Multivariable prediction model for suspected giant cell arteritis: development and validation

Abstract

Purpose: To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA).

Methods: A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL), diplopia, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and platelet level. Multiple imputation was performed for missing data. Logistic regression was used to compare our models with the non-histologic American College of Rheumatology (ACR) GCA classification criteria. Internal validation was performed with 10-fold cross validation and bootstrap techniques. External validation was performed by geographic site.

Results: There were 530 complete TABx records: 397 were negative and 133 positive for GCA. Age, jaw claudication, VL, platelets, and log CRP were statistically significant predictors of positive TABx, whereas ESR, gender, headache, and temporal artery abnormality were not. The parsimonious model had a cross-validated bootstrap area under the receiver operating characteristic curve (AUROC) of 0.810 (95% CI =0.766-0.854), geographic external validation AUROC's in the range of 0.75-0.85, calibration p_H-L of 0.812, sensitivity of 43.6%, and specificity of 95.2%, which outperformed the ACR criteria.

Conclusion: Our prediction rule with calculator and nomogram aids in the triage of patients with suspected GCA and may decrease the need for TABx in select low-score at-risk subjects. However, misclassification remains a concern.

Keywords: diagnosis; giant cell arteritis; nomogram; prediction rule; temporal artery biopsy; validation.

Figure 1

ROC curves for full, parsimonious and ACR models. Notes: Full model (n=530) p_{Hosmer–Lemeshow} =0.549. Parsimonious model (n=530) p_{Hosmer–Lemeshow} =0.812. ACR model = (n=525). p_{Hosmer–Lemeshow} =0.0223 (Five patients under the age of 50 years were excluded from logistic regression.). Abbreviations: ROC, receiver operator characteristics; ACR, American College of Rheumatology Classification non-histologic Criteria.

Figure 2

External geographic validation results of the highest (A) and lowest ranking datasets (B).

Figure 3

Prediction risk profile using the full model and Case 4 of Table 6. Notes: Claudication, jaw claudication; CRP_adj, log (CRP divided by the upper limit of normal CRP). In this hypothetical case, an 80-year-old male has jaw claudication and CRP that is elevated twice normal, but no headache, temporal artery tenderness, or diplopia. The ESR is <50, and the platelet levels are normal. The risk of biopsy-proven GCA is 28% if there is no vision loss (A), but 52% in the setting of ischemic vision loss (B). Abbreviations: CRP, C-reactive protein; GCA, giant cell arteritis; ESR, erythrocyte sedimentation rate; GCAonBx, biopsy-proven giant cell arteritis.

Figure 4

Nomogram of parsimonious model. Notes: The length and location of each nomogram scale indicates the relative importance of the predictor variable. A vertical line is drawn down from the value of each covariate to determine the score. The sum of the scores is used to determine the probability for a positive temporal artery biopsy. Abbreviations: CRP, C-reactive protein; ULN, upper limit of normal.