Making sense of Diagnostic Information

1. Making sense of Diagnostic Information Dr Carl Thompson

2. Clinical spectrum Non-iatropic cases with mild symptoms Non-iatropic Asymptomatic cases Iatropic hospital cases Threshold of iatropy Iatropic cases treated in Primary care

3. Diagnostic universe Positive test Disease False positive True positive False negative True Negative

4. Diagnostic universe

5. Dx info and probability revision Postpositive-test probability of disease + Pre test probability - Post negative test Probability of disease

6. scenario 5 year old girl presents on the ward via A&E with a “sore tummy”, feeling “hot” but with clear, non-smelly urine and otherwise OK physiological signs – can you rule out UTI? A colleague says that that clear urine is a good test for ruling out UTIs. You know its not perfect (I.e. some UTIs are missed) how much weight should you attach to the clear urine? Should you order an (expensive) urinalysis and culture just to be on the safe side (bearing in mind that money spent on that is money that could be spent on something else)?

7. Pre test probability • Random patient from given population • PRE TEST PROB = POPULATION PREVELANCE

8. Diagnostic universe

9. Sensitivity and specificity (a recap) • Sensitivity P(T+|D+) Sn or TPR (true positive ratio • 26/29 (0.89/89%) • Specificity P (T-|D-) Sp or TNR (true negative ratio) • 107/130 (0.82/82%) • FNR = proportion of patients with disease who have a negative test result • 1-TPR (0.11/11) • FPR = proportion of patients without the disease who have a positive test result • 1-TNR (0.18/18)

10. 2 x 2 P revision (steps 1-2 of 4)

11. 2 x 2 P revision (steps 3-4 of 4)

12. Bayes formula • Pre test odds x likelihood ratio = post test odds • Nb* pre test ODDS = prevalence/(1-prevalence) • Steps when finding is present • Calculate LR+ • Convert prior probability to pretest odds • Use odds ratio form of Bayes’ to calculate posttest odds • Steps when finding is absent • Calculate LR- • Convert prior probability to pretest odds • Use odds ratio form of Bayes’ to calculate posttest odds

13. Nomogram Nb. No need to convert to pre test odds just use P PD+|T+ PD-|T-

14. Path Probability No cure Disease present p=.90 No cure Do not operate p=.10 Survive p=.10 p=.10 p=.90 Cure Try for the cure p=.90 p=.10 p=.90 Cure Disease absent Disease present Operative death Operative death p=.10 p=.02 p=.98 Cure Palliate p=.10 p=.90 Operative death Operate Survive p=.01 p=.99 p=.90 No Cure Disease absent Survive Path probability of a sequence of chance events is the product of all probabilities along that sequence

15. T+ (0.9) D+,T+ (162) P(T+|D+ ieSn) D+ (180) P(D+) T- 0.1 D+,T- (18) P(T-|D+ I.e.1-Sn) T+ (0.18) D-,T+ (148) P(T+|D- I.e. 1-Sp) D- P(D-) T- (0.82) D-,T- (672) P(T-|D- I.e. Sp) D+ D+,T+ BAYES P(D+|T+) T+ P(T+) T- D+,T- P(D-|T+) D+ D-,T+ P(D+|T-) T- P(D-) D- D-,T- P(D-|T-)

16. T+ D+,T+ P(T+|D+) D+ P(D+) T- D+,T- P(T-|D+) T+ D-,T+ P(T+|D-) D- P(D-) T- D-,T- P(T-|D-) D+ (162/310) 162 BAYES 0.52 T+ (162+148) 310 D- (148/310) 148 0.48 D+ (18/690 18 0.02 T- (18 + 672) 690 D- (672/690) 672 0.98

17. Pre test P (where do they come from?) • Dx as opinion revision • SHOULD be epidemiological data sets • IS memory and recalled experience

18. Heuristics (1) • Availability: P = ease by which instances are recalled. • Divide the n of observed cases by the total number of patients seen – makes observed case frequency more available

19. Representativeness • P = how closely a patient represents a larger class of events (typical picture) • Remember prevalence

20. Anchoring and adjustment • Starting point overly influential (not a problem with epidemiological data of course) • Cognitive caution is common (Hammond 1966)

21. Value induced bias • Utility is a perception (it’s the bit that goes beyond the facts “which speak for themselves”: cost, benefit, harm, probability) • The fear of consequences affects decisions: I.e. overestimation of malignancy because of fear of missing case