Diffuse large B-cell lymphoma as an extra-hepatic manifestation of hepatitis C and co-infection of helicobacter pylori: A case report.

Along with infecting hepatocytes, the Hepatitis C virus (HCV) is also a lymphotropic virus. Chronic HCV infection can mutate the Bcl2, a proto-oncogene that inhibits apoptosis. This causes continuous stimulation of B lymphocytes, which results in clonal growth of these immunoglobulin-producing cells. In Western countries, there is a well-documented link between HCV and lymphoproliferative illness. HCV and Non-Hodgkin lymphoma (NHL) have been found to be significantly correlated in Europe, Japan, and the southern United States. There, however, has been no association found in central and northern Europe, the northwestern United States, and some Asian countries. A literature deficit exists in South Asia about the incidence of HCV infection in lymphoma patients. Here, the first documented instance of Diffuse Large B-cell NHL (germinal center type) is reported in a 35-year-old patient. The patient presented to the outpatient department at Ruth KM Pfau, Civil Hospital Karachi, in July of 2022, with the chief complaints of altered bowel habits due to involvement of the anorectal junction and concomitant infection by Helicobacter pylori with a prior history of HCV infection.

Review of the Association Between Long-Term and Current Systemic Steroid Use With Electromechanical Complications and Inpatient Mortality After ST-Elevation Myocardial Infarction.

Background The impact of long-term systemic steroid use on electrical and mechanical complications following ST-segment elevation myocardial infarction (STEMI) has not been extensively studied. Methods In a retrospective cohort study of the National Inpatient Sample (NIS) from 2018 to 2020, adults admitted with STEMI were dichotomized based on the presence of long-term (current) systemic steroid (LTCSS) use. The primary outcome was all-cause mortality. Secondary outcomes included a composite of mechanical complications, electrical, hemodynamic, and thrombotic complications, as well as revascularization complexity, length of stay (LOS), and total charge. Multivariate linear and logistic regressions were used to adjust for confounders. Results Out of 608,210 admissions for STEMI, 5,310 (0.9%) had LTCSS use. There was no significant difference in the odds of all-cause mortality (aOR: 0.89, 95%CI: 0.74-1.08, p-value: 0.245) and the composite of mechanical complications (aOR: 0.74, 95%CI: 0.25-2.30, p-value: 0.599). LTCSS use was associated with lower odds of ventricular tachycardia, atrioventricular blocks, new permanent-pacemaker insertion, cardiogenic shock, the need for mechanical circulatory support, mechanical ventilation, cardioversion, a reduced LOS by 1 day, and a reduced total charge by 34,512 USD (all p-values: <0.05). There were no significant differences in the revascularization strategy (coronary artery bypass graft (CABG) vs. percutaneous coronary interventions (PCI)) or in the incidence of composite thrombotic events. Conclusion LTCSS use among patients admitted with STEMI was associated with lower odds of electrical dysfunction and hemodynamic instability but no difference in the odds of mechanical complications, CABG rate, all-cause mortality, cardiac arrest, or thrombotic complications. Further prospective studies are needed to evaluate these findings further.

Comparison of efficacy of daily and alternate day maintenance treatment of GERD with Vonoprazan 10-mg using Gastroesophageal Reflux Disease Symptom Assessment Scale.

Objectives: To compare efficacy of 10-mg of vonoprazan daily & on alternate days by Gastroesophageal Reflux Disease Symptom Assessment Scale (GSAS). Method: This prospective interventional cohort was done at Department of Medicine/Gastroenterology Dow Medical College, Karachi, Pakistan during the period August 2022 & January 2023. Potential participants fulfilling inclusion and exclusion criteria were asked to fill out GSAS questionnaires after their written consent. Patients were allocated in to two groups using random tables. Group-A was given Tab Vonoprazan 10-mg daily for two weeks. Group-B was given Tab Vonoprazan 10-mg on alternate day. GSAS was scored by totaling scores across symptoms and then they are divided by the total number of non-missing symptom scores. Both groups were assessed week-0 & week-2. Results: Only 90 proformas that were completely filled were included, Group-A had 30 males and 15 females while Group-B had 29 males and 16 females. No significant difference in score was found in GSAS score at week-0 except that in item 'gurgling' while at week two there was no significant difference between any of the items. Total GSAS score were significantly lower at Week-2 than at week-0 (p = <.001). Conclusions: Vonoprazan of 10-mg on alternate day is equally effective as 10-mg daily in maintenance of GERD patients at two weeks.

Predictive modelling framework on the basis of artificial neural network: A case of nano-powder mixed electric discharge machining.

In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.

