Safety and efficacy of computed tomography
Transcript of Safety and efficacy of computed tomography
Ying-Lie O
Safety and efficacy of computed tomography
a broad perspective
EC-EURATOM 6 Framework Programme call 2003Project no. FP6/002388
www.msct.info
Ying-Lie O
Safety and efficacy of computed tomography WP1 Justification
UOXFStuart MeesonUHBSTilo Niemann
UOXFStephen GoldingUHBSGeorg Bongartz
UOXFChristopher AlveyAUHKaren Berenth Madsen
UoCNicholas TheocharopoulosAUHJolanta Hansen
UoCKonstantinos ChlapoutakisAUHAnne Grethe Jurik
UoCJohn DamilakisLUMCJob Kievit
UCMLaura Ruiz LopezLUMCJaap Sont
UCMIsabel Salmerón BérlizLUMCAlexander Meijer
UCMEduardo Fraile MorenoLUMCYing-Lie O
UCMAlfonso Calzado CanteraLUMCKoos Geleijns
UOXFUniversity of Oxford, Oxford, United Kingdom
UoCUniversity of Crete, Iraklion, Crete, Greece
UHBSUniversity Hospital Basel, Basel, Switzerland
UCMComplutense University, Madrid, Spain
AUHAarhus University Hospital, Aarhus, Denmark
LUMCLeiden University Medical Center, Leiden, the Netherlands
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Safety and efficacy of computed tomography WP1 Justification
Justification
• multi slice computed tomography MSCT
– new generation CT systems: accurate and fast
– at least 16 slices simultaneously
• safety of MSCT
– radiation effects: cancer and leukaemia
– contrast agents: severe allergic reaction
• efficacy of MSCT
– diagnostic accuracy: sensitivity and specificity
– correct diagnosis: appropriate treatment
– more diagnoses found in anatomic region
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Safety and efficacy of computed tomography WP1 Justification
Medical decision making: modelling to determine
the benefits of MSCT against the adverse effects
! recommendations and guidelines
• Current guidelines are based on previous
generation of CT
• Rapid technological advances in imaging
• The use of CT is growing
• Model-based guideline development
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Safety and efficacy of computed tomography WP1 Justification
• Need for diagnostic imaging
– Precise anatomy: spatial shape and location
– Pathology: intensity of tissue
• Benefits and risks: health-related utilities
• Groups at risk of radiation
– Children
– Pregnant women
– Genetic defects: BRCA
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Safety and efficacy of computed tomography WP1 Justification
• Patient groups who may benefit– good survival if treated properly
– age of interest: 13 - 80
– high risk if diagnosis is missed immediate
– temporary disfunction intermediate
– permanent disfunction long term
• Contrast agents– severe allergic reaction immediate
• Risks of diagnostic radiation– solid cancer long term
– leukaemia intermediate
• Radiosensitivity of Individuals and Susceptibilityto Cancer induced by ionizing RADiationRISC-RAD
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Safety and efficacy of computed tomography WP1 Justification
Medical imaging visualises anatomic regions,
organs, and pathology
• Anatomic regions and organs
– Abdomen: abdominal pain, haematuria, urolithiasis
– Thorax: chest pain
– Head: headache, minor head trauma
– Musculoskeletal: shoulder pain, low back pain
• Pathology
– inflammation and infection
– stenosis, calculus
– bleeding
– obstruction
– degeneration
– lesions, benign and malignant neoplasms
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Safety and efficacy of computed tomography WP1 Justification
Model-based guideline development
for symptom-based indications
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Department of Radiology
Department of Medical Decision Making
Leiden University Medical Center
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Model-based guideline development
Symptom-based: many possible diagnoses
Common modelling approach
• Medical diagnoses:
– probabilistic networks
– many diagnoses
– many tests
• Treatment choice and outcomes:
decision trees, influence diagram
– one disease
– many treatments
– outcome for each treatment
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Model-based guideline development
Model for guideline development
• Includes parts of the medical process
• Visible associations between clinical variables
• Valuation of outcomes to allow comparison
• Adaptation to local situation
And
• Graphical representation
• Understandable for medical and other experts
• Not for direct clinical use
• Suitable for training
Strategy: from comprehensive to restricted
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Model-based guideline development
The model should support the following
• Includes an imaging policy (and advanced tests)
• Includes a treatment policy containing (surgical)
interventions
• Outcomes for presence and absence of disease
