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Transcript of 1) Personalizzazione ed ottimizzazione dei percorsi … · 1) Personalizzazione ed ottimizzazione...
1) Personalizzazione ed ottimizzazione dei percorsi diagnostic i - Mirella Fraquelli
2) Gli studi prognostici : metodologia e interpretazione - Gennaro D’Amico
3) Dimensione dell’effetto terapeutico: Eterogeneit à, confondimento e interazione neitrial clinici - Calogero Cammà
4) Eterogeneità e meta-analisi - Fabio Tinè
5) Valutazioni di costo-efficacia : la cassetta degli attrezzi - Americo CicchettiDiscussione
JAMA 2013
Personalized medicine “refers to the tailoring of medical t reatment to the individual
characteristics of each patient. It does not literally mean the creation of drugs or
medical devices that are unique to a patient, but
rather the ability to classify individuals into subpopulations that differ in their
susceptibility to a particular disease or their response to a specific treatment.
Personalized medicine
JAMA 2013
Although the increasing attention directed to personalize d medicine has
largely focused on the interaction of an individual’s genom e with specific
treatments, any individual characteristics that affect treatment outc omes may
be relevant to clinical decision making.
Personalized medicine
A R Feinstein, J Clin Epidemiol 1998
.Taxonomic subgroups were an important, basic intellectualactivity in medical science. In clinical medicine, the taxonomyof pathophysiology has been an essential component of thereasoning and judgment used in good clinical practice.
During the past two decades, however, pathophysiology hasbecome increasingly ignored in medical research andeducation.
Personalized medicine
• What may be true for a group of people of mean
age 50 is unlikely to be true for an individual 50-year
old. (Morgenstern 1982)
• La stima media PER UN GRUPPO può non essere
valida per il singolo paziente.
Ecological bias
Anything that is good science can’t be bad statistics. Thepotential tragedy now is that what may seem to be goodstatistics will be bad science.
Jerome Cornfield
Personalized medicine
Personalizzazione ed ottimizzazione dei
percorsi diagnostici
Mirella Fraquelli
Fondazione IRCCS Cà Granda Ospedale Maggiore, Policlinico – Milano
Monotematica AISF 2013
“Personalizzazione della Cura in Epatologia”
Pisa, 17 - 19 Ottobre 2013
Dr. Mirella Fraquelli
Fondazione IRCCS Cà Granda Ospedale Maggiore, Policlinico – MI
Il sottoscritto dichiara di non aver avuto negli ultimi 12 mesi conflitto d’interesse in relazione a questa presentazione
e
che la presentazione non contiene discussionedi farmaci in studio o ad uso off-label
The clinical decision process
n Diagnosis, prognosis and treatment are part of a same
sequential process with the goal of progressively reducing the
uncertainty about a patient’s true state and take the best
clinical decisions
n Physicians should apply the ‘summary’ results obtained from
the available evidences to individual patients
Analytical
approaches
The clinical reasoning: the dual process theory
Unconscious intuitive
approaches
Spectrum
Automatic,
biased process
Inferential
mode of
discursive
thinking
System 1 System 2
Pagliaro, Bobbio , Colli. La diagnosi in Medicina, Milano, Raffaello Cortina Editore, 2011
Characteristics Non analytical Analytical
Diagnostic criteria
Cognitive type
Frequence of use
Rapidity
Similarity between
patients
Heuristic, intuitive
Very frequent
Fast
Large amount of
knowledge
Hypotetic-deductive
Iterative
Less frequent
Slow
Area of application
Errors
Common diseases
Initial tests «typical»
Overconfidence
Rare disease
Initial tests atypical
Overload
Overinvestigation
Pagliaro, Bobbio , Colli. La diagnosi in Medicina, Milano, Raffaello Cortina Editore, 2011
Female, 45 yrs
Weight loss : 10 kg during last 3 months
Asthenia
Tremor and palpitation
What is the diagnosis?
System 1- Non-analytical reasoning: e.g. perceptive recognition
Female, 45 yrs
Weight loss : 10 kg during last 3 months
Asthenia
Tremor and palpitation
What is the diagnosis?
����Tyreotoxicosis
System 1- Non-analytical reasoning: e.g. perceptive recognition
System 2 : Analytical reasoning
Initial level of probability of a diagnostic hypothesis
(Epidemiology, medical history, physical examination, laboratory etc.)
PRE-TEST PROBABILITY
INDEX TEST RESULT
Variation of the level of probability
POST-TEST PROBABILITY
Level of certainty necessary for a therapeutic decision
- +
System 2 : Analytical reasoning
The acceptable level of UNCERTAINTY depends on the
penalty for being wrong.
For my single patient
it will be better to be treated if false positive or
not treated if false negative?