Cardiovascular Outcomes Associated With Exposure To Radiation Therapy In Thoracic Malignancies: An Insight Study Using the National Inpatient Database.

Background Thoracic irradiation is a widely used therapeutic and palliative treatment option for thoracic neoplasms. However, short- and long-term cardiovascular adverse effects of radiation exposure remain a major concern. The short-term adverse effects are observed within months of exposure such as pericardial diseases; meanwhile, the long-term complications are usually insidious and manifest over decades, such as congestive heart failure, coronary artery disease, cardiomyopathy, conduction disorders, constrictive pericarditis, and valvular heart disease. Hence, long-term cardiovascular adverse effects are challenging to predict, and the association with radiation exposure remains difficult to establish. Methodology This retrospective, observational study was conducted using data from the National Inpatient Sample (NIS) database from 2016 to 2019. Adult patients with primary thoracic malignancies who underwent radiation therapy (RT) were defined using principal and secondary International Classification of Diseases, Tenth Revision codes. Other malignancies that can be treated with RT and all secondary malignancies were excluded from the primary comparison group. Cardiac outcomes were defined as the prevalence of congestive heart failure, coronary artery disease, cardiomyopathy, conduction disorders, pericardial diseases, and valvular heart diseases in the primary group. The multivariate logistic and the linear regression analyses were used to adjust for confounders. Results When compared to the general population, adults with thoracic malignancies exposed to RT had higher odds of developing chronic pericarditis (adjusted odds ratio (aOR) = 2, 95% confidence interval (CI) = 1.9-2.2, p < 0.001), acute pericarditis (aOR = 2.3, 95% CI = 1.9-2.9, p < 0.001), constrictive pericarditis (aOR = 2.8, 95% CI = 2.1-3.7, p < 0.001), conduction disorders (aOR = 1.3, 95% CI = 1.2-1.35, p < 0.001), coronary artery disease (aOR = 1.24, 95% CI = 1.2-1.27, p < 0.001), heart failure (aOR = 1.44, 95% CI = 1.4-1.5, p < 0.001), and valvular heart disease (aOR = 1.37, 95% CI = 1.3-1.4, p < 0.001). There was no difference in the odds of developing cardiac arrest (aOR = 1, 95% CI = 0.9-1.10, p = 0.6) or acute myocardial infarction (aOR = 1.1, 95% CI = 1-1.15, p < 0.001). When compared to adults with thoracic malignancies not exposed to RT, adults with thoracic malignancies who were exposed to RT had higher odds of developing acute myocardial infarction (aOR = 1.14, 95% CI = 1.1-1.18, p < 0.001), chronic pericarditis (aOR = 1.3, 95% CI = 1.2-1.3, p < 0.001), acute pericarditis (aOR = 1.6, 95% CI = 1.2-2.1, p < 0.001), constrictive pericarditis (aOR = 2.2, 95% CI = 1.5-3.2, p < 0.001), conduction disorders (aOR = 1.1, 95% CI = 1.08-1.13, p < 0.001), coronary artery disease (aOR = 1.14, 95% CI = 1.12-1.16, p < 0.001), heart failure (aOR = 1.2, 95% CI = 1.17-1.23, p < 0.001), and valvular heart disease (aOR = 1.3, 95% CI = 1.2-1.35, p < 0.001). The odds were similar between the two groups for developing cardiac arrest (aOR = 0.86, 95% CI = 0.8-0.98, p = 0.05). Conclusions Adults with thoracic malignancies who were treated with RT have higher odds of developing chronic pericarditis, acute pericarditis, constrictive pericarditis, conduction disorders, coronary artery disease, heart failure, and valvular heart disease while similar odds of developing cardiac arrest or acute myocardial infarction compared to the general adult population.

Outcomes in patients with COVID-19 and new onset heart blocks: Insight from the National Inpatient Sample database.

BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide health crisis since it first appeared. Numerous studies demonstrated the virus's predilection to cardiomyocytes; however, the effects that COVID-19 has on the cardiac conduction system still need to be fully understood. AIM: To analyze the impact that COVID-19 has on the odds of major cardiovascular complications in patients with new onset heart blocks or bundle branch blocks (BBB). METHODS: The 2020 National Inpatient Sample (NIS) database was used to identify patients admitted for COVID-19 pneumonia with and without high-degree atrioventricular blocks (HDAVB) and right or left BBB utilizing ICD-10 codes. The patients with pre-existing pacemakers, suggestive of a prior diagnosis of HDAVB or BBB, were excluded from the study. The primary outcome was inpatient mortality. Secondary outcomes included total hospital charges (THC), the length of hospital stay (LOS), and other major cardiac outcomes detailed in the Results section. Univariate and multivariate regression analyses were used to adjust for confounders with Stata version 17. RESULTS: A total of 1058815 COVID-19 hospitalizations were identified within the 2020 NIS database, of which 3210 (0.4%) and 17365 (1.6%) patients were newly diagnosed with HDAVB and BBB, respectively. We observed a significantly higher odds of in-hospital mortality, cardiac arrest, cardiogenic shock, sepsis, arrythmias, and acute kidney injury in the COVID-19 and HDAVB group. There was no statistically significant difference in the odds of cerebral infarction or pulmonary embolism. Encounters with COVID-19 pneumonia and newly diagnosed BBB had a higher odds of arrythmias, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock than those without BBB. However, unlike HDAVB, COVID-19 pneumonia and BBB had no significant impact on mortality compared to patients without BBB. CONCLUSION: In conclusion, there is a significantly higher odds of inpatient mortality, cardiac arrest, cardiogenic shock, sepsis, acute kidney injury, supraventricular tachycardia, ventricular tachycardia, THC, and LOS in patients with COVID-19 pneumonia and HDAVB as compared to patients without HDAVB. Likewise, patients with COVID-19 pneumonia in the BBB group similarly have a higher odds of supraventricular tachycardia, atrial fibrillation, atrial flutter, ventricular tachycardia, acute kidney injury, sepsis, need for mechanical ventilation, and cardiogenic shock as compared to those without BBB. Therefore, it is essential for healthcare providers to be aware of the possible worse predicted outcomes that patients with new-onset HDAVB or BBB may experience following SARS-CoV-2 infection.

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches.

This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN.

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient's lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets-multi-ROI and single-ROI-are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach.

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 µm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC's nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments.

Independent Predictors and Causes of Thirty-Day Gastrointestinal Readmissions Following COVID-19-Related Hospitalizations: Analysis of the National Readmission Database.

Background: The coronavirus disease 2019 (COVID-19) pandemic led to significant mortality and morbidity in the United States. The burden of COVID-19 was not limited to the respiratory tract alone but had significant extrapulmonary manifestations. We decided to examine the causes, predictors, and outcomes of gastrointestinal (GI)-related causes of 30-day readmission following index COVID-19 hospitalization. Methods: We used the National Readmission Database (NRD) from 2020 to identify hospitalizations among adults with principal diagnosis of COVID-19. We identified GI-related hospitalizations within 30 days of index admission after excluding elective and traumatic admissions. We identified the top causes of GI-related readmission, and the outcomes of these hospitalizations. We used a multivariate Cox regression analysis to identify the independent predictors of readmission. Results: Among 1,024,492 index hospitalizations with a primary diagnosis of COVID-19 in the 2020 NRD database, 644,903 were included in the 30-day readmission study. Of these 3,276 (0.5%) were readmitted in 30 days due to primary GI causes. The top five causes of readmissions we identified in this study were GI bleeding, intestinal obstruction, acute diverticulitis, acute pancreatitis, and acute cholecystitis. Multivariate Cox regression analysis done adjusting for confounders showed that renal failure, alcohol abuse, and peptic ulcer disease were associated with increased odds of 30-day readmission from GI-related causes. Conclusions: GI manifestations of COVID-19 are not uncommon and remain an important cause of readmission. Targeted interventions addressing the modifiable predictors of readmission identified will be beneficial in reducing the burden on already limited healthcare resources.

Impact of Hospital Readmissions Reduction Program Policy on 30-Day and 90-Day Readmissions in Patients With Acute Myocardial Infarction: A 10-Year Trend From the National Readmissions Database.

Hospital readmissions following acute myocardial infarction (AMI) pose a significant economic burden on health care utilization. The hospital readmission reduction program (HRRP) enacted in 2012 focused on reducing readmissions by penalizing Centers for Medicare & Medicaid Services (CMS) Medicare hospitals. We aim to assess the trend of readmissions after AMI hospitalization between 2010 and 2019 and assess the impact of HRRP. The National Readmission Database was queried to identify AMI hospitalizations between 2010 and 2019. In the primary analysis, trends of 30-day and 90-day all-cause and AMI specific readmissions were assessed from 2010 to 2019. In the secondary analysis, trend of readmission means length of stay and mean adjusted total cost were calculated. There were a total of 592,015 30-day readmissions and 787,008 90-day readmissions after an index hospitalization for AMI between 2010 and 2019. The rates of 30-day and 90-day all-cause readmissions decreased significantly from 12.8% to 11.6%, (P = 0.0001) and 20.6 to 18.8, (P = 0.0001) respectively in the decade under study. With regards to HRRP policy intervals, the pre-HRRP period from 2010 to 2012 showed a downward trend in all-cause readmission (12.8% to 11.6%) and similarly a downward trend was also seen in the post HRRP period (2013-2015:11.0%-8.2%, 2016-2019-12.3-11.7%). Secondary analysis showed a trend towards increase in mean length of stay (4.54-4.96 days, P = 0.0001) and adjusted total cost ($13,449-$16,938) in 30-day all-cause readmission for AMI in the decade under review. In our National Readmission Database-based analysis of patients readmitted to hospitals within 30-days and 90-days after AMI, the rate of all-cause readmissions down trended from 2010 to 2019.

