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Objective: The purpose of this study was to determine the factors that increase a patient's chances of death from COVID-19. Study Design: Retrospective study Place and Duration: Medicine department of Benazir Bhutto Hospital (BBH) and Holy Family Hospital (HFH), Rawalpindi during the period from November 2021 to April 2022. Method(s): Total 142 patients of both genders of confirmed coronavirus disease were included. After receiving informed written consent from each participant, detailed demographic information was obtained. This information included the participant's age, gender, body mass index, and list of co-morbidities. Frequency of mortality and factors that increase a patient's chances of death were recorded. SPSS 24.0 was used to analyze all data. Result(s): We found that 87 (61.3%) cases were males and 55 (38.7%) patients were females. Majority of the patients 53 (37.3%) had age >45 years. 65 (45.8%) patients were smokers. Severity of disease was found in 76 (53.5%) cases. There were 95 (66.9%) patients had hypertension, diabetes mellitus found in 75 (52.8%) cases, cardiovascular disease in 48 (33.8%) cases, pulmonary disease in 40 (28.2%) cases and chronic kidney disease in 34 (23.9%) cases. Among 142 patients, 27 (19.01%) patients were died. Among non-survivals, kidney dysfunction was the most common reason found in 17 cases, followed by cardiovascular and diabetes mellitus. Conclusion(s): We came to the conclusion that clinical risk factors for a fatal consequences associated with coronavirus include chronic chronic conditions, complications, and demographic variables. These risk factors include acute renal injury, diabetes, hypertension, heart disease, male sex, older age, current smoker, and obesity. The findings might be used to future study on the disease as well as its control and prevention. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.
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Coronavirus (COVID-19) is a pandemic disease classified by the World Health Organization. This virus triggers several coughing problems (e.g., flu) that include symptoms of fever, cough, and pneumonia, in extreme cases. The human sputum or blood samples are used to detect this virus, and the result is normally available within a few hours or at most days. In this research, we suggest the implementation of automated deep learning without require handcrafted expertise of data scientist. The model developed aims to give radiologists a second-opinion interpretation and to minimize clinicians' workload substantially and help them diagnose correctly. We employed automated deep learning via Google AutoML for COVID-19 X-ray detection to provide an automated and faster diagnosis. The model is employed on X-ray images to detect COVID-19 based on several binary and multi-class cases. It consists of three scenarios of binary classification categorized as healthy, pneumonia, and COVID-19. The multi-classification model based on these three labels is employed to differentiate between them directly. An investigational review of 1125 chest X-rays indicates the efficiency of the proposed method. AutoML enables binary and multi-classification tasks to be performed with an accuracy up to 98.41%.
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Objective: The objectives of this study were to measure the global impact of the pandemic on the volumes for intravenous thrombolysis (IVT), IVT transfers, and stroke hospitalizations over 4 months at the height of the pandemic (March 1 to June 30, 2020) compared with two control 4-month periods. Background: The COVID-19 pandemic led to widespread repercussions on the delivery of health care worldwide. Design/Methods: We conducted a cross-sectional, observational, retrospective study across 6 continents, 70 countries, and 457 stroke centers. Diagnoses were identified by ICD-10 codes and/or classifications in stroke center databases. Results: There were 91,373 stroke admissions in the 4 months immediately before compared to 80,894 admissions during the pandemic months, representing an 11.5% (95%CI,-11.7 to-11.3, p<0.0001) decline. There were 13,334 IVT therapies in the 4 months preceding compared to 11,570 procedures during the pandemic, representing a 13.2% (95%CI,-13.8 to-12.7, p<0.0001) drop. Interfacility IVT transfers decreased from 1,337 to 1,178, or an 11.9% decrease (95%CI,-13.7 to-10.3, p=0.001). There were greater declines in primary compared to comprehensive stroke centers (CSC) for stroke hospitalizations (-17.3% vs-10.3%, p<0.0001) and IVT (-15.5% vs-12.6%, p=0.0001). Recovery of stroke hospitalization volume (9.5%, 95%CI 9.2-9.8, p<0.0001) was noted over the two later (May, June) versus the two earlier (March, April) months of the pandemic, with greater recovery in hospitals with lower COVID-19 hospitalization volume, high volume stroke center, and CSC. There was a 1.48% stroke rate across 119,967 COVID-19 hospitalizations. SARS-CoV-2 infection was noted in 3.3% (1,722/52,026) of all stroke admissions. Conclusions: The COVID-19 pandemic was associated with a global decline in the volume of stroke hospitalizations, IVT, and interfacility IVT transfers. Primary stroke centers and centers with higher COVID19 inpatient volumes experienced steeper declines. Recovery of stroke hospitalization was noted in the later pandemic months, with greater recovery in hospitals with lower COVID-19 hospitalizations, high volume stroke centers, and CSCs.
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Introduction: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The objective was to examine whether subarachnoid hemorrhage(SAH) hospitalizations and ruptured aneurysm coiling interventions demonstrated similar declines. Methods: We conducted a global, retrospective, observational study across 6 continents, 37 countries, and 140 comprehensive stroke centers. Patients with diagnosis of SAH, ruptured aneurysm coiling interventions, COVID-19 were identified using ICD-10 codes or by prospectively maintained stroke databases. The 3-month cumulative volume, monthly volumes for SAH hospitalizations and ruptured aneurysm coiling procedures were compared for the period before (1- year and immediately before) and during the COVID-19 pandemic (March 1 to May 31, 2020). Results: There was a significant decline in SAH hospitalizations with 2,044 admissions in the 3 months immediately before and 1,585 admissions during the pandemic, representing a decline of 22.5% (95%CI, -24.3 to -20.7, p<0.0001). Embolization of ruptured aneurysms declined with 1,170 to 1,035 procedures, respectively, representing an 11.5%(95%CI, -13.5 to -9.8, p=0.002) drop. Hospitals with higher COVID-19 hospitalization burden demonstrated greater declines in SAH and ruptured aneurysm coiling compared to lower COVID-19 burden. A relative increase in coiling of ruptured aneurysms was noted in low coiling volume hospitals of 41.1% (95%CI, 32.3-50.6, p=0.008) despite a decrease in SAH admissions in this tertile. Conclusions: There was a global decrease in subarachnoid hemorrhage admissions and ruptured aneurysm embolizations during the COVID-19 pandemic. Among low-volume coiling SAH hospitals, there was an increase in the ruptured aneurysm coiling intervention. These findings in SAH are consistent with a global decrease in other emergencies such as stroke and myocardial infarction.