31 research outputs found
Effect of mild cortisol cosecretion on body composition and metabolic parameters in patients with primary hyperaldosteronism
Objective
To investigate the effects of simultaneous cortisol cosecretion (CCS) on body composition in computed tomography (CT)-imaging and metabolic parameters in patients with primary aldosteronism (PA) with the objective of facilitating early detection.
Design
Retrospective cohort study.
Patients
Forty-seven patients with PA and CCS confirmed by 1-mg dexamethasone suppression test (DST) with a cutoff of ≥1.8 µg/dL were compared with PA patients with excluded CCS (non-CCS, n = 47) matched by age and sex.
Methods
Segmentation of the fat compartments and muscle area at the third lumbar region was performed on non-contrast-enhanced CT images with dedicated segmentation software. Additionally, liver, spleen, pancreas and muscle attenuation were compared between the two groups.
Results
Mean cortisol after DST was 1.2 µg/dL (33.1 nmol/L) in the non-CCS group and 3.2 µg/dL (88.3 nmol/L) in the CCS group with mild autonomous cortisol excess (MACE). No difference in total, visceral and subcutaneous fat volumes was observed between the CCS and non-CCS group (p = .7, .6 and .8, respectively). However, a multivariable regression analysis revealed a significant correlation between total serum cholesterol and results of serum cortisol after 1-mg DST (p = .026). Classification of the patients based on visible lesion on CT and PA-lateralization via adrenal venous sampling also did not show any significant differences in body composition.
Conclusion
MACE in PA patients does not translate into body composition changes on CT-imaging. Therefore, early detection of concurrent CCS in PA is currently only attainable through biochemical tests. Further investigation of the long-term clinical adverse effects of MACE in PA is necessary
Multi-center study of inter-rater reproducibility, image quality, and diagnostic accuracy of CZT versus conventional SPECT myocardial perfusion imaging
Cadmium-zinc-telluride (CZT)-based detectors exhibit higher diagnostic sensitivity in myocardial perfusion imaging (MPI) than conventional Anger-MPI for detection of coronary artery disease (CAD); however, reduced specificity and diagnostic accuracy of CZT-MPI were observed. This study aims to compare these different camera systems and to examine the degree of inter-rater reproducibility among readers with varying experience in MPI. 83 patients who underwent double stress/rest examinations using both a CZT and conventional SPECT cameras within one visit were included. Anonymized and randomized MPI-images were distributed to 15 international readers using a standardized questionnaire. Subsequent coronary angiography findings of ten patients served as a reference for analysis of sensitivity and specificity. Image quality was significantly better in CZT-MPI with significantly lower breast attenuation (P < 0.05). CZT-MPI exhibited higher sensitivity than Anger-MPI (87.5% vs. 62.5%) and significantly reduced specificity (40% vs. 100%). Readers experienced with both camera systems had the highest inter-rater agreement indicating higher reproducibility (CZT 0.54 vs. conv. 0.49, P < 0.05). Higher diagnostic sensitivity of CZT-MPI offers advantages in detection of CAD yet potentially of at the cost of reduced specificity, therefore it requires special training and a differentiated evaluation approach, especially for non-experienced readers with such camera systems. The online version contains supplementary material available at 10.1007/s12350-022-03054-w
Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis
Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts’ reading performance. The study cohorts include [1] a cohort, characterized by 563 CXRs acquired in the emergency unit that were evaluated by 9 readers (radiologists and non-radiologists) in terms of 4 common pathologies, [2] a collection of 6,248 SCXRs annotated by radiologists in terms of pneumothorax presence, its size and presence of inserted thoracic tube material which allowed for subgroup and confounding bias analysis and [3] a cohort consisting of 166 patients with SCXRs that were evaluated by radiologists for underlying causes of basal lung opacities, all of those cases having been correlated to a timely acquired computed tomography scan (SCXR and CT within < 90 min). CheXNet non-significantly exceeded the radiology resident (RR) consensus in the detection of suspicious lung nodules (cohort [1], AUC AI/RR: 0.851/0.839, p = 0.793) and the radiological readers in the detection of basal pneumonia (cohort [3], AUC AI/reader consensus: 0.825/0.782, p = 0.390) and basal pleural effusion (cohort [3], AUC AI/reader consensus: 0.762/0.710, p = 0.336) in SCXR, partly with AUC values higher than originally published (“Nodule”: 0.780, “Infiltration”: 0.735, “Effusion”: 0.864). The classifier “Infiltration” turned out to be very dependent on patient positioning (best in CXR, worst in SCXR). The pneumothorax SCXR cohort [2] revealed poor algorithm performance in CXRs without inserted thoracic material and in the detection of small pneumothoraces, which can be explained by a known systematic confounding error in the algorithm training process. The benefit of clinically relevant external validation is demonstrated by the differences in algorithm performance as compared to the original publication. Our multi-cohort benchmarking finally enables the consideration of confounders, different reference standards and patient positioning as well as the AI performance comparison with differentially qualified medical readers
