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Diagnostic research. Lecture Contents I. Diagnostics in practice - Explained with a case II.Scientific diagnostic research – Design – Data-analysis –

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Presentation on theme: "Diagnostic research. Lecture Contents I. Diagnostics in practice - Explained with a case II.Scientific diagnostic research – Design – Data-analysis –"— Presentation transcript:

1 Diagnostic research

2 Lecture Contents I. Diagnostics in practice - Explained with a case II.Scientific diagnostic research – Design – Data-analysis – Reporting III.Exercises IV.Summary

3 Diagnostics in practice Diagnostics always start with a patient with a complaint/symptom Case: neck stiffness Child, 2 years-old, comes to ER with parents Child turns out to have a very stiff neck What is the physician’s aim?

4 Diagnostics in practice Aim of the physician Quickly and efficiently determine the correct diagnosis Why diagnose? Basis medical handling Determines treatment choice Gives information about prognosis What are possible diagnoses for neck stiffness?

5 Diagnostics in practice Differential diagnosis (DD) Bacterial meningitis Viral meningitis Pneumonia ENT infection Other (e.g. myalgia) What is the most important diagnosis? Which one does the physician not want to miss?

6 Diagnostics in practice Most important diagnosis Bacterial meningitis (BM) If missed: often fatal

7 Diagnostics in practice Suppose: 20% of all children on the ER with neck stiffness has BM – 20% with disease in that population = prevalence – Prior-probability What is your decision for the child in this case?

8 Diagnostics in practice Decision for child in case Prior-probability too low to treat Prior-probability too high to send home Decision: reduce uncertainty  diagnostics What is the best test?

9 Diagnostics in practice Best test Lumbal punction (liquor culture)

10 Diagnostics in practice Gold standard True disease status; ‘truth’ –Never 24 karat Reference standard/test Decisive test with doubt Perform reference test for everybody (=every child on ER with neck stiffness)?

11 Reference test for everybody? Unethical  too invasive/risky Inefficient  too expensive Do not perform unnecessarily How should we then determine the probability of disease presence and what would be ideal? Diagnostics in practice

12 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 Diagnostics in practice

13 Suppose: after anamnesis & PE 10% probability of BM Probability of disease given test results = posterior- probability The bigger the difference between prior and posterior probability, the better the diagnostic value of the tests Our decision for child in case: probability is too high to send home --> next step?

14 Diagnostics in practice Next step –Additional research, e.g. blood tests (leucocytes, CRP, sedimentation, etc.)

15 Diagnostics in practice Suppose: 1% posterior-probability after anamnesis, PE+ simple lab testsposterior 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

16 Diagnostics in practice Summarizing What does diagnosing involve in practice? –Estimation of probability of disease presence based on test results of the patient When is the probability of disease best estimated? Why is this usually not done?

17 Diagnostics in practice Why not all possible tests? –Invasive (for patient and budget) –Unnecessary: different test results give same info –However: In practice often more tested than necessary! What diagnostics truly necessary scientific diagnostic research


19 Study design Scientific diagnostic research –What tests truly contribute to probability estimation? –Has to serve practicefollow practice

20 Study design Research question Domain Study population Determinant(s): test(s) to study Endpoint: presence/absence disease (outcome) Study design: design Data analysis, interpretation + reporting

21 Research question With as few as possible simple, safe, and cheap tests estimate the probability of the presence/absence of disease. Determinant-outcome relation: – probability of disease as a function of test results – outcome = probability of disease = % = prevalence – test results = determinants

22 Research question Case What tests contribute to probability estimation of presence or absence of BM in children with neck stiffness at the ER? Or: Determinants of presence/absence disease (BM)? %BM = ƒ(age, gender, fever, blood leucocytes, blood CRP, etc)

23 Research population Case: All children with neck stiffness in 2002 at ER Utrecht

24 Domain For whomdomain, generalisation = type of patient with certain symptom/complaint + setting Research population = 1 sample from domain Case: All children (e.g. in Western world) suspected of disease (BM) based on neck stiffness (characteristic) in secondary care (setting)

25 Determinants = Tests to study Diagnostic determinants All possible important tests (in domain) Case Items anamnesis, PE and lab (blood and urine) tests

