Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation
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...
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Dove Medical Press
2019
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giant cell arteritis temporal artery biopsy neural network logistic regression prediction models Ophthalmology RE1-994 |
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giant cell arteritis temporal artery biopsy neural network logistic regression prediction models Ophthalmology RE1-994 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 Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
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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 |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Ing EB |
| title |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| title_short |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| title_full |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| title_fullStr |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| title_full_unstemmed |
Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| title_sort |
neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation |
| publisher |
Dove Medical Press |
| publishDate |
2019 |
| url |
https://doaj.org/article/d9856a79330c4ec5be247b628474ab1f |
| work_keys_str_mv |
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oai:doaj.org-article:d9856a79330c4ec5be247b628474ab1f2021-12-02T07:23:47ZNeural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation1177-5483https://doaj.org/article/d9856a79330c4ec5be247b628474ab1f2019-02-01T00:00:00Zhttps://www.dovepress.com/neural-network-and-logistic-regression-diagnostic-prediction-models-fo-peer-reviewed-article-OPTHhttps://doaj.org/toc/1177-5483Edsel 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 Ing EBMiller NRNguyen ASu WBursztyn LLCDPoole MKansal VToren AAlbreiki DMouhanna JGMuladzanov ABernier MGans MLee DWendel CSheldon CShields MBellan LLee-Wing MMohadjer YNijhawan NTyndel FSundaram ANEten Hove MWChen JJRodriguez ARHu AKhalidi NIng RWong SWKTorun NDove Medical Pressarticlegiant cell arteritistemporal artery biopsyneural networklogistic regressionprediction modelsOphthalmologyRE1-994ENClinical Ophthalmology, Vol Volume 13, Pp 421-430 (2019) |