Evaluation of grit and its associated factors among undergraduate pharmacy students from 14 Asian and Middle Eastern countries amid the COVID-19 pandemic
Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism
ObjectivesThe aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA).Methods269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features.ResultsRadiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features.ConclusionIntegration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA
Middle Eastern Cities in a Time of Climate Crisis
The climate crisis is hitting around the world, including in the Middle East and its cities. Urban regions are exposed to increasingly frequent heat waves and floods that leave decision makers without immediate answers. In the context of this global crisis, this book addresses the need for a better understanding of the current model of urban expansion. Cities are major sources of greenhouse gas (GHG) emissions but they are also celebrated for their contribution to economic growth. The current moment is one of a large paradigm shift as climate change is now recognized as a legitimate public problem. This is especially true for city dwellers, who are increasingly exposed to climate change, the loss of biodiversity and heavy pollution while natural breathing spaces continue to shrink around them. The sixteen chapters of this book do not offer any off-the-rack or technical solutions, but they analyze the urban conundrum and the contribution of cities to the climate crisis. Some chapters focus on individual car ownership, land privatization, waste management and land use changes under the guise of development. Others explore local and contextual answers to urban governance issues. With the support of CEDEJ and the Friedrich-Ebert-Stiftung, researchers, experts and civil society actors explore the ongoing transformations of Middle Eastern urban environments and mobilities and question them in relation to the climate crisis. The contributions are based on empirical knowledge gathered in the Nile Delta, the Greater Cairo Region, Riyadh and Beirut. Without concessions to mainstream thinking, this book contributes to a better understanding of urban challenges, climate threats and policy responses in contexts marked by growing environmental inequalities
Multicentered analysis of percutaneous sclerotherapies in venous malformations of the face
ObjectivesTo evaluate the safety and outcome of image-guided sclerotherapy for treating venous malformations (VMs) of the face.Materials and methodsA multicenter cohort of 68 patients with VMs primarily affecting the face was retrospectively investigated. In total, 142 image-guided sclerotherapies were performed using gelified ethanol and/or polidocanol. Clinical and imaging findings were assessed to evaluate clinical response, lesion size reduction, and complication rates. Sub-analyses of complication rates depending on type and injected volume of the sclerosant as well as of pediatric versus adult patient groups were conducted.ResultsMean number of procedures per patient was 2.1 (±1.7) and mean follow-up consisted of 8.7 months (±6.8 months). Clinical response (n = 58) revealed a partial relief of symptoms in 70.7% (41/58), 13/58 patients (22.4%) presented symptom-free while only 4/58 patients (6.9%) reported no improvement. Post-treatment imaging (n = 52) revealed an overall objective response rate of 86.5% (45/52). The total complication rate was 10.6% (15/142) including 4.2% (7/142) major complications, mostly (14/15, 93.3%) resolved by conservative means. In one case, a mild facial palsy persisted over time. The complication rate in the gelified ethanol subgroup was significantly higher compared to polidocanol and to the combination of both sclerosants (23.5 vs. 6.0 vs. 8.3%, p = 0.01). No significant differences in complications between the pediatric and the adult subgroup were observed (12.1 vs. 9.2%, p = 0.57). Clinical response did not correlate with lesion size reduction on magnetic resonance imaging (MRI).ConclusionImage-guided sclerotherapy is effective for treating VMs of the face. Clinical response is not necessarily associated with size reduction on imaging. Despite the complex anatomy of this location, the procedures are safe for both adults and children
Decline in subarachnoid haemorrhage volumes associated with the first wave of the COVID-19 pandemic
BACKGROUND: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The study\u27s objective was to examine whether subarachnoid haemorrhage (SAH) hospitalisations and ruptured aneurysm coiling interventions demonstrated similar declines.
