scholarly journals The Use of the Central Vein Sign in the Diagnosis of Multiple Sclerosis: A Systematic Review and Meta-analysis

Diagnostics ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1025
Author(s):  
Marco Castellaro ◽  
Agnese Tamanti ◽  
Anna Isabella Pisani ◽  
Francesca Benedetta Pizzini ◽  
Francesco Crescenzo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Regression Analysis ◽  
White Matter ◽  
Diagnostic Performance ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Central Vein ◽  
Meta Regression ◽  
Meta Regression Analysis

Background: The central vein sign (CVS) is a radiological feature proposed as a multiple sclerosis (MS) imaging biomarker able to accurately differentiate MS from other white matter diseases of the central nervous system. In this work, we evaluated the pooled proportion of the CVS in brain MS lesions and to estimate the diagnostic performance of CVS to perform a diagnosis of MS and propose an optimal cut-off value. Methods: A systematic search was performed on publicly available databases (PUBMED/MEDLINE and Web of Science) up to 24 August 2020. Analysis of the proportion of white matter MS lesions with a central vein was performed using bivariate random-effect models. A meta-regression analysis was performed and the impact of using particular sequences (such as 3D echo-planar imaging) and post-processing techniques (such as FLAIR*) was investigated. Pooled sensibility and specificity were estimated using bivariate models and meta-regression was performed to address heterogeneity. Inclusion and publication bias were assessed using asymmetry tests and a funnel plot. A hierarchical summary receiver operating curve (HSROC) was used to estimate the summary accuracy in diagnostic performance. The Youden index was employed to estimate the optimal cut-off value using individual patient data. Results: The pooled proportion of lesions showing a CVS in the MS population was 73%. The use of the CVS showed a remarkable diagnostic performance in MS cases, providing a pooled specificity of 92% and a sensitivity of 95%. The optimal cut-off value obtained from the individual patient data pooled together was 40% with excellent accuracy calculated by the area under the ROC (0.946). The 3D-EPI sequences showed both a higher pooled proportion compared to other sequences and explained heterogeneity in the meta-regression analysis of diagnostic performances. The 1.5 Tesla (T) scanners showed a lower (58%) proportion of MS lesions with a CVS compared to both 3T (74%) and 7T (82%). Conclusions: The meta-analysis we have performed shows that the use of the CVS in differentiating MS from other mimicking diseases is encouraged; moreover, the use of dedicated sequences such as 3D-EPI and the high MRI field is beneficial.

scholarly journals The “Central Vein Sign” on T2*-weighted Images as a Diagnostic Tool in Multiple Sclerosis: A Systematic Review and Meta-analysis using Individual Patient Data

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chong Hyun Suh ◽  
Sang Joon Kim ◽  
Seung Chai Jung ◽  
Choong Gon Choi ◽  
Ho Sung Kim
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Random Effects ◽  
Diagnostic Performance ◽  
Individual Patient Data ◽  
White Matter Lesions ◽  
Patient Data ◽  
Central Vein ◽  
Random Effects Model ◽  
T2 Weighted Images

AbstractWe aimed to evaluate the pooled incidence of central vein sign on T2*-weighted images from patients with multiple sclerosis (MS), and to determine the diagnostic performance of this central vein sign for differentiating MS from other white matter lesions and provide an optimal cut-off value. A computerized systematic search of the literature in PUBMED and EMBASE was conducted up to December 14, 2018. Original articles investigating central vein sign on T2*-weighted images of patients with MS were selected. The pooled incidence was obtained using random-effects model. The pooled sensitivity and specificity were obtained using a bivariate random-effects model. An optimal cut-off value for the proportion of lesions with a central vein sign was calculated from those studies providing individual patient data. Twenty-one eligible articles covering 501 patients with MS were included. The pooled incidence of central vein sign at the level of individual lesion in patients with MS was 74% (95% CI, 65–82%). The pooled sensitivity and pooled specificity for the diagnostic performance of the central vein sign were 98% (95% CI, 92–100%) and 97% (95% CI, 91–99%), respectively. The area under the HSROC curve was 1.00 (95% CI, 0.99–1.00). The optimal cut-off value for the proportion of lesions with a central vein sign was found to be 45%. Although various T2*-weighted images have been used across studies, the current evidence supports the use of the central vein sign on T2*-weighted images to differentiate MS from other white matter lesions.

scholarly journals Microstructural Abnormalities of White Matter Across Tourette Syndrome: A Voxel-Based Meta-Analysis of Fractional Anisotropy

2021 ◽  
Vol 12 ◽  
Author(s):  
Chengmin Yang ◽  
Li Yao ◽  
Naici Liu ◽  
Wenjing Zhang ◽  
Bo Tao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
White Matter ◽  
Tourette Syndrome ◽  
Fractional Anisotropy ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Neural Basis ◽  
Tic Severity ◽  
Meta Regression ◽  
Meta Regression Analysis

