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

Edsel B Ing; Gabriela Lahaie Luna; Andrew Toren; Royce Ing; John J Chen; Nitika Arora; Nurhan Torun; Otana A Jakpor; J Alexander Fraser; Felix J Tyndel; Arun Ne Sundaram; Xinyang Liu; Cindy Ty Lam; Vivek Patel; Ezekiel Weis; David Jordan; Steven Gilberg; Christian Pagnoux; Martin Ten Hove

doi:10.2147/OPTH.S151385

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

Clin Ophthalmol. 2017 Nov 22:11:2031-2042. doi: 10.2147/OPTH.S151385. eCollection 2017.

Authors

Edsel B Ing¹, Gabriela Lahaie Luna², Andrew Toren³, Royce Ing⁴, John J Chen⁵, Nitika Arora⁶, Nurhan Torun⁷, Otana A Jakpor⁸, J Alexander Fraser⁹, Felix J Tyndel¹⁰, Arun Ne Sundaram¹⁰, Xinyang Liu¹¹, Cindy Ty Lam¹, Vivek Patel¹², Ezekiel Weis¹³, David Jordan¹⁴, Steven Gilberg¹⁴, Christian Pagnoux¹⁵, Martin Ten Hove²

Affiliations

¹ Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto.
² Department of Ophthalmology, Queen's University, Kingston, ON.
³ Department of Ophthalmology, University of Laval, Quebec, QC.
⁴ Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada.
⁵ Mayo Clinic, Department of Ophthalmology and Neurology.
⁶ Mayo Clinic, Department of Ophthalmology, Rochester, MN.
⁷ Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA.
⁸ Harvard Medical School, Boston, MA, USA.
⁹ Department of Clinical Neurological Sciences and Ophthalmology, Western University, London.
¹⁰ Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada.
¹¹ Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People's Republic of China.
¹² Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
¹³ Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB.
¹⁴ Department of Ophthalmology, University of Ottawa, Ottawa, ON.
¹⁵ Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, Canada.

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.