Factor Xa inhibitors versus vitamin K antagonist in morbidly obese patients with venous thromboembolism: a systematic review and meta-analysis.

INTRODUCTION: Guidelines have endorsed non-vitamin K antagonist oral anticoagulants (NOACs), consisting of factor Xa inhibitors (xabans) and direct thrombin inhibitors, as the first line of treatment in venous thromboembolism (VTE) and atrial fibrillation. However, morbidly obese patients were under-represented in landmark trials of NOACs. Therefore, this study aimed to systematically review and perform a meta-analysis of studies on xabans versus vitamin K antagonist (VKA) in this high-risk population with VTE. METHODS: PubMed, Embase, Medline, Cochrane library, and Google Scholar databases were searched to identify studies that compared xabans and VKA in treating morbidly obese patients with VTE. Morbid obesity was defined as body weight ≥ 120 kg or BMI ≥ 40 kg/m2. Outcomes of interest included recurrent VTE, major bleeding, and clinically relevant non-major bleeding (CRNMB). RESULTS: Eight studies comprising 30,895 patients were included. A total of 12,755 patients received xabans while 18,140 received VKAs. No significant difference in the odds of recurrent VTE (OR 0.75, 95% CI 0.55-1.01) and CRNMB (OR 0.69, 95% CI 0.44-1.09) was observed between the xabans group and the VKA group. However, the xabans group was associated with lower odds of major bleeding (OR 0.70, 95% CI 0.59-0.83). CONCLUSION: Xabans have lower odds of major bleeding but similar odds of recurrent VTE when compared with VKAs in treating VTE in morbidly obese patients. Large registry analyses or future randomized controlled trials will be helpful in confirming these findings.

Trends and Outcomes of Transcatheter Aortic Valve Implantation in Patients With Peripheral Arterial Disease: Insights From the National Readmissions Database.

Due to shared risk factors, many patients with severe aortic stenosis (AS) also have concomitant peripheral arterial disease (PAD). The readmission rates and long term clinical outcomes of these patients following transcatheter aortic valve implantation (TAVI) in a large sample has not been well defined. The National Readmissions Database (NRD) (2015-2019) was used to identify patients undergoing TAVI. TAVI in patients with PAD was compared with those without PAD using a propensity score matched (PSM) analysis to obtain adjusted odds ratios (aOR) with 95% confidence intervals (CI) of net adverse clinical events (NACE), and its components. A total of 189,216 patients were identified, of which 14,925 patients (7386 with PAD, 7539 without PAD) were selected for adjusted analysis. Using PSM, patients with PAD undergoing TAVI had significantly higher in-hospital adjusted odds of NACE (aOR 1.60, 95% CI 1.36-1.88), and mortality (aOR 4.10, 95% CI 2.88-5.83). However, rates of other in-hospital peri procedural complications (stroke, major bleeding, paravalvular leak, cardiogenic shock) were not significantly different. There was no significant difference in the incidence of NACE, mortality, or other complications between the 2 groups at 30- and 180 days follow-up. Patients with PAD undergoing TAVI have an increased risk of mortality and NACE during the periprocedural period. However, following discharge, there was no statistically significant difference in 30 days and 6 month outcomes of TAVI in this population compared to those without significant PAD.

Validation and exploratory factor analysis of Urdu Version of Chronic Liver Disease Questionnaire.

