Mini-Lecture for CPR_051511
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Transcript of Mini-Lecture for CPR_051511
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Contents
What is CPR?
Why is it important for us to do CPR studies
Development of CPR Validation of CPR
Implementation of CPR
Progress in CGMH
Potential collaboration
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Contents
What is CPR?
Why is it important for us to do CPR studies
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What is CPR?
Terminology
Clinical Prediction Rule
Clinical Prediction Model
Clinical Decision Rule
Riskscores
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Prediction models vs.
Diagnostic testsDiagnostic test Prediction model
Can be single test/device or
combo of predictorsAlmost always combo ofpredictors
May involve models Always involves models
No limit on predictability Predictability is limited
May need validation Always needs validation
Affected by dz prevalence Affected by dz prevalence
Applied to symptomatic pop Can be applied to all
Context and use usually fairlyspecific
.Can be used for many
purposes and settings.
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CPR in MedCalc
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CPR in MedCalc
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Examples of CPRABCD2 score
Alvarado score
Blatchford score
Canadian C-spine rule
Canadian CT Head rule
CAP PIRO score
CATCH rule
CHADS2 score
CHA2DS2-VASc Score
CURB-65 score
Geneva score for PE
HAS-BLED score
NEXUS C-spine rule
NEXUS II CT-Head rule
Ottawa Knee rule
PERC
PSI
Ransons score
Rockall score
San Francisco syncope rule
SIRS
SOFA score
TIMI score
VAP PIRO score
Well score for DVT
Well score for PE
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Examples of CPRNeuro
ABCD2 score
Trauma
Canadian C-spine rule
Canadian CT Head ruleCATCH rule
NEXUS C-spine rule
NEXUS II CT-Head rule
Ottawa Knee rule
GI
Alvarado score
Blatchford score
Ransons score
Rockall score
ID
CAP PIRO score
CURB-65 score
PSI
SIRS
SOFA score
VAP PIRO score
CV
CHADS2 score
CHA2DS2-VASc Score
Geneva score for PEHAS-BLED score
PERC
San Francisco syncope rule
TIMI score
Well score for DVT & PE
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Niche for CPR?
Clinical decision making
High clinicalstakes
Achieve costsavings /s compromising patient care
ID high risk persons
forpreventive interventions
forclinicalorepidemiologicalstudies
Medical/biologic insight
Patient/family planning purposes
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Any evidence based benefit?
Pneumonia Severity Index*
> 50,000patients with CAP
Safelyincrease % patients treated as outpatient
Reduce Length of Stay
butnotReduce Quality of Life
*Aujesky et al, CID 2008
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Contents
Development of CPR
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Development of CPR
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Development of CPR
Published articles: 6,744 in 1995 -> 15,662 in 2005* Mainly development
Lack of validation & impact analysis
Begin: constructing a list of potentialThen: examine a and the factors
Statistical analysis
Discriminant analysis
Neural network
Random forest *Toll el al. JCE 2008
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Development of CPRRegression
Example: SBP = 33 + 1.45 * DBP
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Development of CPRRegression
ABCD2 for TIA
Hazard of TIA= A + B + C + D + D
A = Age 60 years -> 1 point B = SBP > 140 or DBP 90 mmHg -> 1 point
Clinical feature
Unilateral weakness -> 2 point
Speech disturbance /s weakness -> 1 point
Duration of symptoms
60 minutes -> 2 point
10 < 59 minutes -> 1 point
Diabetes -> 1 point
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Development of CPRRegression
ABCD2 for TIA*
Hazard of TIA
*Rothwell et al. Lancet 2005
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Development of CPRRegression
ABCD2 for TIA
Hazard of TIA = 2.57Age + 9.67BP + 6.61Clinical1 +
2.59Clinical2 + 6.17Duration1 + 3.08Duration2 +4.39DM
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Development of CPRRegression
ABCD2 for TIA
Hazard of TIA = 2.57Age + 9.67BP + 6.61Clinical1 +
2.59Clinical2 + 6.17Duration1 + 3.08Duration2 +4.39DM
Hazard of TIA = Age + Hypertension + 2*Clinical1 +Clinical2 + 2*Duration1 + Duration2 + DM
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Development of CPR
Recursive partitioning analysis
Canadian C-Spine Rule
*Stiell, JAMA 2001
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Development of CPR
Recursive partitioning analysis
San Francisco Syncope Rule
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Development of CPR
Recursive partitioning analysis
San Francisco Syncope Rule
*Quinn et al, AEM 2004
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Development of CPR
Recursive partitioning analysis
Non-parametric, tree based
Partitioning subjects by selecting variables recursively
Strengths No assumption for
Can deal with high dataset
Sophisticated methods for
Unaffected by , Weakness
Poor in modeling structure
Not model, no
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Admission 73
Discharge 69
High VL 10
Low VL 2
High VL 23
Low VL 47
High VL 33
Low VL 49
High VL 4
Low VL 29
High VL 19
Low VL 18
High VL 6
Low VL 5
High VL 13
Low VL 13
Age < 18m/oAge > 18m/o
Age > 5.5Age < 5.5
Prediction Tree for Viral Load in Pediatrics
High VL 6
Low VL 10
High VL 7
Low VL 3
Abnormal ANCNormal ANC
No underlying illness Any underlying illness
40%
33% 83%
12% 51%
55%50%
38% 70%
Chen et al, in preparation
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Development of CPR
Discriminant analysis
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Development of CPR
Neural networkNeural Networks
X1 X2 X3 X4 Xp-1 Xp!!!!!!!
