for the modified New York criteria, whereas the specificity only decreased from 99% to 98%.” Rheumatology 4 Ed. 2008

38 38 LR’s and Odds of disease (assuming 1/100 frequency of AS) Original NY criteria Sensitivity: 76% Specificity: 99% + LR: 0.76/ = 76 (3.2:1) - LR: /0.99 = 0.24 (500:1) Modified NY criteria Sensitivity: 83% Specificity: 98% + LR: 0.83/ = 42 (0.7:1) - LR: /0.98 = 0.17 (588:1)

39 39 Diagnosis is classification of the individual patient.

40 40 What to do? Define well your pre-test odds. Go to well defined practices (subspecialties).

41 41 Ann Intern Med 1999

42 42 Causes of Uveitis (%) in Japan* & USA** Japan (3060)USA (1237) Idiopathic B27 assoc Sarcoidosis JRA SLE Behçet HIV02.4 * H Goto et al. Jpn J Ophtalmol 2007; ** A Rodriquez et al. Arch Ophthalmol 1996

43 43 Conclusions There are no separate classification or diagnostic criteria but c/d criteria. Classification and diagnosis are components of a continuum of which epidemiology is a most integral part. To make a diagnosis is not an art but science backed by experience and arithmetic.

44 4444 A Proposal Avoid attempts for “universal” disease criteria and Aim for classification/diagnostic criteria for subspecialties H Yazici et al. Arthritis Rheum, 2008

45 45 Proposed Scheme of Preparing c/d Criteria in a Subspecialty Setting Retrospective tabulation of demography and diagnoses (old & new pts.; 6 mo.) Same on new patients with checklists (crossed) of symptoms/findings /lab./rad./hist. (6 mo.) Estimation of clinical prediction rules and classification criteria after selecting the comparator groups Internal and external validation of the criteria in the same and different settings (new patients; 6 mo.)

46 46 9/10 = 900/1000 MC Reid, MS Lachs, AR Feinstein JAMA /10 ≠ 900/1000 Confidence Intervals


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