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5 Written questions

5 Multiple choice questions

  1. 12 lead ECG monitored at:
    - rest
    - last minute of each stage
    - each minute during recovery)
    BP monitored at:
    - rest
    - last minute of each stage unless response is abnormal
    - each minute during recovery
    RPE monitored at:
    - last minute of each stage
    - ask more often towards end of test
    Symptoms:
    - include angina or chest pain
    - dyspnea or SOB
    - ask about other discomforts
  2. - Age - as you age, pretest likelihood increases
    - Gender - women have lower pretest likelihood than men
    - Symptoms - those w/ symptoms have much higher pretest likelihood
  3. 1) Determine # of ppl in population w/ disease
    2) Determine # w/ disease that will have '+' testing (# w/ disease x 0.7)
    3) Determine # w/ disease that will have '-' test (Total diseased # - # w/ '+' test)
    4) Do the same for those w/o disease
  4. - 3-4 on angina scale
    - drop in SBP >= 10 mmHg
    - ventricular tachycardia
    - fatigue
  5. - treadmill
    - leg ergometer
    - arm ergometer

5 True/False questions

  1. What is pretest likelihood?- treadmill
    - leg ergometer
    - arm ergometer

          

  2. List some absolute contraindications to exercise- unstable angina
    - acute infection
    - uncontrolled arrhythmia

          

  3. List some typical reasons that people are referred to a clinic for exercise testing- Diagnosis - do they have CVD (most used)
    - Disease severity and prognosis in patients w/ CAD
    - Evaluation of medical therapy
    - Stratify patients for further medical intervention (e.g. determine if person is good patient for heart transplant)
    - Functional capacity for activity counseling and exe program

          

  4. Describe the 5 aspects of the bayesian method of probability- treadmill
    - leg ergometer
    - arm ergometer

          

  5. What is the formula used to determine positive predictive value and negative predictive value- Positive Predictive Value = True Positive/(TP + false positive)
    - Negative Predictive Value = True Negative/ (TF + False Negative)

          

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