Citations

Abstract Study Objectives Sleep is an important dimension in the care of chronic obstructive pulmonary disease (COPD), but its relevance to exacerbations is unclear. We wanted to assess whether sleep quality as measured by the Pittsburgh Sleep Quality Index (PSQI) is associated with an increased risk of COPD exacerbations and does this differ by socio-environmental exposures. Methods We included 1647 current and former smokers with spirometrically confirmed COPD from the SPIROMICS cohort. We assessed incidence rate ratios for exacerbation using zero-inflated negative binomial regression adjusting for demographics, medical comorbidities, and multiple metrics of disease severity, including respiratory medications, airflow obstruction, and symptom burden. Our final model adjusted for socio-environmental exposures using the Area Deprivation Index, a composite measure of contemporary neighborhood quality, and Adversity–Opportunity Index, a composite measure of individual-level historic and current socioeconomic indicators. We used a pre-determined threshold of 20% missingness to undertake multiple imputation by chained equations. As sensitivity analyses, we repeated models in those with complete data and after controlling for prior exacerbations. As an exploratory analysis, we considered an interaction between socio-environmental condition and sleep quality. Results After adjustment for all co-variates, increasing PSQI scores (range 0–21) were associated with a 5% increased risk for exacerbation per point (p = .001) in the imputed dataset. Sensitivity analyses using complete cases and after controlling for prior exacerbation history were similar. Exploratory analysis suggested less effect among those who lived in poor-quality neighborhoods (p-for-interaction = .035). Conclusions Poor sleep quality may contribute to future exacerbations among patients with COPD. This represents one target for improving disease control. Clinical Trial Registration Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS). ClinicalTrials.gov Identifier# NCT01969344. Registry URL: https://clinicaltrials.gov/ct2/show/.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

AbstractThe extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of ‘sporadic’ late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease.We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer’s Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset.Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants.These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Abstract Gallbladder polyps (GPs) are a common incidental finding on ultrasound; however, important differences in recommended management exist among professional society guidelines. An electronic survey was sent to 189 fellows of the Society of Radiologists in Ultrasound. Main outcomes included preferences and current practice patterns for evaluation, management, and surveillance of GPs as well as personal lifetime experience with gallbladder sonography and GPs. A total of 64 subjects (34%) with experience in gallbladder sonography completed the study. The estimated combined total number of gallbladder scans seen by the responders was 3,071,880. None of fellows had ever seen a pedunculated GP <1 cm detected on ultrasound that was proven to be malignant at the time of detection or during subsequent follow-up. All of the fellows used size as a feature to stratify recommendations. The median size threshold currently used by Society of Radiologists in Ultrasound fellows for recommending ultrasound follow-up was 6 mm, and their preferred threshold was 7 mm. The median size threshold for recommending surgical consultation was 10 mm, and the preferred threshold was 10 mm. Wall thickening and shape were considered important factors by 76% and 67% of respondents, respectively. Society of Radiologists in Ultrasound fellows tend to provide recommendations most similar to the American College of Radiology and Canadian Association of Radiology guidelines for management of GPs. Many would prefer guidelines that result in fewer recommendations for follow-up and surgical consultation. Despite a substantial combined experience, this survey did not uncover any case of a small GP that was malignant.

AbstractBackgroundThough pathogenic variations in PSEN1 leading to Autosomal Dominant Alzheimer's disease (ADAD) are all highly penetrant, there is substantial inter‐individual variability in the rates of cognitive decline and biomarker change observed in ADAD. We hypothesized that this inter‐individual variability may be associated with the location of the pathogenic variant within PSEN1. We tested this hypothesis using biomarker and cognitive data from the Dominantly Inherited Alzheimer Network (DIAN) observational study.MethodPSEN1 pathogenic variant carriers [grouped based on whether their ADAD genotype affected transmembrane (PSEN1 TM; N = 125) or cytoplasmic (PSEN1 CY; N = 62) domains] and non‐carriers [NC; N = 176] enrolled in DIAN underwent clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF). Linear mixed effects models with a restricted cubic spline were used to determine cross‐sectional differences in clinical and biomarker measures between NC, PSEN1 TM, and PSEN1 CY groups, across the disease course (estimated years to symptom onset; EYO).ResultBackground characteristics are reported in Table 1. PSEN1 CY and PSEN1 TM groups had elevated β‐amyloid (PiB‐PET) and phosphorylated‐tau levels (CSF pTauT181) compared to NC, but these biomarker measures were not different between PSEN1 TM and CY groups (Figure 1a‐b). Additionally, the PSEN1 TM group had decreased glucose metabolism compared to NC but was similar to the PSEN1 CY group (Figure 1c). However, we observed that PSEN1 TM carriers had significantly smaller hippocampal volume, greater lateral ventricle volume, and greater cognitive impairment (Figure 1d‐f) across EYO as compared to PSEN1 CY carriers.ConclusionDespite similar β‐amyloid PET and CSF pTauT181 levels across EYO, individuals with pathogenic variants affecting transmembrane domains in PSEN1 may have more rapid progression of disease as measured by cognitive function and neurodegeneration as compared to those with variants affecting cytoplasmic domains. These results have implications both for understanding the heterogeneity in ongoing ADAD clinical trials and for better understanding the pathobiology of ADAD.