An actual framework for clinical reasoning

To prove or not to prove, that is the question... Be all my errors re e er d." J. Van den Ende, IMTA

Aristoteles • Categorico-deductive – All humans are mortal – Socrates is human – Thus: Socrates is mortal

• Hypothetico-inductive – Socrates, Plato, Aristotoles and Toon Hermans are mortal – They are human – Hence: Humans are mortal

Popper: hypothetico-deductive • H potheti o a du ti e – Humans are mortal – Socrates is mortal – Thus : Is Socrates human???

• In human medicine: – TB patients cough – Yakasi coughs. – Does Yakasi has got TB?

Reverend Bayes • Pretest – +

• Sensitivity – +

• False positives – =

• Post test Probability

“et theor : of set theor , li era os do i e

Claude Elwood Shannon: information theory

• Entropy= uncertainty • Entropy= chaos • Chaos=molecule movements • Molecule movements=temperature

Lowering entropy in physics

7

Entropy representation: maximum at 50%

Entropy as function of pretest

pretest 0,001

0,09

1

entropy

0,01 0,5 0,99 0,999

1,2

post test prob 0,01

0,8 pretest

0,6

pure entropy

0,4

0,91 1,00 1,00

0,2 0 1

2

3 steps

4

5 8

Entropy change: not illuminating

Entropy change

pretest

0,001 0,01 0,5 0,99 0,999

post test prob

1 0,8 0,6

pretest

0,4

entropy change

0,2

0,01 0,09 0,91 1,00 1,00

0 1

2

3

4

5

9

Entropy of a set of diseases

10

Entropy of a set of diseases 2

11

Entropy: Conclusions • • • •

Entropy study for a set of hypothesis No value for a single hypothesis Uncertainty maximum at 0.5 Log10odds much more intuitive for clinicians

12

Kahneman: thinking fast and slow

• Heuristics – – – –

Availability Confirmation Anchoring Hindsight

• Thinking fast: pattern recognition • Thi ki g slo : true reaso i g

The concept of threshold: Balance between utility of treating vs not treating

Threshold The threshold

Probability of disease

sure

Enough evidence to treat

DECISION THRESHOLD

Not enough evidence to treat

impossible

Factors factors influencing the thresholds

Probability of disease

sure

impossible

treatment risk and cost

disease seriousness, contagiousness

availability and effectiveness of treatment

Sackett

• Predictive values : useless for the clinician • Use likelihood ratios • Use Fagan nomogram • Apply threshold

The Antwerp framework for clinical reasoning

a history of three rebellions and of serendipity (Rie de Ridder) and a lot of intellectual fun.

I the earl

i eties …

• Prof. Gigase: tour in Western-Africa – Depressed – Average diagnostic spectrum 12 diseases – Organize some "thinking lessons"

• Formal and applied course in clinical epidemiology. TB positives EXAMEN +

TB negatives

0,10

EXAMEN -

12,0 99

12

1

88

0,001

87,9

0,10

99,9

A bridge between clinical epidemiology and clinical reasoning • Clinical practice and clinical epidemiology – same phenomena – no bridge – no common language.

• cross-fertilization • resulting in a new model for clinical logic.

Bayes for 1 argument

TB positives EXAMEN +

TB negatives

0.10

EXAMEN -

33.0 99

33

1

67

0.001

66.9

0.10

99.9

Bayes for n arguments

Rebellion I • Students: – "What is all this athe ati al ru ish for! – Gi e us so e real-life e a ples!

• Course closed.

But: students were right!

• Foreign language – English – Not clinical

• Where to find LR? • Negative LR difficult: 0.03 ?? • Convert probability to odds !!

New language

• li i ia -frie dl la guage • L‘+ = o fir i g po er • LR- = e ludi g po er • pre-test probability = li i al suspi io , roo ha e • post-test pro a ilit = ertai t le el

aiti g

Rebellion II • Nobody less than Paul Janssen – eminent founder of Janssen pharmaceuticals – You are o pletel ro g – Human mind thinks in categories, not in continuous numbers

• Ho to ultipl ategories to at h Ba es’ theore ? • That’s our pro le , ot i e!

Answer: • Work in logarithms of likelihood ratios • Use u likelihood ratio : -10 ~ 0.1 • Round • Add in stead of multiplying

Turing,

• father of the term artifi ial i tellige e • father of the ROC curve • same idea • never published • gone our great discovery!

Add in stead of multiplying

pre-test odds

x likelihood ratio = post-test odds

log10 pre-test odds

+ log10 LR = log10 post-test odds

Work with cathegories, rounded values

clinical class pre-test+ log10 LR

= clinical class post-test

clinical class pre-test+ power class

= clinical class post-test

The Feinman-Tufte principle

• What can be shown in

a graph, • should not be explained in text.

