Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation
Authors Ing EB, Miller NR, Nguyen A, Su W, Bursztyn LLCD, Poole M, Kansal V, Toren A, Albreiki D, Mouhanna JG, Muladzanov A, Bernier M, Gans M, Lee D, Wendel C, Sheldon C, Shields M, Bellan L, Lee-Wing M, Mohadjer Y, Nijhawan N, Tyndel F, Sundaram ANE, ten Hove MW, Chen JJ, Rodriguez AR, Hu A, Khalidi N, Ing R, Wong SWK, Torun N
Received 5 November 2018
Accepted for publication 17 January 2019
Published 21 February 2019 Volume 2019:13 Pages 421—430
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Cristina Weinberg
Peer reviewer comments 2
Editor who approved publication: Dr Scott Fraser
Edsel B Ing,1 Neil R Miller,2 Angeline Nguyen,2 Wanhua Su,3 Lulu LCD Bursztyn,4 Meredith Poole,5 Vinay Kansal,6 Andrew Toren,7 Dana Albreki,8 Jack G Mouhanna,9 Alla Muladzanov,10 Mikaël Bernier,11 Mark Gans,10 Dongho Lee,12 Colten Wendel,13 Claire Sheldon,13 Marc Shields,14 Lorne Bellan,15 Matthew Lee-Wing,15 Yasaman Mohadjer,16 Navdeep Nijhawan,1 Felix Tyndel,17 Arun NE Sundaram,17 Martin W ten Hove,18 John J Chen,19 Amadeo R Rodriguez,20 Angela Hu,21 Nader Khalidi,21 Royce Ing,22 Samuel WK Wong,23 Nurhan Torun24
1Ophthalmology, University of Toronto, Toronto, ON, Canada; 2Ophthalmology, Johns Hopkins University, Baltimore, MD, USA; 3Statistics, MacEwan University, Edmonton, AB, Canada; 4Ophthalmology, Western University, London, ON, Canada; 5Queens University, Kingston, ON, Canada; 6Ophthalmology, University of Saskatchewan, Saskatoon, SK, Canada; 7Laval University, Quebec, QC, Canada; 8Ophthalmology, University of Ottawa, Ottawa, ON, Canada; 9University of Ottawa, Ottawa, ON, Canada; 10Ophthalmology, McGill University, Montreal, QC, Canada; 11University of Sherbrooke, QC, Canada; 12University of British Columbia, Vancouver, BC, Canada; 13Ophthalmology, University of British Columbia, Vancouver, BC, Canada; 14Ophthalmology, University of Virginia, Fisherville, VA, USA; 15Ophthalmology, University of Manitoba, Winnipeg, MB, Canada; 16Ophthalmology, Eye Institute of West Florida, Tampa, FL, USA; 17Neurology, University of Toronto, Toronto, ON, Canada; 18Ophthalmology, Queens University, Toronto, ON, Canada; 19Ophthalmology & Neurology, Mayo Clinic, Rochester, MN, USA; 20Ophthalmology, McMaster University, Hamilton, ON, Canada; 21Rheumatology, McMaster University, Hamilton, ON, Canada; 22Undergraduate Science, Ryerson University, Toronto, ON, Canada; 23Statistics, University of Waterloo, Waterloo, ON, Canada; 24Ophthalmology, Harvard University, Boston, MA, USA
Purpose: To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review.
Methods: An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers. The outcome variable was biopsy-proven GCA. The predictor variables were age, gender, headache, clinical temporal artery abnormality, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. The data were divided into three groups to train, validate, and test the models. The NN model with the lowest false-negative rate was chosen. Internal and external validations were performed.
Results: Of 1,833 patients who underwent TABx, there was complete information on 1,201 patients, 300 (25%) of whom had a positive TABx. On multivariable LR age, platelets, jaw claudication, vision loss, log C-reactive protein, log erythrocyte sedimentation rate, headache, and clinical temporal artery abnormality were statistically significant predictors of a positive TABx (P≤0.05). The area under the receiver operating characteristic curve/Hosmer–Lemeshow P for LR was 0.867 (95% CI, 0.794, 0.917)/0.119 vs NN 0.860 (95% CI, 0.786, 0.911)/0.805, with no statistically significant difference of the area under the curves (P=0.316). The misclassification rate/false-negative rate of LR was 20.6%/47.5% vs 18.1%/30.5% for NN. Missing data analysis did not change the results.
Conclusion: Statistical models can aid in the triage of patients with suspected GCA. Misclassification remains a concern, but cutoff values for 95% and 99% sensitivities are provided (https://goo.gl/THCnuU).
Keywords: giant cell arteritis, temporal artery biopsy, neural network, logistic regression, prediction models, ophthalmology, rheumatology
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