This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Summary StatementVisual and software-based quantification of well aerated lung parenchyma on admission chest CT were predictors of intensive care unit (ICU) admission or death in patients with pneumonia. Key Results� Patients with COVID-19 pneumonia at baseline chest CT who had ICU admission or who died had 4 or more lobes of the lung affected compared to patients without ICU admission or death (16% versus 6% of patients, p<.04).� After adjustment for patient demographics and clinical parameters, visually assessed well aerated lung parenchyma on admission on chest CT less than 73% was associated with ICU admission or death (OR 5.4, p<.001); software methods for lung quantification showed similar results. List of abbreviations SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2; COVID-19 = coronavirus disease 19; RT-PCR = reverse-transcription polymerase chain reaction; WOG = worse outcome group; N-WOG = not-worse outcome group; %V-WAL = visual assessment of well aerated lung percentage; %S-WAL = software-based assessment of well aerated lung percentage; VOL-WAL = open-source software assessment of well aerated lung absolute volume; AT = adipose tissue. I n P r e s sAbstract Background: Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia.Purpose: The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. Materials and Methods: Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasalpharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance.Results: The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL<73% (OR, 5.4; 95% CI, 2.7-10.8; P<0.001), %S-WAL<71% (OR, 3.8; 95% CI, 1.9-7.5; P<0.001), and VOL-WAL<2.9 L (OR, 2.6; 95% CI, 1.2-5.8; P<0.01) were predictors of ICU admission/death. In comparison with clinical model containing only clinical parameters (AUC, 0.83), all three quantitative models showed higher diagnostic performance (AUC 0.86 for all models). ...
We have investigated the cerebral activation centre in four patients with episodic cluster headache (CH) with functional magnetic resonance imaging (f-MRI). The patients underwent MRI scans for anatomical and functional data acquisition in the asymptomatic state, during a headache attack and after subcutaneous administration of sumatriptan. Anatomical images were acquired by means of 3D-MPRAGE sequences and f-MRI images were obtained by means of echo-planar imaging. Data was analysed using the BrainVoyager QX version 1.7.81 software package. In all patients, the data showed significant hypothalamic activation of the hypothalamus ipsilateral to the pain side, attributable to a headache attack. Overall, we have demonstrated the anatomical location of central nervous system activation by means the first f-MRI study in CH patients. f-MRI offers a good balance of spatial and temporal resolution, and this method of study appears appropriate for investigating the pathogenetic aspects of primary headaches. Positron emission tomography and f-MRI may be regarded as little or no importance in a clinical context, they do, however, offer great potential for the exploration of headache physiopathology and the effects of pharmacological treatment.
Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Purpose Chest computed tomography (CT) is considered a reliable imaging tool for COVID-19 pneumonia diagnosis, while lung ultrasound (LUS) has emerged as a potential alternative to characterize lung involvement. The aim of the study was to compare diagnostic performance of admission chest CT and LUS for the diagnosis of COVID-19. Methods We included patients admitted to emergency department between February 21-March 6, 2020 (high prevalence group, HP) and between March 30-April 13, 2020 (moderate prevalence group, MP) undergoing LUS and chest CT within 12 h. Chest CT was considered positive in case of “indeterminate”/“typical” pattern for COVID-19 by RSNA classification system. At LUS, thickened pleural line with ≥ three B-lines at least in one zone of the 12 explored was considered positive. Sensitivity, specificity, PPV, NPV, and AUC were calculated for CT and LUS against real-time reverse transcriptase polymerase chain reaction (RT-PCR) and serology as reference standard. Results The study included 486 patients (males 61 %; median age, 70 years): 247 patients in HP (COVID-19 prevalence 94 %) and 239 patients in MP (COVID-19 prevalence 45 %). In HP and MP respectively, sensitivity, specificity, PPV, and NPV were 90–95 %, 43–69 %, 96−72 %, 20–95 % for CT and 94−93 %, 7–31 %, 94−52 %, 7–83 % for LUS. CT demonstrated better performance than LUS in diagnosis of COVID-19, both in HP (AUC 0.75 vs 0.51; P < 0.001) and MP (AUC 0.85 vs 0.62; P < 0.001). Conclusions Admission chest CT shows better performance than LUS for COVID-19 diagnosis, at varying disease prevalence. LUS is highly sensitive, but not specific for COVID-19.