METHODS: We conducted a cross-sectional, retrospective, observational study across 6 continents, 37 countries and 140 comprehensive stroke centres. Patients with the diagnosis of SAH, aneurysmal SAH, ruptured aneurysm coiling interventions and COVID-19 were identified by prospective aneurysm databases or by International Classification of Diseases, 10th Revision, codes. The 3-month cumulative volume, monthly volumes for SAH hospitalisations and ruptured aneurysm coiling procedures were compared for the period before (1 year and immediately before) and during the pandemic, defined as 1 March-31 May 2020. The prior 1-year control period (1 March-31 May 2019) was obtained to account for seasonal variation.
FINDINGS: There was a significant decline in SAH hospitalisations, with 2044 admissions in the 3 months immediately before and 1585 admissions during the pandemic, representing a relative decline of 22.5% (95% CI -24.3% to -20.7%, p\u3c0.0001). Embolisation of ruptured aneurysms declined with 1170-1035 procedures, respectively, representing an 11.5% (95%CI -13.5% to -9.8%, p=0.002) relative drop. Subgroup analysis was noted for aneurysmal SAH hospitalisation decline from 834 to 626 hospitalisations, a 24.9% relative decline (95% CI -28.0% to -22.1%, p\u3c0.0001). A relative increase in ruptured aneurysm coiling was noted in low coiling volume hospitals of 41.1% (95% CI 32.3% to 50.6%, p=0.008) despite a decrease in SAH admissions in this tertile.
INTERPRETATION: There was a relative decrease in the volume of SAH hospitalisations, aneurysmal SAH hospitalisations and ruptured aneurysm embolisations during the COVID-19 pandemic. These findings in SAH are consistent with a decrease in other emergencies, such as stroke and myocardial infarction
Hypervascular Nodules and Stiffer Liver are Associated with Recurrence after Microwave Ablation in Patients with Hepatocellular Carcinoma: A Double-Center Analysis
Background and Aims The aim of this study was to detect the most important risk factors for recurrence after microwave ablation (MWA) of hepatocellular carcinoma (HCC).
Methods A total of 92 patients with 110 HCC focal lesions (FLs) underwent MWA therapy. All the patients underwent triphasic CT before and after 1 and 3 months of MWA therapy. Complete ablation and recurrence rates were recorded, and the risk factors associated with recurrence were analyzed.
Results Regarding the 110 HCC FLs that were detected pre-MWA, adequate ablation was recorded post-MWA procedure in 88 FLs (80%) and incomplete ablation in 22 FLs (showed residual contrast enhancement). However, there were newly detected lesions (17 FLs). The rate of recurrence was significantly higher in patients with multiple larger (> 4 cm) sized and hypervascular nodules. Diabetics were significantly associated with a higher recurrence rate of HCC. The rate of recurrence was significantly higher in patients with baseline level of serum alfa-fetoprotein (AFP) ≥200 ng/mL. Stiffer liver> 25 kPa had higher incidence for recurrence after ablation.
Conclusion Meticulous follow-up is mandatory in diabetic patients, patients with AFP > 200 ng/dL starting value, hypervascular large hepatic FL, and in stiffer liver> 25 kPa, as these patients have higher incidence for recurrence after ablation