26 Endpoint ‘True’ presence/absence disease = Diagnostic outcome = Results reference test NB:reference = not infallible but always best available test in practice at that moment Case Positive liquor culture

27 PICO EBM Population/ problem Intervention Comparison/ control Outcome Domain Determinant Reference test Outcome

28 Measure determinants/endpoint Determinants –Without knowledge (blinded) of the outcome –Same method in study and practice never measure more precisely than in practice (overestimation information yield) Endpoint –Assessment blind for determinants –With the best possible test known in practice

29 Study design Observational and descriptive –Observational = no manipulation of determinants –Descriptive = not causal –if the determinant only predicts –no hypothesis functional mechanism determinant- outcome >1 determinant

30 Study design Cross-sectional = Simultaneously measure determinants and outcome

31 Data-analysis After data collection, per patient –Value determinants (test results) –Diagnostic outcome (reference test)

32 Data-analysis Data analysis: 3 steps 1) Estimation a priori probability (without test results) 2) Compare each test result separately with reference = univariate 3) Compare combination of test results with reference = multivariate (via model) - Following order in practice - Determine added value test result to already collected (previous) test results

33 Data-analysis Case Data scientific research available: 200 patients with neck stiffness at ER Liquor culture positive (BM+) n=40 Liquor culture negative (BM-) n=160 Step 1: A priori probability (prevalence) of BM? = % BM+ = 40/ 200 patients = 20%

34 Data-analysis reading 2 by 2 table Disease PresenceAbsence Test PositiveTrue positive A False positive B NegativeC False negative D True negative Step 2: Analysis per determinant (univariate) Use 2 by 2 table

35 Data-analysis reading 2 by 2 table Horizontally Positive predictive value (PV+) = probability Disease + if test + PV+ = A / A + B Negative predictive value (PV-) = probability disease - if test - PV- = D / C + D Vertically Sensitivity (SE) = probability test + if disease + SE = A / A + C Specificity (SP) = probability test - if disease - SP = D / B + D What numbers do you think are most useful in practice (PV+ and PV- or SE and SP)? TP A FN C B FP Gold standard Disease +Disease – Test + Test – D TN

36 Data-analysis Perfect diagnostic test False Positive = 0 False Negative = 0 e.g. Fever > 38 0 C as predictor for BM 20 40160 7090 200 2090110 BM+BM-tot. Yes (+) Fever > 38 0 C No (-)

37 Data-analysis reading 2 by 2 table Horizontally probability BM+ if fever+ = 20/110 = 18% PV+ = A / A + B probability BM - if fever- = 70/90 = 78% PV- = D / C + D Vertically probability fever+ if BM+ = 20/40 = 50% SE = A / A + C probability fever- if BM- = 70/160 = 44% SP = D / B + D What numbers do you think are most useful in practice (PV+ and PV- or SE and SP)? 20 TP A FN C 20 90 B FP Gold standard BM+BM– Fever + Fever – D TN 70


39 Exercise 1 Mercury thermometer or timpanic membrane infrared meter

40 Exercise 1 Ad question 1 Research question: Can fever be determined with the TIM? Determinant: test under study = timpanic membrane infrared meter Outcome: fever determined with rectal mercury thermometer (RMT) Domain: Children in secondary/tertiary care (ER hospital)

41 Exercise 1 Ad question 2 77 TP A FN C 19 9 B FP TIM >38° TIM  38° D TN 108 GS RMT Fever+Fever– Se = probability TIM+ if RMT+ = 77/96 = 80 % SP = probability TIM- if RMT- = 108/117 = 92%

42 Exercise 1 Ad question 3 77 TP A FN C 19 9 B FP TIM >38° TIM  38° D TN 108 GS RMT Fever+Fever– PV+ = probability RMT+ if TIM+ = 77/86 = 90 % PV- = probability RMT- if TIM- = 108/127 = 85%

43 Exercise 1 Ad question 4 –The prior probability of fever in the general practice is lower, e.g. 20% (X/213=0,2  X=43) – For similar Se and SP: (A/43=0,8  A=34) (D/170=0,92  D=156) –PV+ becomes lower (34/48 = 70%) –PV – becomes higher (156/164 = 95%) 9 43170 156164 213 341448 TIM+ TIM- GS RMT Fever+Fever–

