Multivariable prediction model for suspected giant cell arteritis: development and validation
Received 17 September 2017
Accepted for publication 7 October 2017
Published 22 November 2017 Volume 2017:11 Pages 2031—2042
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Scott Fraser
Edsel B Ing,1 Gabriela Lahaie Luna,2 Andrew Toren,3 Royce Ing,4 John J Chen,5 Nitika Arora,6 Nurhan Torun,7 Otana A Jakpor,8 J Alexander Fraser,9 Felix J Tyndel,10 Arun NE Sundaram,10 Xinyang Liu,11 Cindy TY Lam,1 Vivek Patel,12 Ezekiel Weis,13 David Jordan,14 Steven Gilberg,14 Christian Pagnoux,15 Martin ten Hove2
1Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto, 2Department of Ophthalmology, Queen’s University, Kingston, ON, 3Department of Ophthalmology, University of Laval, Quebec, QC, 4Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada; 5Mayo Clinic, Department of Ophthalmology and Neurology, 6Mayo Clinic, Department of Ophthalmology, Rochester, MN, 7Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA, 8Harvard Medical School, Boston, MA, USA; 9Department of Clinical Neurological Sciences and Ophthalmology, Western University, London, 10Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada; 11Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People’s Republic of China; 12Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 13Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB, 14Department of Ophthalmology, University of Ottawa, Ottawa, ON, 15Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, Canada
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 pH–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: temporal artery biopsy, diagnosis, prediction rule, nomogram, giant cell arteritis, validation
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