Presentation on theme: "How to Use Heart Rate Changes to Improve Exercise Test Results V. Froelicher, MD Professor of Medicine Stanford University VA Palo Alto HCS."— Presentation transcript:
How to Use Heart Rate Changes to Improve Exercise Test Results V. Froelicher, MD Professor of Medicine Stanford University VA Palo Alto HCS
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? Is an invasive intervention appropriate?
Why do we need more than ST depression? Classic criteria of one mm ST depression has low sensitivity and hi specificity Other more expensive modalities appear to have better discriminatory characteristics Exercise ST depression has not been prognostic in all studies
Goal is convince you: Functional capacity is the strongest predictor of prognosis and Heart rate recovery adds to it. Maximal Heart rate is an important part of diagnostic scores We should stop worshipping ST segments and cardiac cath for prognostic and diagnostic assessments.
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 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
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
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
Population Selection Critical!! Consecutive patients presenting with the problem for evaluation Limit work up bias Avoid limited challenge
Duke Treadmill Score (uneven lines, the elderly?)
The HR Recovery Studies Hi-light problems with Prediction of Prognosis Failure to remove patients with interventions results in prediction of outcome after application of standard therapies Failure to use infarct-free survival or cardiovascular death as outcome negates development of strategies or scores for treatment of CAD Does not allow for prediction of who should receive therapies or interventions
Cause of deathAgeVariableHR Resting ST depression1.7 Delta PRP1 65CABS History1.7 yearsMI /Q waves1.5 CardiacAbnormal exercise ST1.2 METs.9 65 CABS History1.9 yearsMI /Q waves1.7 Abnormal exercise ST1.2 METs.9 65Resting ST depression1.5 All causesyearsDelta PRP1 METs.9 65 years Abnormal resting ECG1.4 CABS History1.4
The ST/HR index studies highlight the problem of limited challenge Comparison of the sickest to the most well exaggerates the discriminatory value The well have high heart rates, the sick have low maximal HRs
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