44 Exercise 1 Ad question 5 –In the general practice an unjustly referred or treated child is less of a problem than an unjust reassurance of the parents –Especially the negative predictive value must therefore be sufficiently high

45 Data-analysis: combination of determinants In practice not one single diagnosis based on 1 test –Tests together distinguish ill/non-ill –Method: statistical model Moreover: diagnostic process is hierarchical –(simple --> invasive/expensive) --> always start with anamnesis model --> see case

46 Data-analysis Case: model with all anamnestic tests gender + age + fever + pain %BM = ƒ(gender, age, fever, pain) Statistical model can be seen as 1 (composed) test Quantify diagnostic value model with area under ROC curve ( R eceiver O perating C haracteristic = A rea Under Curve (AUC))

47 Data-analysis

48 Case: AUC anamnesis model = 0,71 Informal interpretation AUC = % correctly diagnosed The larger the ROC area  the better the model AUC range: 0,5  1,0 AUC = 0,5  bad (Se = 1- Sp  diagonal [coin]) AUC > 0,7  reasonable AUC > 0,8  good AUC > 0,9  excellent AUC = 1,0  perfect (Se=100% & 1-Sp=0%)

49 Data-analysis Quantify added value additional tests to previous tests Extend previous model (follow order practice) Quantify change in AUC Case Model 1anamnesis model + physical exam (5 extra tests) --> AUC = 0,72  interpretation? Model 2anamnesis model + 3 blood tests ---> AUC = 0,90  interpretation?

50 Data-analysis

51 The AUC does not directly say anything about individual patients and is therefore not directly applicable

52 Reporting Research question Study set-up Research population, setting, determinants, outcome, design Results Predictive values (new) test and/or ROC curve ROC curve combination of tests Added value new test --> ROC curve

53 Exercise 2

54 Ad question 1 -Cross-sectional study in patients suspected of a stomach or duodenum ulcer -For all patients anamnestic data were collected -For all patients a gastroscopy was done -Independent diagnostic value of anamnestic factors (determinant) for the diagnosis of ulcer (outcome: gastroscopy) were calculated Exercise 2

55 Ad question 2 Adults with stomach complaints referred to a gastro- enterology policlinic in a peripheral hospital

56 Exercise 2 Ad question 3 Score is 5, risk is 57%

57 Exercise 2 Ad question 4 -Everybody above the cut-off point has the same risk (and the same below the cut-off point) -Of course this is not true and the score loses precision -Preferably predictive values for score-categories and predictive values for more cut-off points

58 Exercise 2 Ad question 5 20 TP A FN C 5 11 B FP Test + Test - D TN 64 Peptic ulcus +– PV+ = 20/31 = 65% PV- = 64/69 = 93%

59 Exercise 2 Ad question 6 -Predictive values more favourable and therefore preferred -But it is not about the isolated predictive value but about the added diagnostic value given the results of the anamnestic score

60 Exercise 2 Ad question 7 Perform the anamnestic score and the breath test for a population from the domain. Subsequently perform the reference test (endoscopy) for everybody Compare the next determinant-outcome relations: P(ulcus) = ƒ (age, gender, anamnesis,...) P(ulcus) = ƒ (age, gender, anamnesis,..., breath test) Then compare the Receiver Operating Characteristic (ROC)-curve of the models

61 Exercise 2 Ad question 8 -Breath test partially contains the same information as the score -Suppose that the breath test is more often positive with age, then the breath test also measures age and therefore the added value is less than when the breath test would be completely independent of the score

62 Exercise 2 Ad question 9 - Preferably not, but if the assessor is informed of the data in the score in practice, than it should be the same in the study

63 Diagnostics Summary (1) Diagnostics in practice –Uncertainty reduction –Determines prognosis & determines policy Diagnostic research Design –Observational –Descriptive

64 –Cross-sectional Simultaneous measurement determinant and outcome (reference standard) –Always study >1 determinant Design –Assess determinants as in practice –Assess disease status & determinant status with double blinding Diagnostics Summary (2)

65 Analysis –Univariate (per determinant) –Multivariate: combination of test results in relation to outcome Endpoint = ƒ(combination of determinants) Determine added value; first analyse least invasive tests (as in practice) Reporting –Mainly added value of test Diagnostics Summary (3)

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