Non-analytic vs analytic reasoning
The case of acute cholecystitis
Abdominal pain characteristics, fever and Murphy sign:
LR+ 30
n The clinical judge is simple and rapid (Gestalt) when facing with a
typical case
n Combinations of certain symptoms, signs, and laboratory results likely
have more useful LRs, and inform the diagnostic impressions of
experienced clinicians more than predictive rules
Trowbridge J AMA 2003; 298:80-86
simple
chaotic
complex
1
1Level of uncertainty
Lev
el o
f d
isa
grr
em
en
t
0
Diagnosis-
treatment
strategies
guidelines
RCT
Stacey RD. Strategic management and organizational dynamics. London: Pitmann Publishing, 1996.
How can I transfer the results obtained from diagnostic
studies to my single patient ?
Internal validityExternal validity
Internal validity
Correct study design
Ideal experimental conditions
Data homogeneity
Reduced heterogeneity
Data precision and
repeatability
External validity
Clinically relevant context
No center selection
No patients selection
Co-morbidity
Data transferability
Diagnostic test accuracy studies
• Critical appraisal of available diagnostic literature by assessing:
- methodological quality (INTERNAL VALIDITY)
- transferability (EXTERNAL VALIDITY)
- reporting (correct expression of diagnostic estimates)
(QUADAS 2, STARD INITIATIVE)
• Match the characteristic of my patient to that of those reported in
the literature as measures of accuracy may vary across patient
groups
Factors affecting the transferability of data derived from
diagnostic studies to a single patient
Practical example: transient elastography (TE, fibroscan)
A non-invasive technique conceived to assess
hepatic fibrosis by measuring liver stiffness.
Factors affecting the transferability of data derived from
diagnostic studies to a single patient
INTERNAL VALIDITY
n Assess existing pathways and formulate the proposed role for the IT
n Assess the index test intrinsic properties (repeatability, indeterminate
results, cut off) and Operator/s performances and variability
n Choose the correct study design
n Consider the reference standard properties
Roles of index test (’new’)
Bossuyt et al. BMJ 2006
Consecutive patients with
suspected chronic liver disease
Transient
elastography
positive negative
Liver Biopsy
TE role: replacement
TE - Intrinsic properties and operator’s characterisitcs
Applicability : failure 2.4 - 5% (related to ascites, reduced intercostal space)
inconsistent results 8-18% (mainly related BMI >28 kg/m2 )
Reproducibility: intra- extra-observer variability ICC 0.97-0.98
Normal values: mean (+SD) 4.8-5.4 (+1.5-6.9) kPa
median 4.1 (females ) 4.6 (males)
95th 7.4 (females) 7.8 (males)
Increased if steatosis, increased BMI, metabolic syndrome
Correct study design: architecture of diagnostic research
Phase 0
Intrinsic TE properties (reliability, reproducibility)
Phase 2Consecutive HCV patients with a wide spectrum of hepatic
fibrosis , all undergoing both TE (IndexTest) and liver biopsy(Ref Standard)
Phase 3
To assess if TE tested patients fare better than comparable patients tested
by a LB or not tested (efficacy)
Phase 4 Benefits and harms of the TE-treatment strategy into clinical practice (effectiveness & safety )
Phase 1TE values in healthy volunteers, blood
donors, and the general populationFactors influencing TE values
(sex, age, BMI)
Colli, Fraquelli et al. Hepatology 2013, accepted
“Relevant” spectrum of
patientsTE
Liver biopsy
Liver biopsy
TP
FP
FN
TN_
Basic design of diagnostic accuracy studies: Prospective, blinded cross
classification of test and reference standard in a clinical relevant setting
Study design
Diagnostic Case Control Study
Test parameter
Healthy
volunteers
%
Very sick
individuals
Test
threshold
Spectrum effects: evaluation of two very different
populations
the healthiest the sickest
SPECTRUM BIAS
Diagnostic Cross sectional Study
Test parameter
Patients without
disease
%
Patients with
disease
Test
threshold
Spectrum effects: evaluation of representative
populations
SPECTRUM VARIATION
Factors affecting the transferability of data derived from
diagnostic studies to a single patient
EXTERNAL VALIDITY
n Patients spectrum (disease prevalence, center or patient
selection)
n Patients characteristics (sex, age, BMI , etc)
n Co-morbidity
Severity of the disease
co-morbidities
Severity of the diseaseco-morbidities
Severity of the disease
co-morbidities
• Sensitivity and specificity (LR+ & LR -) are intrinsic properties of a
diagnostic test assumed to be independent from context.
Actually they may change according to different settings (primary
care vs referral center) and these changes are not predictable.
• Variation in disease prevalence and test accuracy between studies
should prompt the readers to detect important differences in
study population or study design affecting accuracy
Effect of prevalence on diagnostic estimates
Pos Aagreement
BCases detected only by
the Index Test
Neg CCases detected only by
the Reference Standard
Dagreement
TE
LIVER BIOPSY
Pos Neg
Index Test more
specific
Index Test
more
sensitive
Glasziou Ann Intern Med 2008; 149:816
Disagreements between Reference Standard and Index Test
Methodological and reporting quality:
the correct expression of diagnostic estimates
Degos et al, J Hepatol 2010Friedrich-Rust et al. Gastroenterology 2008
AUROC:
0.84 (0.82-0.86)
AUROC
Diagnostic accuracy: ROC curves and thresholds
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
1-Specificity
Sen
sitiv
ity
0
0,2
0,4
0,6
0,8
1
0 0,2 0,4 0,6 0,8 1
1-Specificity
Sen
sitiv
ity
Cut offvariation
AUROC: quantitative measure of the diagnostic accuracy from 0.5 to 1
Good if > 0.80 (but look at confidence intervals !!!)
