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Tools for Decision Making Sections
Author Bio
Introduction
Probability Theory
Case Study 1: Patient History
Bayes' Theorem
Currently selected section: Methods for Estimating Pre-test Probability
Estimating Likelihood Ratios
Sensitivity and Specificity
Interpreting Test Results
Calculating Post-test Probabilities
Post-test Probabilities in Clinical Practice
Conclusions: Case Study 1
Part II
Part III
References


Chapter 14: Tools for Decision Making: Methods for Estimating Pre-test Probability
        

Disease Prevalence Estimates in Patients with Similar Symptoms

It is possible to estimate pre-test probability based on the prevalence of a disease in other patients with the same syndrome. This method is straightforward: the physician uses the frequency of disease as established in a series of patients with the same clinical syndrome as the patient.

The best example of this method is the diagnosis of suspected coronary artery disease in patients with chronic chest pain. On the basis of the clinical history, the physician can place the patient in one of three categories:

  • Typical angina pectoris
  • Atypical angina
  • Non-anginal chest pain

Many published studies have measured the frequency of angiographically proven coronary disease in patients with these syndromes. The table shows representative results.

Table 1.5.1: Probability of Coronary Artery Disease
Chest pain history Men Women
Atypical chest pain0.14
0.05
Atypical angina 0.660.36
Typical exertional angina 0.93 0.72


Question 1.5.2 - Applying the Method

To apply the disease prevalence method, you would have to decide what chest pain syndrome the patient has. You may want to re-read his history.

Click to review patient history

Then, select the syndrome you think the patient is most likely to have.

Selection ATypical angina pectoris
Selection BAtypical angina
Selection CNon-anginal chest pain
Selection DNone of the above

Research Opportunities: Disease Prevalence Estimates

Estimating pre-test probability based on the prevalence of disease in other patients with the same syndrome can offer some intriguing research opportunities. For example, you might investigate patients with similar complaints, but who give different histories.

Clinicians usually have opinions about what features of the history are most worrisome, and it might be useful to find a way to quantify this. Your study might establish criteria for classifying a patient's history as worrisome and reassuring. You could take the history on a series of patients, using a data collection form to remind you to gather the key data that would place the patient in one category or the other. Once you have classified the patient, you could do a careful follow-up to establish the diagnosis and the final outcome. These data would allow you to establish the probability of disease in worrisome and reassuring histories as well as to establish the prognosis as a function of the patient's history.

 

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