Better Medical Better Medical Diagnostic Diagnostic

Decisions thru Decisions thru ScienceScience

V. Froelicher, MDProfessor of MedicineStanford UniversityVA Palo Alto HCS

Optimal Clinical Application of Optimal Clinical Application of Exercise ECG TestingExercise ECG Testing

What are the Questions being asked regarding Coronary

Disease and Exercise Testing Does this patient have or not have Coronary Disease?Is this patient going to experience a Cardiac Event? Better decisions are made possible by applying the following two methods to clinical and exercise test data:

Scientific Decision Methods

Statistical Prediction Rules = Scores

Receiver Operator Characteristic Curves

Statistical Prediction Rules

Based on mathematical models or equations that can be simplified as scores They increase accuracy by enhancing the odds that any decision will be correct (a reliable second opinion)

Clinical ScoresClinical Scores1. Predicting Outcomes

Follow up required (time, complete)Endpoint Limitations (Death, CABG)No Natural History

2. Predicting Angiographic FindingsInstant EpidemiologyLimitations of AngiographySub-ischemic Lesions cause events

Making any of these Five Mistakes Evaluating Diagnostic Tests can invalidate Scores &

Stats

Limiting the population Challenge by choosing extremes Failure to reduce Work up biasUse of Heart rate targetsInclusion of MI patients Use of Surrogates

Making any of these Four Mistakes Evaluating Prognostic Tests can invalidate Scores &

Stats

Limited Challenge and work up biasIncomplete Follow upFailure to CensorUsing Misleading Endpoints

Clinical ScoresClinical Scores

1. Survival AnalysisBased on Follow-up and CensoringCox Hazard Function; time to event rather than proportion differencesWeighted Coefficients used to construct Equations for Scores and Nomogram

2. Probability of Coronary DiseaseBased on AngiographyMultiple Logistic RegressionCoded Variables x Coefficients added then solved in Natural Log Equation to fit a Sigmoid Curve

Paradigm for Matching the Clinical Management Strategy to the Estimated Probability of CAD

Probability for clinically significant CAD

Low probabilityLow probability

Patient reassured symptoms most likely not due to CAD

Intermediate probabilityIntermediate probability

Require other tests, such a stress echo, nuclear, or angiography to clarify diagnosis; anti-anginal medications tried.

High probabilityHigh probability

Anti-anginal treatment indicated; intervention if clinically appropriate; angiography usually required

Meta Analysis of Prognosis in Stable CAD

Poor Exercise Capacity 6/9CHF 3/9ST Depression Resting 2/9 Exercise 3/9Exercise SBP 3/9

Exercise Test and Cath (N=9)

Meta Analysis of Prognosis in Stable CAD

Exercise induced ST depression not consistently a predictorExercise Capacity usually a predictorTwo Studies have used Cox Hazards Function to chose variables significantly and independently associated with time to CV event (hard events, not CABG)

Exercise Test and Cath (N=9)

Prognostic Scores in Stable CAD

DUKE SCOREMETs - 5 X [mm E-I ST Depression] -

4 X [Treadmill Angina Index]******see Nomogram*******

VA SCORE 5 X [CHF/Dig] + [mm E-I ST Depression]

+ change in SBP score - METsE-I = Exercise Induced

Duke Treadmill Score (uneven lines)

Prediction of Prognosis

Censoring when lost to follow up or when an intervention performed that alters outcome

All-cause mortality, infarct-free survival or cardiovascular death

Predictors of MI and death differ

Prediction of Prognosis

What to do with patients who have Interventions that could alter

Outcomes?Exclude from analysisIgnoreUse as endpoints (after a time lag)Censor (end time of follow up)Partial censoring?

The HR Recovery Studies Hi-light problems with

Prediction of PrognosisFailure to censor results in prediction of outcome after application of standard therapiesDoes not allow for prediction of who should receive therapies or interventionsFailure to censor and use infarct-free survival or cardiovascular death negates development of strategies or scores for treatment of CAD

Diagnostic Scores:

ACC/AHA guidelines state that multi-variable equations should be used to enhance the diagnostic characteristics of the exercise treadmill test.

The Equations are often not applied in practice because of their complexity

Multi-Variable Logistic Regression

Probability (0 to 1) = 1 / (1 + e - (a + bx + cy . . . ))

where a = intercept, b and c are coefficients, x and y are variable values.

For instance:x = age, y= chest pain type, z = diabetes ….

