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Fagan Nomogram calculator

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Fagan's Nomogram calculator is a tool for estimating how much the result of a diagnostic test changes the probability that a patient has a disease. This online calculator provides a quick way to compute the post-test probability based on the pre-test probability, sensitivity, specificity, and test result.

Fagan's Nomogram Calculator

50%

Post-test Probability (%): -

Guide on using Fagan Nomogram calculator

Step 1: Set the Pre-test Probability:

  • Action: Use the "Pre-test Probability (%)" slider.
  • Example: If you believe there's a 30% chance the patient has a condition before considering the test results, slide the bar to 30%.

Step 2: Input the Sensitivity and Specificity:

  • Action: Use the "Sensitivity (%)" and "Specificity (%)" input boxes.
  • Example: If a diagnostic test has a sensitivity of 95% (meaning it correctly identifies 95% of patients with the disease) and a specificity of 90% (meaning it correctly identifies 90% of patients without the disease), enter 95 in the Sensitivity box and 90 in the Specificity box.

step 3: Select the Test Result:

  • Action: Choose from the dropdown menu labeled "Test Result".
  • Example: If the patient's test result was positive, select "Positive" from the dropdown. If negative, choose "Negative".

Step 4: Calculate Post-test Probability:

  • Action: Click the "Calculate Post-test Probability" button.
  • Example: Upon clicking the button, the post-test probability will be displayed both as a numeric percentage and visually with a red needle on a scale. If it shows 75%, this indicates there's now a 75% probability of the patient having the disease, considering the test result.

Guide to Determine Pre-test Probability:

  1. Clinical Judgment: Use your clinical experience and understanding of the patient's presentation, history, physical examination, and other available data to make an initial assessment. For example, a patient with chest pain might have a higher pre-test probability for coronary artery disease if the pain is exertional and associated with risk factors like diabetes, smoking, and family history.
  2. Known Disease Prevalence: For many conditions, the prevalence in a given setting or population is known. Using this information can help set an initial pre-test probability. For instance, if you're testing for a disease that's known to affect 5% of a certain population, then your pre-test probability might start at 5%.
  3. Use of Clinical Decision Rules: Certain clinical decision rules or prediction models exist for various conditions. These are algorithms or scoring systems derived from research that combines several clinical variables to predict the probability of a disease.
  4. Consider Context: The setting can play a big role in pre-test probability. For instance, the pre-test probability for community-acquired pneumonia is different in an outpatient setting versus an intensive care unit.
  5. Reviewing Previous Literature: Studies or meta-analyses that have been published about the condition in question can provide insights into the pre-test probability, especially if they have been conducted in a similar setting or population.
  6. Adjust Based on Additional Information: As more clinical information becomes available, the pre-test probability can be adjusted. For example, the absence of certain symptoms might lower the pre-test probability.
  7. Expert Consultation: If uncertain, consulting with a specialist or colleague can provide additional insight into determining a pre-test probability based on their expertise.

References

TJ F. Nomogram for Bayes's theorem. N Engl J Med. 1975;293: https://www.nejm.org/doi/full/10.1056/NEJM197507312930513