• Ranking of probabilities of diseases according to
criteria
• Temporal order of groups (not within a group)
• Policy evaluation by ranking outcomes, not the
best policy
• Input variables may not be recalculated (control
value)
• Logical conditions that depend on variables
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Model-based guideline development
Influence diagram as model for guideline extraction
• Entry point: symptoms, patient groups, diseases
– symptoms in inclusion criteria
– (local) disease a priori
• Diagnostic part: general tests, imaging policy
– level of detail related to urgency
– causal relation between diseases and tests
• Treatment part: treatment policy
– focuses on outcomes
– includes negative treatment
• Outcomes part: disease and treatment outcomes,
health-related utilities
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Model-based guideline development
Probabilistic networks do not support ordering, but
it can be added on control and UI level
Order of probabilities of diseases and outcomes
• Ranking for each patient group and overall
• Criteria for ranking
• Ranking after each recalculation
Temporal order of groups of activities
• Order of activities can be imposed through the UI
• Marked activities may be skipped
• Logical conditions with respect to test results
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Model-based guideline development
Guideline extraction from influence diagram
• Diagnostic part: general tests, imaging policy
– Rank the probabilities after each test
– and for each imaging policy
• Treatment part: treatment policy
– Order the outcomes of most probable diseases
– including negative treatments
– for all treatment policies
Expected problems:
• Large numbers of combinations
• Decision paths not visible
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Model-based guideline development
Example: acute abdominal pain
• Common cause of presentation in emergency
• Many organs in abdomen, difficult to diagnose
• A third have appendicitis
of which a third of is equivocal without imaging
• Misdiagnosis lead to severe complications that
require hospitalisation and may lead to death
• Age group of adolescents and young adults
Categories of possible diseases
• Medical tract: digestive, urinary, …
• Pathology: inflammation and infections, …
• Urgency with respect to bad outcomes
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Model-based guideline development
Example: acute abdominal pain
Order of probabilities for different test results
Order of outcomes of imaging policies and
treatment policies
Disease Appendicitis
Imaging policies only US gold standard decide MSCT gold standard always MSCT gold standard laparoscopy gold standard
present 0,36 0,98 0,90 1,00 0,91 1,00 0,93 1,00
US uncertain uncertain uncertain uncertain
MSCT maybepos maybepos maybepos maybepos
diagnostic laparoscopy positive positive
Treatment policies decide surgery decide surgery decide surgery laparoscopy
surgery no surgery surgery no surgery surgery no surgery laparoscopy missed
Outcomes T+| D+ T-| D+ T+| D+ T-| D+ T+| D+ T-| D+ T+| D+ T-| D+
death 0,00 0,62 0,01 0,10 0,01 0,09 0,01 0,07
disfunction 0,00 0 0,01 0 0,01 0 0,01 0
complication disfunction 0,02 0 0,04 0 0,05 0 0,05 0
complication 0,02 0 0,04 0 0,05 0 0,05 0
recovery 0,32 0 0,79 0 0,81 0 0,82 0
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Model-based guideline development
Example: acute abdominal pain
• Order of probabilities for different test results
" suspected appendicitis
• Order of outcomes of imaging policies and
treatment policies
1. Laparoscopy: combination of diagnosis and
intervention
2. Always MSCT followed by surgery
3. Decide for MSCT followed by surgery
4. Only ultrasound followed by surgery
• Not modelled:
– Consequences of time delay
– Specific adverse effects of laparoscopy
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Model-based guideline development
Guidelines for acute abdominal pain in case of
suspected appendicitis
• Conventional guidelines include ultrasound (low
sensitivity)
• “Always MSCT” gives a good outcome
• MSCT also detects other diseases
• Laparoscopy gives the best outcome, but may
have severe adverse effects (not modelled)
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Model-based guideline development
Modelling “prescription” for guideline development
• Needs support of the modelling tool
• Needs a good user interface for development
• Needs a tailored user interface for visualisation
and training
Challenges
• Specific modelling constructs
– Noisy-OR for the same underlying pathology
– Sub-models for different pathologies?
– Time delay in conditional probabilities
• Evidence-based data and expert knowledge
• Multi-attribute health-related utilities