(1) Background: The three factors within the Virchow triad play the leading role in the development of deep vein thrombosis (DVT) during pregnancy. (2) Methods: This research approaches the various risk factors associated with DVT and its most representative complications, pulmonary thromboembolism and cerebral venous thrombosis, in pregnant and postpartum women across a 15-year period (2007–2021). (3) Results: A total of 201 out of 287 patients with DVT had associated risk factors, while 86 did not present with any. Out of the 201 patients with risk factors, 47 developed pulmonary thromboembolism, while 12 experienced cerebral thrombosis. The statistical analysis of risk factors involved in DVT revealed high significance for obesity (OR 3.676; CI 2.484–5.439), gestational diabetes (OR 3.394; CI 2.101–5.483), hypertension (OR 2.325; CI 1.591–3.397), preeclampsia (OR 4.753; CI 2.342–9.645), thrombophilia (OR 12.138; CI 8.973–16.417), and varicose veins (OR 9.678; CI 7.321–12.793); for pulmonary thromboembolism, there was high significance for obesity (OR 7.867; CI 4.297–14.401), hypertension (OR 2.605; CI 1.246–5.446), preeclampsia (OR 7.483; CI 2.346–23.872), thrombophilia (OR 11.035; CI 5.910–20.602), and varicose veins (OR 6.837; CI 3.665–12.757); and for cerebral thromboembolism (CTE), the risk factors identified were obesity (OR 6.755; CI 1.954–23.347), hypertension (OR 1.167; CI 0.155–8.770), preeclampsia (OR 9.655; CI 1.283–72.672), and thrombophilia (OR 33.275; CI 12.884–85.939). (4) Conclusions: Obesity was the only significant factor found to influence DVT, pulmonary embolism and CTE risks, and hereditary thrombophilia was the main factor influencing the risk for pulmonary thromboembolism and CTE. Systemic lupus erythematosus and gestational diabetes revealed conflicting results that require further investigation.

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS.

The Background: monoclonal antibody therapies represent high-efficacy treatment options for relapsing forms of multiple sclerosis (MS). However, the absence of direct head-to-head randomized trials and the use of heterogeneous comparators across pivotal studies complicate comparative effectiveness assessments. While network meta-analysis (NMA) offers a framework to integrate evidence, the fragmented structure of the available evidence base precludes a conventional NMA with global indirect comparisons and treatment ranking. Methods: A systematic review with qualitative assessment of treatment effects of randomized controlled trials evaluating monoclonal antibody therapies in relapsing forms of multiple sclerosis was conducted. Annualized relapse rate (ARR) was analyzed as the primary outcome, and six-month confirmed disability progression (CDP) as the key secondary outcome. Network geometry and connectivity were explicitly assessed for each outcome prior to quantitative synthesis. Analyses were restricted to comparator-defined connected components of the evidence base, and indirect comparisons across disconnected components were not performed. Sensitivity analyses, including descriptive analyses in progressive multiple sclerosis, were conducted where appropriate. Results: nine randomized controlled trials involving 6762 patients were included. For ARR, the evidence network was fragmented into three disconnected components defined by placebo-, interferon beta-1a-, and teriflunomide-controlled trials. Within connected sub-networks, monoclonal antibody therapies consistently demonstrated substantial reductions in ARR relative to their respective comparators, with overlapping confidence intervals suggesting broadly comparable relapse suppression among high-efficacy agents. For CDP, network connectivity was more limited, and treatment effects were more heterogeneous. Significant reductions in disability progression were observed for some agents within comparator-specific networks, while uncertainty remained for others. Due to network disconnection, no global treatment ranking was performed. Conclusions: this study provides a transparent synthesis of randomized evidence on monoclonal antibody therapies in relapsing MS. By explicitly accounting for network connectivity and comparator heterogeneity, the analysis avoids unsupported indirect comparisons and global treatment hierarchies. The findings support robust relapse suppression across monoclonal antibody therapies within comparable trial frameworks, while highlighting heterogeneity in disability outcomes. These results illustrate the importance of contextual interpretation in comparative effectiveness research in MS.