Youden index: maximum joint sensitivity and specificity
Cut off to rule out
Cut off to rule in
TE performance in diagnosing cirrhosis in HCV
Author, yr Etiology Disease
prev (%)
Patient
#
Cut-off,
kPa
Sens
(%)
Spec
(%)
-LR +LR AUROC
Ziol, 2005 HCV 19 251 14.6 86 96 0.14 23.0 0.87
Castera, 2005 HCV 25 183 12.5 87 91 0.14 9.7 0.95
Ganne-Carrié 2006 Mixed 15 775 14.6 79 95 0.11 15.8 0.95
Coco, 2007HCV-HBV 20 159 14.0 78 98 0.22 39 0.96
Fraquelli, 2007 Mixed 18 200 12.0 91 89 0.10 8.2 0.90
Arena, 2008 HCV 19 150 14.8 94 92 0.07 11.3 0.98
Lupşor, 2008 HCV 21 324 11.8 87 91 0.14 9.4 0.94
Zarski, 2012 HCV 15 382 12.9 77 90 0.25 7.7 0.93
Incertezza
DIAGNOSI
Probabilità Pre-Test
Risultato del Test LR+ & LR-
Probabilità Post-Test
Pre-test
ODDS
Post-test
ODDS
Bayesian approach
Some practical examples….
1. Male, 60 yr, HCV positive, HCV RNA 1.298.000, genotype
1b, non-responder dual tx, Potus 100 gr ETOH/die, BMI 31
(PRE-TEST PROB. ≈ 50%)
2. Male 50 yrs, HCV positive, HCV RNA 1.234.565, genotype
1b, naive, no potus, BMI 26
(PRE-TEST PROB. ≈ 25%)
3. Female, 25 yr, HCV positive, HCV RNA 652489
genotype 2, naive, no potus, BMI 21
(PRE-TEST PROB. <10%)
Study Exclusion criteria Sex
(males, %)
Age BMI Comorbidities/
cofactors
Lupsor et al.
(tertiary
center)
-Pregnancy
-HCC
- HBV or HIV
coinfection
- Ascites
35 48±10 26±4 NR
Arena et al.
(tertiary
center)
- BMI> 30
- Previous or current
ETOH abuse
-HBV or HIV
coinfection
-Hepatic
decompensation, HCC
61 50±12 23±2.8 NR
Ziol et al.
(secondary
centers)
- Ascites 62 47±13 24±3 NR
Castera et al
(tertiary
center)
- HBV or HIV
coinfection
- uninterpretable LB
examination
- HCC
57 51±12 25±4 NR
LR+ 9.4
LR- 0.14
25 50 75 100
25
5
0
75
1
00
Test Information
Pre-test
probability
Po
sr-t
est
pro
ba
bili
ty
TE in diagnosing F=4
cut off 11.8
Ex. 1
Lupsor et al. J Gastr Liver Dis 2008
LR+ 11.3
LR- 0.07
25 50 75 100
25
5
0
75
1
00
Test Information
Pre-test
probability
Po
sr-t
est
pro
ba
bili
ty
TE in diagnosing F=4
cut off 14.8
Ex. 2
Arena et al . Gastro 2008
LR+ 9.4
LR- 0.10
25 50 75 100
25
5
0
75
1
00
Test Information
Pre-test
probability
Po
sr-t
est
pro
ba
bili
ty
TE in diagnosing F=4
cut off 11.8
Ex. 3
Lupsor et al. J Gastr Liver Dis 2008
LR+ 9.4
LR- 0.10
25 50 75 100
25
5
0
75
1
00
Test Information
Pre-test
probability
Po
sr-t
est
pro
ba
bili
ty
TE in diagnosing F=4
cut off 11.8
Ex. 3
Lupsor et al. J Gastr Liver Dis 2008
Gender ?
Age ???
Prevalence ?
What is important to patients…
• ...always different to what is important to doctors….
• Physicians seem not always be able to interpret the preferences
of their patients
• Patients and their families should be encouraged to participate
more actively to the decision regarding diagnostic and
therapeutic choices
Mutiple Sclerosis
Rothwell et al. BMJ 1997; 314: 1580–83.
n Well trained and experienced physicians rarely used the analytic
approach especially for common, simple and iterative problems
n On the contrary, complex, unusual problems are more efficiently
approached with the analytic approach or by combining the two
processes
n A critical appraisal of the existing scientific evidence is essential to
transfer the results of diagnostic studies to individual patients
n Patients should be properly informed by physicians and their
preferences should always be taken into account to take the best
clinical decisions
Conclusions