Power of arguments

Diseases Probability

certain

very strong argument

probable

possible

less probable

impossible

weak argument

good argument

strong argument

Advantages • • • • • •

Added value constant Symmetry Multiple tests Extremities of scale visible Multiple diseases OR

Effect of a positive test on disease probability, on a linear scale 100 90 80 70 60 50 40 30 20 10 0

student prostitute

waiting room

HIV test pos

Difference between added value of a positive HIV elisa 99.9

3

99

2

90

1

50

0

student

10

-1

1

-2

0.1

-3

0.01

-4

waiting room

HIV test +

prostitute

Asymmetry 2

1

Log10 odds

0

Pretest

Thick film

confirmer

-1

excluder -2

-3

-4

Consecutive steps

Asymmetry 2

1.5

Log10 odds

1

confirmer

0.5

0

Pretest

Tertian fever

-0.5

-1

-1.5

-2

Consecutive steps

excluder

di m

w

sp in dl e

x

fe ve r

ia

an gu di sc la t io it i sn os te ol ys is

vi ty

ro om pa ra pl eg

ng

se ns it i

ai ti

in is he d

pr ev al en ce

Probability evolution

99.99 4

99.9 3

99 2

90 1

50 0

10 -1

1 -2

0.1 -3

0.01 -4

Extremities of scale 100 90 80 70 60 50 40 30 20 10 0

Odds ratio • Total discriminative value – Positive LR + Negative LR – Pos LR / (inverse of negative LR) – 10/0.1

• Show it on a graph

Odds ratio 2

1.5

1

Odds Ratio

Log10 odds

0.5

confirmer

0

Pretest

Tertian fever

-0.5

-1

-1.5

-2

Consecutive steps

excluder

A LGORI THM E RA M IFI A NT DY SPNEE DE L'A DULTE

inspiratoire

globale expiratoire

fièvre voix rauque

fièvre

asthme

goit re fièvre

toux

orthopnée

eosinophilie massive goit re plongeant

corps etranger pneumonie

fausses membranes

diphtérie

katayama

laryngite

signes de phlébite

râles crépit ants pieds gonflés

bronchite, bronchiolite

insuff isance cardiaque

embolie pulmonaire

autres causes

Rebellion III

• Dr Jansen of the Damian Foundation – algorith i thi ki g is ot hu a • Answer: – propose a other logi al fra e . • Jansen: – this is our pro le , ot i e

Lumbago in Verona • Algorithms – Serial – Flip-flop, dichotomous

• Human brain – Parallel – Fuzzy logic

• City of Romeo and Juliet • Tablecloth: the solution

The idea of the diagnostic landscape or panorama • Avoid the tunnel trap – Go for serious and treatable diseases first – Do not stop at the first plausible pattern

• Look for key findings – Confirmers – Excluders

Typhoid fever Borreliosis Acute glaucoma Malaria, acute

Leptospirosis

acute headache woman

Arteritis temporalis

Bact. meningitis

CO-Intoxication Mastoiditis Pre-eclampsia

Hypertensive crisis

Typhoid fever recurrent exclusion diagnosis, leucopenia, Abdominal symptoms

Borreliosis thick smear, Splenomegaly pallor

Acute glaucoma

Fever

Malaria, acute red eye (blue ring), pain, vision disturbance

acute headache woman

Leptospirosis Exposure, jaundice, Haemorrhages, proteinuria

Arteritis temporalis

Neck stiffness

Mastoiditis

CO-Intoxication proteinuria blood pressure

local pain, ear infection

Unilateral, ESR

No fever

Bact. meningitis

Pre-eclampsia

Exposure, red face, heating in house

Hypertensive crisis

Viral Infection, incl Arbo, Hanta, Lassa

heatstroke

Typhoid fever

recurrent

Sinusitis, acute exclusion diagnosis, leucopenia, Abdominal symptoms

Borreliosis thick smear, Splenomegaly pallor

Fever

Acute glaucoma

Malaria, acute

acute headache woman

Leptospirosis Exposure, jaundice, Haemorrhages, proteinuria

red eye (blue ring), pain, vision disturbance

migraine

Arteritis temporalis

Viral meningitis Unilateral, ESR

No fever

Bact. meningitis Neck stiffness

CO-Intoxication Exposure, red face, heating in house

proteinuria

Mastoiditis

blood pressure local pain, ear infection

Pre-eclampsia

cluster headache tension headache

Hypertensive crisis

trauma

Acute renal failure Hemorrhage, intracerebral, subdural, epidural

Zona

Future I • Train students in clinical reasoning • Train trainers: university professors • Through thorough remodeling of the logic – More patients can be cured – For much lower expenses. – In developing countries of utmost importance !

Future II • Evidence of usefulness of the model • Fundamental research in – Thresholds – Powers – Logic

Thanks Al a s make the audience suffer as much as possi le . Alfred Hitchkock