Purpose To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. Methods The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. Results The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85, P = 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94, P = 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16, P < 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46, P < 0.01/ P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9, P = 0.04/ P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922; P = 0.04 for both models). Conclusions In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. Supplementary Information The online version contains supplementary material available at 10.1007/s10140-020-01867-1.
IntroductionThe relationship between sleep and headache has been known for over a century. Headache and sleeping problems are both some of the most commonly reported problems in clinical practice, and cause considerable social and family problems, as well as socio-economic impact and costs. Much data suggests an association between headache, especially migraine, on one side and sleep and its disorders on the other, but the cause and effect of this relationship is not clear. With regard to migraine, rest and sleep usually bring relief and have therefore been attempted in the treatment strategy; on the other hand however, migraine episodes are frequently triggered by several factors including sleep pattern changes; emotional stress, hypoglycaemia, sensorial stimulation (loud noise, bright light, heat or cold) and also lack of sleep or excess (weekend migraine) may, indeed, represent migraine triggers. Premonitory symptoms like mood states such as alert, tense, depressed or tired and changes in sleep quality have been described to occur up to 2 days before a migraine attack and were hypothesised to be related to a hypothalamic involvement in the prodromic phase of migraine [1]. Recently it has also been documented that overuse of migraine symptomatic drugs may worse sleep pattern in migraineurs and the withdrawal of the misused medication can alleviate the associated sleep disturbance [2].Several findings also suggest a role of chronobiological factors in migraine, probably related -as previously J Headache Pain (2005) 6:258-260 DOI 10.1007/s10194-005-0201-2 Sleep quality, chronotypes and preferential timing of attacks in migraine without aura Abstract Clinical observations show that migraine attacks have a seasonal, menstrual and circadian timing, suggesting a role of chronobiological mechanisms and their alterations in the disease, but little experimental data exists about this issue. The aim of this study was to estimate sleep quality chronotypes, and the possible circadian timing of attacks in migraneurs. One hundred patients suffering from migraine without aura according to the IHS criteria (2004), and 30 controls were enrolled. Morning and evening type subjects were more represented in migraine patients than in controls and showed a tendency towards worse sleep quality and higher disability. Forty-two percent of migraineurs presented more than 75% of their attacks at night. Morning and evening types rather than intermediate and differences between real and preferred times may represent stressors that can worsen the disease. A preferential timing for occurrence of migraine attacks during the night and early morning hours was documented. P S Y C H O B I O L O G I C A L A S P E C T S O F H E
Neuropathy is a common complication of diabetes mellitus (DM) with a wide clinical spectrum that encompasses generalized to focal and multifocal forms. Entrapment neuropathies (EN), which are focal forms, are so frequent at any stage of the diabetic disease, that they may be considered a neurophysiological hallmark of peripheral nerve involvement in DM. Indeed, EN may be the earliest neurophysiological abnormalities in DM, particularly in the upper limbs, even in the absence of a generalized polyneuropathy, or it may be superimposed on a generalized diabetic neuropathy. This remarkable frequency of EN in diabetes is underlain by a peculiar pathophysiological background. Due to the metabolic alterations consequent to abnormal glucose metabolism, the peripheral nerves show both functional impairment and structural changes, even in the preclinical stage, making them more prone to entrapment in anatomically constrained channels. This review discusses the most common and relevant EN encountered in diabetic patient in their epidemiological, pathophysiological and diagnostic features.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.