Z1 Z2 Z3 Z4 Z5
Y1 Y2
Derived variables
Original predictors
Outcome indicator variables
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Development of CPR
Random forest
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Development of CPR
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Development of CPR
Questions:
How were predictors chosen and defined
How were study subjects selected
Was the sample size adequate
including number of outcome events
Were allimportant predictors present
Does the rule make clinicalsense
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Development of CPR
Guidelines
Ratio of predictors to patients
Methods of predictors selection
Weight assignment
Shrinkage of coefficients to prevent over-fitting
Estimate the potential by internal validation
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Development of CPR
Disappointing accuracy
Example: Children with FUO
AUC of derivation study: 0.76, AUC of validation study: 0.57
Reason Inadequately developed
Difference between derivation & validation population (generalizability)
Different definition of predictor and outcome variables
Should Only include good reliability (inter-observer variability)
Case-mix: EuroSCORE study
Fewer individuals in validation studies
At least 100 events and 100 nonevents
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Hierarchy evidence of CPR
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Level I:
Rules that can be used in a wide variety of settings with confidence thatthey can change clinician behavior and improve patient outcomes
At least one validation in a and one, demonstrating change in clinician with beneficial
consequences.
Level II:Rules that can be used in various settings with confidence in theiraccuracy
Demonstrated accuracy in either one study including aof patients and clinicians, orvalidated in
settings who differ from one another.
Level III:
Rules that clinicians may consider using with caution and only if patients
in the study are similar to those in your clinical settingThese rules have been in only one sample.
Level IV:
Rules that need further evaluation before they can be applied clinicallyThese CPRs have been but or have only been validated
in split samples, large databases, or by techniques.
Hierarchy evidence of CPR
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Evaluation stage of CPRs
Stage 0:Pick up a good CPR to use:
e.g. Sepsis/syncope/pneumonia
Stage 1:Validate it by retrospectively
review in one setting, modify if performance
sub-optimal
Stage 2:Validate it narrowly by
prospective review in one setting
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Evaluation stage of CPRs
Stage 3:Validate it broadly by prospective
review in varied setting with wide spectrum
of patients and physicians
Stage 4: Narrowimpactanalysis of CPR
used as decision rule
Stage 5: Broad impact analysis of CPR as
decision rule
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Reasons:
Due to chance?
different set of predictors will emerge
Statistical methods:
Idiosyncratic ?
fail in a new setting Feasibility?
succeeds in theor but fails in practice
Validation of CPR
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Questions:
Pt chosen in fashion?
Wide spectrum of severity of disease?
assessment of criterion standard for all?
Explicit & accurate of variables? 100% -up?
Validation of CPR
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Types of validation
Temporal
Geographical
Domain
Types of impact analysis
Randomized cluster/community trials
Before-after analysis
Validation of CPR
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Predictive power
Sensitivity and specificity
Confidence interval
Likelihood ratios, or as absolute or relative risks.
ROC (receiver operative characteristics)
Validation of CPR
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Change in behavior ? intuitive estimation vs CPR?
Comparing physicians vs. Rules prediction Face validity
User friendly?
! HIS/EPR
! CDSS (clinical decision support system)
practical barriers (litigation!)
Impact analysis of CPR
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Contents
Progress in CGMH
Potential collaboration
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CPRs @ CGMH
Stage0: ,CPR
Stage1: chart review,(>80% sensitivity, >0.8 AUC)
modifyderiveCPR,
Stagedatabasederivation,missing data,prospectivelydatabase(validation)
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CPRs @ CGMH
Stage2: stageCPR,prospective validation
single settingvalidation,
stage,modify/derive
CPRCDSS(clinical decision support system),Mars-Tprospectively
Stage3: multi-centersprospective validateCPR sample size
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Stage4: ()seminar
before-after,CPR
(impact)practice
Stage5: ()impact analysis
CPRs @ CGMH
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Focused group
CPRs @ CGMH
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Shared information
CPRs @ CGMH
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Resource integration
IT: HIS information extraction
CPRs @ CGMH
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Database management: Access + SQL + VBA
Novel MARS-T system
Collaboration with faculty in CGU
NSC project: Comparative Effectiveness ResearchInitiative in Clinical Study
CPRs @ CGMH
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CPRs @ CGMH + SKH ?
Impact analysis
Larger prospective development/validation
Longitudinal & cross-sectional collaboration
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Questions?