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Better Medical Diagnostic Decisions thru Science V. Froelicher, MD Professor of Medicine Stanford University VA Palo Alto HCS Optimal Clinical Application.

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Presentation on theme: "Better Medical Diagnostic Decisions thru Science V. Froelicher, MD Professor of Medicine Stanford University VA Palo Alto HCS Optimal Clinical Application."— Presentation transcript:

1 Better Medical Diagnostic Decisions thru Science V. Froelicher, MD Professor of Medicine Stanford University VA Palo Alto HCS Optimal Clinical Application of Exercise ECG Testing

2 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:

3 Scientific Decision Methods Statistical Prediction Rules = Scores Receiver Operator Characteristic Curves

4 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)

5 Clinical Scores 1.Predicting Outcomes Follow up required (time, complete) Endpoint Limitations (Death, CABG) No Natural History 2.Predicting Angiographic Findings Instant Epidemiology Limitations of Angiography Sub-ischemic Lesions cause events

6 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 bias Use of Heart rate targets Inclusion of MI patients Use of Surrogates

7 Making any of these Four Mistakes Evaluating Prognostic Tests can invalidate Scores & Stats Limited Challenge and work up bias Incomplete Follow up Failure to Censor Using Misleading Endpoints

8 Clinical Scores 1. Survival Analysis Based on Follow-up and Censoring Cox Hazard Function; time to event rather than proportion differences Weighted Coefficients used to construct Equations for Scores and Nomogram 2. Probability of Coronary Disease Based on Angiography Multiple Logistic Regression Coded Variables x Coefficients added then solved in Natural Log Equation to fit a Sigmoid Curve

9 Paradigm for Matching the Clinical Management Strategy to the Estimated Probability of CAD Probability for clinically significant CAD Low probability Low probability Patient reassured symptoms most likely not due to CAD Intermediate probability Intermediate probability Require other tests, such a stress echo, nuclear, or angiography to clarify diagnosis; anti-anginal medications tried. High probability High probability Anti-anginal treatment indicated; intervention if clinically appropriate; angiography usually required

10 Meta Analysis of Prognosis in Stable CAD Poor Exercise Capacity 6/9 CHF3/9 ST Depression Resting2/9 Exercise3/9 Exercise SBP3/9 Exercise Test and Cath (N=9)

11 Meta Analysis of Prognosis in Stable CAD Exercise induced ST depression not consistently a predictor Exercise Capacity usually a predictor Two 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)

12 Prognostic Scores in Stable CAD DUKE SCORE METs - 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 - METs E-I = Exercise Induced

13 Duke Treadmill Score (uneven lines)

14 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

15 Prediction of Prognosis What to do with patients who have Interventions that could alter Outcomes? Exclude from analysis Ignore Use as endpoints (after a time lag) Censor (end time of follow up) Partial censoring?

16 The HR Recovery Studies Hi-light problems with Prediction of Prognosis Failure to censor results in prediction of outcome after application of standard therapies Does not allow for prediction of who should receive therapies or interventions Failure to censor and use infarct-free survival or cardiovascular death negates development of strategies or scores for treatment of CAD

17 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

18 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 ….

19 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)

20 Problems with Scores Censoring Follow-up Confounded by Interventions The major Mistakes for evaluating Tests Differences between Studies as to Variables and Their Coding Skepticism that Scores Can Be Better Than Physician Estimates Require Nomograms or Computers to Calculate the Prediction Are they Portable?

21 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

22 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%)

23 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

24 Variable Circle responseSum 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: Males Choose only one per group <40=low prob 40-60= intermediate probability >60=high probability

25 Scientific Decision Methods Statistical Prediction Rules = Scores Receiver Operator Characteristic Curves

26 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

27 Overlapping, not separate; the further apart, better the test Other Mxmnts: EBCT Calcium, Echo WMA, Nuclear, ST depr

28 Specificity Sensitivity Inverse relationship cutpoint

29 Chest Pain Screening AUC


31 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

32 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

33 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

34 Comparison of Tests

35 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 bias Scores provide a management Strategy

36 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

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