Objective: To present and validate psychometric properties of Urdu version CLDQ, yet another objective was to do exploratory factorial analysis (EFA) of CLDQ Urdu version. Methods: This Cross-sectional Analytical Study was conducted at Dr. Ruth K.M. Pfau Civil Hospital Karachi during the period Nov. 15, 2021 to Jan. 30, 2022. CLDQ Urdu questionnaire was self-administered by the patients. The questionnaire consisted of 29 items and responses were recorded on 7-point Likert type scale. Reliability testing was done by Cronbach's α, test value of >0.7 is taken as reliable. Exploratory factor analysis (EFA) was conducted with principal component analysis with varimax rotation. Adequacies for conduction of EFA depended on Kaiser-Meyer-Olkin (KMO) value of ≥0.5 and Bartlett's Test of Sphericity (BTS) of ≤0.05. Mean CLDQ Urdu scores were also compared with Child Class using ANOVA and post-hoc analysis was done. Results: A total of 320 patients were selected after informed consent. All conditions for adequate EFA were met (Cronbach's α =.949; KMO = .846; BTS ≤.001). Mean CLDQ Urdu score was 156.74 in male and 133.27 in female (p<.001). Child Class-A had best quality of life with score of 186.63 ±6.91 and Child Class-C had the worst with scores of 109.78 ±21.33. EFA resulted in reduction of domains to 4 (Muscular Symptoms, Emotional Symptoms, Abdominal Symptoms & Somnolence) & reduced the number of items from 29 to 11. Conclusion: Urdu CLDQ version is validated in our settings. EFA resulted in reductions in number of domains and items. CLDQ Urdu showed that quality of life decreases significantly with Child Class.

Readmission and In-Hospital Outcomes After Transcatheter Aortic Valve Replacement in Patients With Dementia.

BACKGROUND/PURPOSE: The prevalence of dementia and aortic stenosis (AS) increases with each decade of age. Transcatheter aortic valve replacement (TAVR) is a definitive treatment for AS, but there are scarce data on morbidity, mortality, and readmission risk after TAVR in patients with dementia. METHODS/MATERIALS: We identified all admissions for TAVR in patients with AS in the National Readmissions Database in 2017-2018 and stratified them according to the presence or absence of a secondary diagnosis of dementia. Inpatient outcomes were compared using logistic regression. Cox proportional-hazards models were used to compare 30-, 60-, and 90-day readmissions. RESULTS: A total of 48,923 index hospitalizations for TAVR were identified, of which 2192 (4.5 %) had a secondary diagnosis of dementia. Presence of dementia was associated with higher odds of delirium, pacemaker placement, acute kidney injury, and fall in hospital. The hazard of 30-day readmission was not significantly different between patients with and without dementia, but patients with dementia experienced a higher hazard of 60-day readmission (HR 1.15, 95 % CI 1.03-1.26, p = 0.011) in the unadjusted model and higher hazard of 90-day readmission in both unadjusted (HR 1.18, 95 % CI 1.08-1.30, p < 0.001) and adjusted models (aHR 1.14, 95 % CI 1.04-1.25, p = 0.004). CONCLUSIONS: Patients with dementia who undergo TAVR are at higher risk of in-hospital adverse outcomes and 60- and 90-day readmissions compared with patients without dementia. These estimates should be integrated into shared decision-making discussions with patients and families.

Outcomes After Transcatheter Aortic Valve Implantation in Patients Excluded From Clinical Trials.

Background: The use of transcatheter aortic valve implantation (TAVI) in patients with aortic valve disease excluded from clinical trials has increased with no large-scale data on its safety. Objectives: The purpose of this study was to assess the trend of utilization and adjusted outcomes of TAVI in clinical trials excluded (CTE) vs clinical trials included TAVI (CTI-TAVI) patients. Methods: We used the National Readmission Database (2015-2019) to identify 15 CTE-TAVI conditions. A propensity score-matched analysis was used to calculate the adjusted odds ratio (aOR) of net adverse clinical events (composite of mortality, stroke, and major bleeding) in patients undergoing CTE-TAVI vs CTI-TAVI. Results: Among the 223,238 patients undergoing TAVI, CTE-TAVI was used in 41,408 patients (18.5%). The yearly trend showed a steep increase in CTE-TAVI utilization (P = 0.026). At index admission, the adjusted odds of net adverse clinical events (aOR: 1.83, 95% CI: 1.73-1.95) and its components, including mortality (aOR: 2.94, 95% CI: 2.66-3.24), stroke (aOR: 1.20, 95% CI: 1.07-1.34), and major bleeding (aOR: 1.49, 95% CI: 1.36-1.63) were significantly higher in CTE-TAVI compared with CTI-TAVI. Among the individual contraindications to clinical trial enrollment in the CTE-TAVI, patients with bicuspid aortic valve, leukopenia, and peptic ulcer disease appeared to have similar outcomes compared with CTI-TAVI, while patients with end-stage renal disease, bioprosthetic aortic valves, and coagulopathy had a higher readmission rate at 30 and 180 days. Conclusions: CTE-TAVI utilization has increased significantly over the 4-year study period. Patients undergoing CTE-TAVI have a higher likelihood of mortality, stroke, and bleeding than those undergoing CTI-TAVI.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.