# Fagan Nomogram calculator

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

### 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:

**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.**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%.**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.**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.**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.**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.**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