Cerebral iodized lipid embolization via a pulmonary arteriovenous shunt: rare complication of transcatheter arterial embolization for hepatocellular carcinoma

We report the first European case of cerebral iodized lipid embolism post transcatheter arterial embolization for hepatocellular carcinoma. Lipiodol emboli and corresponding multifocal brain ischemia were documented with computed tomography (CT) and magnetic resonance (MR) in the acutely symptomatic patient. Transcranial Doppler sonography with contrast indicated a right-to-left shunt, while on a follow-u

Artroszkópos rotátorköpeny-rekonstrukció prospektív vizsgálata

Bevezetés:A rotátorköpeny rekonstrukciója után kialakuló posztoperatív funkciót rendkívül sok tényező befolyásolja, közülük kiemelten fontos az esetlegesen kialakuló reruptura. Célkitűzés: A szerzők célul tűzték ki az artroszkópos rotátorköpeny-rekonstrukción átesett betegek műtét előtti és utáni állapotának összehasonlítását. Módszer: 2008 és 2012 júliusa között operált 22 beteg 23 vállát vizsgálták prospektíven. Minden esetben fizikális vizsgálat, röntgen és ultrahang történt. Az életminőséget és a funkcionális eredményeket Constant Score és Vizuális Analóg Skála segítségével határozták meg. Eredmények: A rekonstrukció a használt pontrendszerek alapján a betegek több mint 80%-ánál kiváló vagy jó eredményt hozott, a Constant Score 45-ről 79-re emelkedett, a Vizuális Analóg Skálán mért fájdalom szintje 6,6-ről 2,5-re csökkent. Teljes vastagságú szakadást nem, részleges szakadást 7 esetben (30%) észleltek. Az operált oldalon az acromiohumeralis távolság átlagosan 8,5 mm volt az ép oldalon mért 9,5 mm-rel szemben. Következtetések: Az artroszkópos rotátorköpeny-rekonstrukcióval a nyílt műtéthez hasonló jó eredmények érhetőek el. Orv. Hetil., 2014, 155(16), 620–626

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12–7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02–4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32–2.33, p p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12–7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02–4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32–2.33, p < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 95% CI = 1.02–1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification