A nomogram is a chart or graph of scaled variables that facilitates the approximate computation of a mathematical function via intersecting lines. The objective of this study was to illustrate the use of a nomogram for the prediction of giant cell arteritis (GCA). A nomogram was constructed from a multivariable logistic regression prediction model with 10 covariates: age, sex, clinical temporal artery abnormality, new-onset headache, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein and platelet level. The magnitude and location of the nomogram scale for each predictor variable is shown to graphically illustrate the net effect of each covariable and is shown to be especially useful for continuous variables such as age and bloodwork values. The authors conclude that nomograms allow integration and synthesis of the relative importance of clinical variables and provide a graphic representation of the odds ratios, p values and confidence intervals of logistic regression prediction models. They conclude that although nomograms and prediction rules cannot substitute for clinical judgement, they can help objectify and optimise the individualised risk assessments for patients with suspected GCA.