Sensitivity and specificity for screening of chronic cerebrospinal venous insufficiency using a multimodal non-invasive imaging approach in patients with multiple sclerosis

The aim of this study was to investigate whether a combination of Doppler sonography (DS) and magnetic resonance venography (MRV) on 3T MRI increases specificity for detection of chronic cerebrospinal venous insufficiency (CCSVI) in 171 (113 relapsing-remitting, 47 secondary-progressive, 11 primary progressive) patients with multiple sclerosis (MS) and 79 age- and sexmatched healthy controls (HCs). One hundred ten (64.3%) MS patients and 30 (38%) HCs presented ≥2 venous hemodynamic CCSVI criteria (p<.0001). Both DS and MRV showed relatively high specificity but lower sensitivity for determining a CCSVI diagnosis in patients with MS vs HCs and between MS subgroups. In MS patients this diagnostic specificity increased to over 90% by combining internal jugular vein and vertebral vein abnormal DS and MRV findings, reflux in deep cerebral veins and MRV findings of >1 collateral veins. This study suggests that a multimodal non-invasive approach (DS and MRV) increases the specificity for a diagnosis of CCSVI in patients with MS

Risk Factors for Chronic Cerebrospinal Venous Insufficiency (CCSVI) in a Large Cohort of Volunteers

BACKGROUND: The role of intra- and extra-cranial venous system impairment in the pathogenesis of various vascular, inflammatory and neurodegenerative neurological disorders, as well as in aging, has not been studied in detail. Nor have risk factors been determined for increased susceptibility of venous pathology in the intra-cranial and extra-cranial veins. The aim of this study was to investigate the association between presence of a newly proposed vascular condition called chronic cerebrospinal venous insufficiency (CCSVI) and environmental factors in a large volunteer control group without known central nervous system pathology. METHODS AND FINDINGS: The data were collected in a prospective study from 252 subjects who were screened for medical history as part of the entry criteria and participated in the case-control study of CCSVI prevalence in multiple sclerosis (MS) patients, and then were analyzed post-hoc. All participants underwent physical and Doppler sonography examinations, and were assessed with a structured environmental questionnaire. Fullfilment of ≥ 2 positive venous hemodynamic (VH) criteria on Doppler sonography was considered indicative of CCSVI diagnosis. Risk and protective factors associated with CCSVI were analyzed using logistic regression analysis. Seventy (27.8%) subjects presented with CCSVI diagnosis and 153 (60.7%) presented with one or more VH criteria. The presence of heart disease (p = .001), especially heart murmurs (p = .007), a history of infectious mononucleosis (p = .002), and irritable bowel syndrome (p = .005) were associated with more frequent CCSVI diagnosis. Current or previous smoking (p = .029) showed a trend for association with more frequent CCSVI diagnosis, while use of dietary supplements (p = .018) showed a trend for association with less frequent CCSVI diagnosis. CONCLUSIONS: Risk factors for CCSVI differ from established risk factors for peripheral venous diseases. Vascular, infectious and inflammatory factors were associated with higher CCSVI frequency

Evaluation of cerebrovascular reactivity in chronic hepatitis C patients using transcranial color Doppler.

Hepatitis C viral (HCV) infection is associated with systemic inflammation and metabolic complications that might predispose patients to atherosclerosis, including cerebrovascular atherosclerosis. The aim of this study was to assess cerebrovascular reactivity in patients with chronic hepatitis C. Seventeen patients with chronic hepatitis C infection, as well as 11 healthy blood donors in the control group, were assessed for cerebrovascular reactivity according to the well-established breath-holding test that uses the transcranial color Doppler for measurement of blood flow velocity. Results obtained during the breath-holding revealed significantly lower average peak systolic (AvPS start, P = 0.018), end-diastolic (AvED start, P = 0.031) and mean velocity values at the very beginning of the breath-holding procedure (AvmeanV start, P = 0.02), as well as a lower mean peak systolic velocity at the end of the breath-holding test (AvPS max, P = 0.02) in the hepatitis C group. Vascular reactivity values, calculated as the breath-holding index, were also significantly lower (P = 0.045) in the hepatitis C group. In conclusion, the results of this study suggest an association between chronic HCV infection and altered cerebrovascular reactivity which may ultimately have an unfavorable effect on cerebrovascular hemodynamics and lead to increased risk of cerebrovascular diseases

Can Lung Imaging Scores and Clinical Variables Predict Severe Course and Fatal Outcome in COVID-19 Pneumonia Patients? A Single-Center Observational Study

COVID-19 prediction models mostly consist of combined clinical features, laboratory parameters, and, less often, chest X-ray (CXR) findings. Our main goal was to propose a prediction model involving imaging methods, specifically ultrasound. This was a single-center, retrospective cohort observational study of patients admitted to the University Hospital Split from November 2020 to May 2021. Imaging protocols were based on the assessment of 14 lung zones for both lung ultrasound (LUS) and computed tomography (CT), correlated to a CXR score assessing 6 lung zones. Prediction models for the necessity of mechanical ventilation (MV) or a lethal outcome were developed by combining imaging, biometric, and biochemical parameters. A total of 255 patients with COVID-19 pneumonia were included in the study. Four independent predictors were added to the regression model for the necessity of MV: LUS score, day of the illness, leukocyte count, and cardiovascular disease (&chi;2 = 29.16, p &lt; 0.001). The model accurately classified 89.9% of cases. For the lethal outcome, only two independent predictors contributed to the regression model: LUS score and patient&rsquo;s age (&chi;2 = 48.56, p &lt; 0.001, 93.2% correctly classified). The predictive model identified four key parameters at patient admission which could predict an adverse outcome