P.0433 Diagnostic value of anti-NMDA receptor antibodies in first-episode psychosis: a mini review

2021 ◽  
Vol 53 ◽  
pp. S315-S316
Author(s):  
T. Rollas ◽  
M.İ. Yıldız ◽  
E. Özçelik Eroğlu
2015 ◽  
Vol 30 ◽  
pp. 1568 ◽  
Author(s):  
E. Kelleher ◽  
P. McNamara ◽  
B. Fitzmaurice ◽  
R. Walsh ◽  
Y. Langan ◽  
...  

2014 ◽  
Vol 153 ◽  
pp. S35 ◽  
Author(s):  
Belinda Lennox ◽  
Michael S. Zandi ◽  
Julia B. Deakin ◽  
Alasdair Coles ◽  
Linda Scoriels ◽  
...  

2020 ◽  
Vol 28 (2) ◽  
pp. 199-201
Author(s):  
Joel Singer ◽  
Perminder Sachdev ◽  
Adith Mohan

Objective: The current guidelines recommend screening all patients with first episode psychosis (FEP) for anti-NMDA receptor encephalitis. This paper explores the pitfalls of this strategy. Conclusion: Screening for anti-NMDA receptor encephalitis in patients with FEP when the pre-test probability based on the clinical presentation is low creates a risk of false positive results. Testing based on clinical suspicion would be preferable.


2019 ◽  
Vol 29 ◽  
pp. S68
Author(s):  
C. Loureiro ◽  
H.A. Fachim ◽  
F. Corsi-Zuelli ◽  
P.R. Menezes ◽  
C.F. Dalton ◽  
...  

2019 ◽  
Vol 46 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Sandra Vieira ◽  
Qi-yong Gong ◽  
Walter H L Pinaya ◽  
Cristina Scarpazza ◽  
Stefania Tognin ◽  
...  

Abstract Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability of the findings is unclear due to potential methodological issues that may have inflated the existing literature. This study aimed to elucidate the extent to which the application of ML to neuroanatomical data allows detection of first episode psychosis (FEP), while putting in place methodological precautions to avoid overoptimistic results. We tested both traditional ML and an emerging approach known as deep learning (DL) using 3 feature sets of interest: (1) surface-based regional volumes and cortical thickness, (2) voxel-based gray matter volume (GMV) and (3) voxel-based cortical thickness (VBCT). To assess the reliability of the findings, we repeated all analyses in 5 independent datasets, totaling 956 participants (514 FEP and 444 within-site matched controls). The performance was assessed via nested cross-validation (CV) and cross-site CV. Accuracies ranged from 50% to 70% for surfaced-based features; from 50% to 63% for GMV; and from 51% to 68% for VBCT. The best accuracies (70%) were achieved when DL was applied to surface-based features; however, these models generalized poorly to other sites. Findings from this study suggest that, when methodological precautions are adopted to avoid overoptimistic results, detection of individuals in the early stages of psychosis is more challenging than originally thought. In light of this, we argue that the current evidence for the diagnostic value of ML and structural neuroimaging should be reconsidered toward a more cautious interpretation.


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