The Use of a Nomogram to Visually Interpret a Logistic Regression Prediction Model for Giant Cell Arteritis

ABSTRACT

Objective: To illustrate the utility of a nomogram for the prediction of giant cell arteritis (GCA).

Method: 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.

Results: The magnitude and location of the nomogram scale for each predictor variable graphically illustrates the net effect of each covariate and is especially useful for continuous variables such as age and bloodwork values.

Conclusions: 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. Although nomograms and prediction rules cannot substitute for clinical judgment, they help objectify and optimize the individualized risk assessments for patients with suspected GCA.

KEYWORDS:

• Giant cell arteritis
• temporal artery biopsy
• prediction model
• nomogram

Disclosure statement

No financial disclosures or conflicts of interest.