Mini-Lecture for CPR_051511

8/6/2019 Mini-Lecture for CPR_051511

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1Monday, May 16, 2011


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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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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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ABCD2 for TIA*

Hazard of TIA

*Rothwell et al. Lancet 2005



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ABCD2 for TIA

Hazard of TIA = 2.57Age + 9.67BP + 6.61Clinical1 +

2.59Clinical2 + 6.17Duration1 + 3.08Duration2 +4.39DM



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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


San Francisco Syncope Rule



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Development of CPR


San Francisco Syncope Rule

*Quinn et al, AEM 2004



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Development of CPR


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?

Documents

Mini-Lecture for CPR_051511