Meta Analysis of 24 Studies Predicting Angiographic CAD

Most consistent clinical variables chosen were:

Gender, Chest pain type, Age and Hypercholesterolemia

Most consistent exercise test variables chosen were:

ST depression and slope, Maximal Heart rate and exercise capacity (METs)

Problems with Scores

CensoringFollow-up Confounded by InterventionsThe major Mistakes for evaluating TestsDifferences between Studies as to Variables and Their Coding Skepticism that Scores Can Be Better Than Physician EstimatesRequire Nomograms or Computers to Calculate the PredictionAre they Portable?

Simplified Score:

Derivation of a simplified treadmill score based on multi-variable statistical techniques

Validation of this treadmill score in another population and comparison to the ST response alone and the Duke treadmill score

Methods:Clinical and exercise test variables were coded: Continuous and dichotomous variables all set from 0 to 5 (five cells for continuous, yes = 5) for proportionality

Graded as 0 for good and 5 for bad

The coded variables were entered into a standard logistic regression model to discriminate between those with and without angiographically significant CAD (equal or greater than 50%)

Methods:The derived equation was then Simplified by dividing all Coefficients by the least coefficient so that they all became multiples of one

The Simplified score then was created by adding the variables after scoring and multiplication It was compared to the logistic regression equation results by ROC analysis and found to be equivalent

Variable Circle response

Sum

Maximal Heart Rate Less than 100 bpm = 30

100 to 129 bpm = 24

130 to 159 bpm =18

160 to 189 bpm =12

190 to 220 bpm =6

Exercise ST Depression

1-2mm =15

> 2mm =25

Age >55 yrs =20

40 to 55 yrs = 12

Angina History Definite/Typical = 5

Probable/atypical =3

Non-cardiac pain =1

Hypercholesterolemia?

Yes=5

Diabetes? Yes=5

Exercise test Occurred =3

induced Angina Reason for stopping =5

Total Score:

MalesChoose

only one per

group

<40=low prob

40-60= intermediate probability

>60=high probability

Scientific Decision Methods

Statistical Prediction Rules = Scores

Receiver Operator Characteristic Curves

Receiver Operator Characteristic Curves

Improve the utility of decision-making approaches ensuring that the number of true cases diagnosed does not come at the cost of too many false positives (“false alarms”)

Allows comparison of the diagnostic ability of competing diagnostic techniques and scores

Overlapping, not separate; the further apart, better the test

Other Mxmnts: EBCT Calcium, Echo WMA, Nuclear, ST depr

Specificity

Sensitivity

Inverse relationship

cutpoint

Chest Pain

Screening

AUC

AUC

But Can Physicians do as well as the Scores? 954 patients - clinical/TMT

reports Sent to 44 expert cardiologists,

40 cardiologists and 30 internists Scores did better than all three

but were most similar to the experts

Two ways to compare the discriminatory/diagnostic characteristics of a test/

measurement

1.Range of Characteristic curves –unaffected by prevalence, can be used to choose cut points, require continuous variables

2.Predictive Accuracy – TP+TN/pop, requires dichotomy, same prevalence to compare

Methods of Test comparison:

ROC Plots 1 perfect discrimination, .50 none Not dependent upon prevalence of

disease

Predictive Accuracy Percent of total true calls (TP +TN) Dependent upon prevalence of

disease

Comparison of Tests Grouping # of

Studies Total #

Patients Sens Spec Predictive

Accuracy Standard ET 147 24,047 68% 77% 73%

ET Scores 24 11,788 80%

Score Strategy 2 >1000 85% 92% 88%

ThalliumScint 59 6,038 85% 85% 85%

SPECT 16+14 5,272 88% 72% 80%

Adenosine SPECT 10+4 2,137 89% 80% 85%

Exercise ECHO 58 5,000 84% 75% 80%

Dobutamine ECHO 5 <1000 88% 84% 86%

Dobutamine Scint 20 1014 88% 74% 81%

Electron Beam Tomography (EBCT)

16 3,683 60% 70% 65%

Conclusions:

Scores can predict the presence of CAD better than ST analysis alone.

Scores can predict Prognosis Scores can provide a valuable second

opinion (as good as experts).Scores reduce the effect of physician

biasScores provide a management Strategy

Conclusions:

Scores can optimize the clinical application of the standard exercise ECG Test.

The Duke Treadmill Test Score and VA/WV Simple Score should be part of every exercise test interpretation