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

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dc.contributor.authorIng, Edsel B.
dc.contributor.authorMiller, Neil R.
dc.contributor.authorNguyen, Angeline
dc.contributor.authorSu, Wanhua
dc.contributor.authorBursztyn, Lulu L.
dc.contributor.authorPoole, Meredith
dc.date.accessioned2020-12-01
dc.date.accessioned2022-05-31T01:16:37Z
dc.date.available2022-05-31T01:16:37Z
dc.date.issued2019
dc.description.abstractPurpose: 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.
dc.format.extent1.48MB
dc.format.mimetypePDF
dc.identifier.citationIng, E. B., Miller, N. R., Nguyen, A., Su, W., Bursztyn, L. L., Poole, M., ... & Muladzanov, A. (2019). Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation. Clinical Ophthalmology (Auckland, NZ), 13, 421.
dc.identifier.urihttps://hdl.handle.net/20.500.14078/2079
dc.languageEnglish
dc.language.isoen
dc.rightsAttribution-NonCommercial (CC BY-NC)
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectgiant cell arteritis
dc.subjecttemporal artery biopsy
dc.subjectneural network
dc.subjectlogistic regression
dc.subjectprediction models
dc.subjectophthalmology
dc.subjectrheumatology
dc.titleNeural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validationen
dc.typeArticle
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