Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation.

Abstract

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).

• Authors: Edsel B Ing, Neil R Miller, Angeline Nguyen, Wanhua Su, Lulu L C D Bursztyn, Meredith Poole, Vinay Kansal, Andrew Toren, Dana Albreki, Jack G Mouhanna, Alla Muladzanov, Mikaël Bernier, Mark Gans, Dongho Lee, Colten Wendel, Claire Sheldon, Marc Shields, Lorne Bellan, Matthew Lee-Wing, Yasaman Mohadjer, Navdeep Nijhawan, Felix Tyndel, Arun N E Sundaram, Martin W Ten Hove, John J Chen, Amadeo R Rodriguez, Angela Hu, Nader Khalidi, Royce Ing, Samuel W K Wong, Nurhan Torun
• Journal: Clinical ophthalmology (Auckland, N.Z.) (Clin Ophthalmol) Vol. 13 Pg. 421-430 ( 2019) ISSN: 1177-5467 [Print] New Zealand
• PMID: 30863010 (Publication Type: Journal Article)