Introduction: Tourette syndrome (TS) is a neuropsychiatric disorder with multiple motor and vocal tics whose neural basis remains unclear. Diffusion tensor imaging (DTI) studies have demonstrated white matter microstructural alternations in TS, but the findings are inconclusive. In this study, we aimed to elucidate the most consistent white matter deficits in patients with TS.Method: By systematically searching online databases up to December 2020 for all DTI studies comparing fractional anisotropy (FA) between patients with TS and healthy controls (HCs), we conducted anisotropic effect size-signed differential mapping (AES-SDM) meta-analysis to investigate FA differences in TS, as well as performed meta-regression analysis to explore the effects of demographics and clinical characteristics on white matter abnormalities among TS.Results: A total of eight datasets including 168 patients with TS and 163 HCs were identified. We found that TS patients showed robustly decreased FA in the corpus callosum (CC) and right inferior longitudinal fasciculus (ILF) compared with HCs. These two regions preserved significance in the sensitivity analysis. No regions of increased FA were reported. Meta-regression analysis revealed that age, sex, tic severity, or illness duration of patients with TS were not linearly correlated with decreased FA.Conclusion: Patients with TS display deficits of white matter microstructure in the CC and right ILF known to be important for interhemispheric connections as well as long association fiber bundles within one hemisphere. Because the results reported in the primary literature were highly variable, future investigations with large samples would be required to support the identified white matter changes in TS.

2185-PUB: Once-Weekly Semaglutide 1mg vs. Empagliflozin 25mg as Add-On to Metformin Monotherapy in Patients with Type 2 Diabetes: A Meta-regression Analysis of Individual Patient Data

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2185-PUB
Author(s):  
ILDIKO LINGVAY ◽  
ANDREI-MIRCEA CATARIG ◽  
ANNA SANDBERG ◽  
JACK LAWSON ◽  
MATTHEW CAPEHORN ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Regression Analysis ◽  
Individual Patient Data ◽  
Patient Data ◽  
Metformin Monotherapy ◽  
Meta Regression ◽  
Meta Regression Analysis

Rate of Death and Complications in Laparoscopic and Open Roux-en-Y Gastric Bypass. A Meta-analysis and Meta-regression Analysis on 69,494 Patients

2016 ◽  
Vol 26 (8) ◽  
pp. 1956-1963 ◽  
Author(s):  
Emanuele Rausa ◽  
Luigi Bonavina ◽  
Emanuele Asti ◽  
Maddalena Gaeta ◽  
Cristian Ricci
Keyword(s):  
Gastric Bypass ◽  
Regression Analysis ◽  
Meta Analysis ◽  
Meta Regression ◽  
Meta Regression Analysis

scholarly journals When does the use of individual patient data in network meta-analysis make a difference? A simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Steve Kanters ◽  
Mohammad Ehsanul Karim ◽  
Kristian Thorlund ◽  
Aslam Anis ◽  
Nick Bansback
Keyword(s):  
Simulation Study ◽  
Treatment Effect ◽  
Mean Squared Error ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Model Performance ◽  
Patient Data ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
The Impact

Abstract Background The use of individual patient data (IPD) in network meta-analyses (NMA) is rapidly growing. This study aimed to determine, through simulations, the impact of select factors on the validity and precision of NMA estimates when combining IPD and aggregate data (AgD) relative to using AgD only. Methods Three analysis strategies were compared via simulations: 1) AgD NMA without adjustments (AgD-NMA); 2) AgD NMA with meta-regression (AgD-NMA-MR); and 3) IPD-AgD NMA with meta-regression (IPD-NMA). We compared 108 parameter permutations: number of network nodes (3, 5 or 10); proportion of treatment comparisons informed by IPD (low, medium or high); equal size trials (2-armed with 200 patients per arm) or larger IPD trials (500 patients per arm); sparse or well-populated networks; and type of effect-modification (none, constant across treatment comparisons, or exchangeable). Data were generated over 200 simulations for each combination of parameters, each using linear regression with Normal distributions. To assess model performance and estimate validity, the mean squared error (MSE) and bias of treatment-effect and covariate estimates were collected. Standard errors (SE) and percentiles were used to compare estimate precision. Results Overall, IPD-NMA performed best in terms of validity and precision. The median MSE was lower in the IPD-NMA in 88 of 108 scenarios (similar results otherwise). On average, the IPD-NMA median MSE was 0.54 times the median using AgD-NMA-MR. Similarly, the SEs of the IPD-NMA treatment-effect estimates were 1/5 the size of AgD-NMA-MR SEs. The magnitude of superior validity and precision of using IPD-NMA varied across scenarios and was associated with the amount of IPD. Using IPD in small or sparse networks consistently led to improved validity and precision; however, in large/dense networks IPD tended to have negligible impact if too few IPD were included. Similar results also apply to the meta-regression coefficient estimates. Conclusions Our simulation study suggests that the use of IPD in NMA will considerably improve the validity and precision of estimates of treatment effect and regression coefficients in the most NMA IPD data-scenarios. However, IPD may not add meaningful validity and precision to NMAs of large and dense treatment networks when negligible IPD are used.

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