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Transcript of Log in to Poll everywhere › wp-content › uploads › 2020 › 06 › ... · 2020-06-18 ·...
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martindachse767
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Martin Dachsel, Acute Medicine Consultant
Medicine will be harder after this
talk!!!!
Black or white
The World of Ana Steele
Qualities of a good clinician for diagnosis and Management
Knowledgable Clinical Decision maker
Medical Schools and
PG Education?
Think, reason and decision making → Probably most critical skill
What does clinical reasoning involve
• Includes: • History
• Examination findings
• Test results
Clinical Uncertainty
Diagnosis and Treatment
Cognitive Biases
Group of people read profile
Equal mix of introvert and extrovert attidudes
What happened?
Recalling more examples
of introversion
Recalling more examples
of extroversion
Confirmation Bias
Cognitive Biases
However
Around 160 cognitive biases described on Wikipedia
Metacognition and cognitive debiasing
• Thinking about thinking → reflection about clinical decision making
• Is used to reduce the impact of biases
• Debiasing examples: • Differential diagnosis → forcing strategy to consider other diagnoses → will work for anchoring, adjustment, search satisficing, premature diagnostic closure
• Mnemonics → protect against memory failures, ensures full range of possibilities is considered → works against availability bias, anchoring and adjustment
It’s all in the history and examinationClinician starts to generate different hypotheses
Can start asking specific questions
Testing of Hypotheses
Physical Examination can narrow things down
Test results narrow differentials further
But diagnostic tests tell us for sure, or?
Test
Results
Pre-test probability / belief
Increase / Decrease likelihood of disease
Mr T, 88 years
Ischaemic bowel (pre-test probability 80%)
Severe abdominal pain, lactate of 12, history of AF
Excellent Test: CT angio (Sensitivity 93%, Specificity 94%) is NEGATIVE
Excluded Ischaemic Bowel? Chance still 20%
Mammography for breast cancer
• Prevalence of breast cancer age 40: 1%
• Sensitivity of mammography: 80%
• Specificity: 90.4%
• What’s the likelihood that someone positively tested has breast cancer?
Epidemiology
Positive Test means Cancer in
7.8%
P(Cancer / +ve Mammogram) = P(+ve Mammogram / Cancer) x P(Cancer)
P(+ve Mammogram)
P(Cancer / +ve Mammogram) = Sensitivity x Prevalence
P(+ve Mammogram)
Cancer
No Cancer
+ ve test
+ ve test
- ve test
- ve test
P( +ve Mamogram) = (0.01 x 0.8) + (0.99 x 0.096) = 0.103
P(Cancer / +ve Mammogram) = P(+ve Mammogram / Cancer) x P(Cancer)
P(+ve Mammogram)
P(Cancer / +ve Mammogram) = Sensitivity x Prevalence
P(+ve Mammogram)
P(Cancer / +ve Mammogram) = 0.8 x 0.01
0.103
P(Cancer / +ve Mammogram) = 0.0776 = 7.76%
P(Cancer / +ve Mammogram) = 0.8 x 0.20
0.2368
P(Cancer / +ve Mammogram) = 0.676 = 67.6%
How about women over 70
Prevalence of breast cancer age 70: 20 %Sensitivity of mammography: 80%Specifity: 90.4%
Ruling In versus Ruling Out test
•Highly sensitive test ?
•Highly Specific Test ?
Diagnostic Tests (Troponin T levels)
Threshold 13
Troponin T 13
ACS No ACS
Trop >13 100 8
Trop <13 0 70
Sensitivity: 100/100 = 1
Specificity: 70/78 = 0.89
PPV: 100/108 = 0.92
NPV: 70/70 = 1
Threshold 30
Threshold 30
Sensitivity: 80/100 = 0.80
Specificity: 85/88 = 0.96
PPV: 80/83 = 0.96
NPV: 85/105 = 0.81
ACS No ACS
Trop >30 80 3
Trop <30 20 85
0
10
20
30
40
50
60
70
80
90
100
No ACS ACS
Threshold 55
Threshold 55
Sensitivity: 50/100 = 0.50
Specificity: 88/88 = 1
PPV: 50/50 = 1
NPV: 88/138 = 0.64
ACS No ACS
Trop >55 50 0
Trop <55 50 88
Highly Sensitive Test (i.e. Troponin level 13)
PPV: 100/108 = 0.92
NPV: 70/70 = 1Good Rule out test
Highly Specific Test (i.e. Troponin level 55)
PPV: 50/50 = 1
NPV: 88/138 = 0.64Good Rule in test
Graphical Representation
• ?
