<p>Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation</p>

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 ).

European Headache Federation recommendations for neurologists managing giant cell arteritis

“…Headache alone may be the initial symptom for those with cranial GCA, and as the undiagnosed disease progresses, it becomes a more commonly reported symptom [24]. The full headache phenotype is little further described than "new onset" in the majority of the literature, and therefore when used in isolation, it appears to be only a modest discriminator when trying to predict the likelihood of GCA [25].…”

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

“…These indexes were generally considered to be important performance scores in previous studies [ 28 , 30 – 34 ]. Furthermore, the 10-fold cross validation was performed for internal validation of the ANN model [ 24 , 35 ]. The Hosmer–Lemeshow test was used to evaluate the fitness of the logistic regression model (the cut-off value was P ≥ 0.05) [ 36 ].…”

“…MLP is one of the typical supervised learning algorithms in which a small number of parameters can be used to predict outcomes [ 21 , 22 ]. In addition, MLP can be performed by packaging software, such as SPSS (IBM, Tokyo, Japan) and JMP (SAS Institute, Inc., Cary, NC, USA) [ 4 – 6 , 23 , 24 ]. Since it does not require complex programming, the methodology established in this research is expected to be easily adaptable to other ADRs by clinicians and pharmacists.…”

Comment on: ‘A new era for giant cell arteritis’

“…“Clinician insight” is valuable in the diagnosis of GCA, but is subject to bias. TRIPOD-compliant [ 12 ], externally validated prediction models [ 13 ] more objectively estimate the pretest probability in patients with suspected GCA. Clinicians may have difficulty estimating the risk of non-linear continuous variables such as age and bloodwork, and may not appreciate that the platelet level is a stronger predictor for GCA than ESR or CRP [ 13 ].…”