Diagnostic performance of 14-3-3η and anti-carbamylated protein antibodies in Rheumatoid Arthritis in Han population of Northern China

Zhang, Yuan; Liang, Yongming; Lin, Feng; Cui, Liyan

doi:10.1016/j.cca.2019.12.011

Cited by 12 publications

(10 citation statements)

References 33 publications

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“…In this study, we introduced an ANN into the diagnosis of RA, enabling the integration of all features to increase the accuracy of diagnosis and decrease the waste of indicator information caused by threshold division. This ANN algorithm achieved a better prediction accuracy (90.6%) than that of the threshold algorithm (88.8%) 2 . Among these features, anti-CCP had the greatest influence while age and anti-CarP also had a weaker but evident influence on RA diagnosis, allowing us to appreciate an age factor in RA diagnosis that was not previously recognized.…”

Section: Discussionmentioning

confidence: 84%

“…In a previous study, we showed that in the Han population of Northern China, anti-CarP and 14-3-3η protein are valuable indicators of RA, and when combined with RF and anti-CCP, the detection accuracy is maximized 2 . However, in the process of diagnosis, in addition to the above two indicators, other factors such as age and gender are ignored.…”

Section: Introductionmentioning

confidence: 96%

“…This work has laid the foundation for applying deep learning to the diagnosis of RA 3 . Considering that our previous study did not include certain universal characteristics (such as age and gender) 2 , here we incorporated a deep learning ANN into our RA diagnosis and evaluated the effect of different clinical features on the outcome by re-training the network.…”

Section: Introductionmentioning

confidence: 99%

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Improved diagnosis of rheumatoid arthritis using an artificial neural network

Bai

Zhang

Wang

et al. 2022

Sci Rep

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Rheumatoid arthritis (RA) is chronic systemic disease that can cause joint damage, disability and destructive polyarthritis. Current diagnosis of RA is based on a combination of clinical and laboratory features. However, RA diagnosis can be difficult at its disease onset on account of overlapping symptoms with other arthritis, so early recognition and diagnosis of RA permit the better management of patients. In order to improve the medical diagnosis of RA and evaluate the effects of different clinical features on RA diagnosis, we applied an artificial neural network (ANN) as the training algorithm, and used fivefold cross-validation to evaluate its performance. From each sample, we obtained data on 6 features: age, sex, rheumatoid factor, anti-citrullinated peptide antibody (CCP), 14-3-3η, and anti-carbamylated protein (CarP) antibodies. After training, this ANN model assigned each sample a probability for being either an RA patient or a non-RA patient. On the validation dataset, the F1 for all samples by this ANN model was 0.916, which was higher than the 0.906 we previously reported using an optimal threshold algorithm. Therefore, this ANN algorithm not only improved the accuracy of RA diagnosis, but also revealed that anti-CCP had the greatest effect while age and anti-CarP had a weaker on RA diagnosis.

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Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Improved diagnosis of rheumatoid arthritis using an artificial neural network

Bai

Zhang

Wang

et al. 2022

Sci Rep

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“…Carrier et al 2 performed a 5‐year prospective research involving 330 patients with early undifferentiated polyarthritis to evaluate the predictive capability of 14‐3‐3η protein for RA, revealing that 14‐3‐3η combined with other RA‐associated biomarkers elevated the identification of RA patients. Zhang et al 3 detected the serum level of 14‐3‐3η protein in 291 RA patients, 223 patients with autoimmune diseases except RA, and 156 healthy subjects to explore the application value of 14‐3‐3η protein for RA diagnosis, demonstrating that the sensitivity and specificity were 0.52 and 0.94 with a cut‐off value of 0.2 ng/mL.…”

Section: Figurementioning

confidence: 99%

Diagnostic accuracy of 14‐3‐3η protein in rheumatoid arthritis: A meta‐analysis

Chen

Jiang

2020

Int J of Rheum Dis

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We have read with great interest the article by Wang et al 1 entitled "Diagnostic accuracy of 14-3-3η protein in rheumatoid arthritis: A metaanalysis", which summarized available published studies on the diagnostic accuracy of 14-3-3η protein for rheumatoid arthritis (RA). A total of 13 studies (1554 positive and 1934 negative participants) were included in the study. The results demonstrated that the pooled sensitivity and specificity of 14-3-3η protein for RA were 0.73 (95% CI 0.71-0.75) and 0.88 (95% CI 0.87-0.90), indicating that 14-3-3η protein has moderate accuracy for the diagnosis of RA. Although we acknowledge the authors for their efforts to summarize the available data on this topic, we would like to raise several concerns needing further discussion.

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“…Therefore, we excluded this study based on the inclusion criteria of our meta‐analysis. The detection of 14‐3‐3 η protein is not by enzyme‐linked immunosorbent assay (ELISA) but by light‐initiated chemiluminescent assay (LiCA) in the research conducted by Zhang et al 2 Therefore, we excluded this study based on the inclusion criteria of our meta‐analysis.…”

mentioning

confidence: 99%