ROC curves
Absolute vs relative risk reduction
Absolute risk reduction: 1%Relative risk reduction: 50%
Sepsis Mortality
•Neighboring trust showed reduction in Sepsis mortality from 16% to 11%
•Automatic call of outreach team when NEWS>3
•Highly celebrated project
Over 3 months
before after
Reviewed patients 430 650Absolute deaths 70 72Mortality rate 16.3% 11.1%
•Work increase for outreach team by 51%•No decrease in Sepsis mortality
The Solution: the NNT
•Number needed to treat = 1/absolute risk reduction
•Answer to how many patient I need to treat to have 1 positive outcome → really good to explain benefit and risks to patients and families
•Coffee example: absolute risk reduction 1% : NNT = 1 / 0.01 = 100
NNT of 5
• New drug to reduce flatulence: NNT of 5
• Good?
• Side effect: 10% suffer fatal MI
• Still good?
NNT of 50
• New Asthma drug reduces hospital admission in 1 in 50 patients
• Good?
• How about harm:• None?
• Brilliant?
• Costs £100.000 per patient, so 1 hospital admission avoidance: £ 5.000.000
NNT of 500
• Novel treatment to avoid heart attacks
• Good?
• No side effects
• Good?
• Free
• Good?
• Exercise 3 x week
NNT
•Look at the whole picture:•NNT•NNH: number needed to harm•Costs / efficiency
Fragility Index
Event No Event
Treatment A 2 98
Treatment B 10 90
Event No Event
Treatment A 3 97
Treatment B 10 90
Fisher’s exact test: p=0.03 Fisher’s exact test: p=0.08
How many events needed to render trial not significant?
Fragility Index: 1
Trial of 56 Randomised ICU trials (Ridgeon et al., 2016): Median Fragility Index: ?
2Fragility Index < loss to follow up in 87%
Dr Nobel vs Dr Repeat
• Dr Nobel aspires for Nobel Prize, studies highly original hypotheses
• Dr Repeat repeats the research of others, looks to reproduce significant results
Dr Noble versus Dr Repeat
• Dr Noble: • 1000 randomised trials, 900 studies: Null Hypothesis is true, 100 studies:
Alternative hypothesis is true• All trials had large amount of patients, type I error 5% (effect shown when
null hypothesis is true) and type II error is 10% (effect not shown when alternative hypothesis is true)
• Dr Repeat:• 1000 trials, however examining previous positive trials, Alternative hypothesis
true in 900 trials, Null hypothesis true in 100 trials• Type I error is 5%, type II error is 10%
HA H0 Total
p<0.05
p>0.05
Total 1000
Dr Nobel
• 1000 randomised trials, 900 studies: Null Hypothesis true, 100 studies: Alternative Hypothesis is true
• All trials had large amount of patients, type I error 5% (effect shown when null hypothesis is true) and type II error is 10% (effect not shown when alternative hypothesis is true)
HA H0 Total
p<0.05
p>0.05
Total 100 900
Total
135
865
1000
H0
45
855
900
HA
90
10
100
Dr Repeat
• 1000 trials, however examining previous positive trials, Alternative hypothesis true in 900 trials, Null hypothesis true in 100 trials
• Type I error is 5%, type II error is 10%
HA H0 Total
p<0.05
p>0.05
Total 900 100 1000
H0
5
95
100
Total
815
185
1000
HA
810
90
900
Who is more trustworthy
•Positive result: Dr Noble vs Dr Repeat
•Negative result: Dr Noble vs Dr Repeat
Lets work it out
• Positive result: • Dr Noble: 135 trials, in 45 (33%) the null hypothesis was true (90/135 = 66%)
• Dr Repeat: 815 trials, in 5 trials the null hypothesis was true (810/815 = 99.4%)
• Negative result: • Dr Noble: 865 trials, in 855 the null hypothesis is true (855/865 = 98.8%)
• Dr Repeat: 185 trials, in 95 the null hypothesis is true (95/185 = 51.3%)
What to make out of this
• When we read a study of a trial we must consider if study belongs to Dr Repeat’s or Dr Noble’s universe
• If we have confidence in null hypothesis (theoretical considerations or other results) → probably Dr Noble’s universe, probably shouldn’t believe in significant p-value
• If we however think the two treatment have different t, we may refer study to Dr Repeat’s universe, and decide it is relevant even with a p value above 0.05
Summary
• Important topic to provide patient centred care
• Bayes Theorem is fun
• Remember the NNT and in particular the NNH
• Rule out rule in tests
Resources
Further Resources