ABSTRACT
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.
ABSTRACT
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.
ABSTRACT
Background: Our understanding of the spectrum of neurological manifestations associated with COVID-19 keeps evolving. Reports of life-threatening neurological complications, such as acute disseminated encephalomyelitis (ADEM), are alarmingly growing in number. Case presentation: We report a 42 years old previously healthy man who presented with left visual loss and cognition deterioration, manifesting at least ten days after infection with SARS-CoV-2. Serological work-up for potential immunological markers (i.e., antibodies against aquaporin-4 and myelin oligodendrocyte glycoprotein) were negative. Magnetic resonance imaging revealed multiple bilateral and asymmetrical lesions in the brainstem, cortical, juxtacortical, and periventricular regions, with surrounding edema. Post-contrast sequences demonstrated punctate, ring, and open ring enhancement patterns. Methylprednisolone pulse therapy was initiated for the patient, and he was placed on rituximab. After one month, his clinical symptoms had resolved, and his cognitive function was normal. Conclusion(s): We conducted an extensive literature search, and COVID-19-associated ADEM cases reported thus far were identified and reviewed. ADEM often occurs in a post-infectious fashion;however, it is unclear how SARS-CoV-2 infection can trigger such rapidly progressive episodes of encephalopathy and demyelination. Nevertheless, considering the alarming number of cases of ADEM developing after SARS-CoV-2 infection, neurologists should consider this severe phenotype of COVID-19 neurological complication in mind, enabling prompt therapeutic interventions to be made.Copyright © 2022
ABSTRACT
Objective: The coronavirus disease-2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2, started in Wuhan, China, and was recognized as a pandemic by the World Health Organization. In Iran, the first confirmed COVID-19 case was officially reported on February 19. The aim of this study was to investigate the epidemiological and clinical characteristics, and comorbid conditions, and determine risk factors for the mortality of COVID-19 patients as well as provide a comparison of the epidemiological features between the 3 waves of COVID-19 in the North-East of Iran from January 21, 2020, to March 20, 2021. Material(s) and Method(s): The current retrospective epidemiological population-based study was conducted on COVID-19 patients who were admitted to the hospitals affiliated to the Mashhad University of Medical Sciences in Razavi-Khorasan province, Iran. The data were extracted from the Medical Care Monitoring System of the Mashhad University of Medical Sciences. Result(s): In total, 43.6% of subjects had at least one coexisting underlying medical condition. The most common comorbidities were hypertension, diabetes, and cardiovascular diseases with the prevalence of 19.7, 15.1, and 13.3%;respectively. The overall case fatality rate was 15.0%, following a median of 4 days [interquartile range (IQR) 1-10] of hospitalization. The mean+/-SD and the median (IQR) of age in expired subjects were 67.40+/-18.27 and 70 (59-81) years;respectively. Conclusion(s): Our results demonstrated that age >60, male sex, loss of consciousness, respiratory distress, having at least one comorbidity, and diabetes were mortality risk factors among COVID-19 patients.Copyright © 2023 by Turkiye Klinikleri.
ABSTRACT
Boarding and disembarking an aircraft is a time-critical airport ground handling process. Operations in the confined aircraft cabin must also reduce the potential risk of virus transmission to passengers under current COVID-19 boundary conditions. Passenger boarding will generally be regulated by establishing passenger sequences to reduce the influence of negative interactions between passengers (e.g., congestion in the aisle). This regulation cannot be implemented to the same extent when disembarking at the end of a flight. In our approach, we generate an optimized seat allocation that takes into account both the distance constraints of COVID-19 regulations and groups of passengers traveling together (e.g., families or couples). This seat allocation minimizes the potential transmission risk, while at the same time we calculate improved entry sequences for passengers groups (fast boarding). We show in our simulation environment that boarding and disembarkation times can be significantly reduced even if a physical distance between passenger groups is required. To implement our proposed sequences during real disembarkation, we propose an active information system that incorporates the aircraft cabin lighting system. Thus, the lights above each group member could be turned on when that passenger group is requested to disembark. © ATM 2021. All rights reserved.
ABSTRACT
Objective(s): Neurological complications of COVID-19 have raised serious concerns among the experts, and hence, many mechanisms have been proposed to explain it. We sought to collect evidence by investigating the possible effect of the virus on multiple sclerosis (MS) disease course, in COVID-19-contracted people with relapsing-remitting multiple sclerosis (RRMS). Material(s) and Method(s): This prospective-retrospective hybrid cohort study conducted from July 2020 until July 2021, compares the rates of probable disease progressions (PDP) and relapses between the pre- and post-COVID-19 periods of RRMS patients, using non-parametric tests, a matched binary logistic model offset by follow-up, Kaplan-Meier plots, and a cox regression model. Result(s): The PDP rate (0.06 vs 0.19, P = 0.04) and relapse rate (0.21 vs 0.30, P = 0.30) were both lower in the post-COVID-19 period compared to the pre-COVID-19 period. However, matched binary logistic model offset by follow-up failed to display a significant difference in odds of PDP (OR [95% confidence interval]: 0.41 [0.13, 1.34], P = 0.14) and relapse (OR [95% confidence interval]: 0.99 [0.45, 2.17], P = 0.99), at the endpoints of pre- and post-COVID-19 periods. Kaplan-Meier plots and cox regression model did not show significant difference between the pre- and post-COVID-19 periods, regarding both the PDP rates (HR [95% CI]: 0.46 [0.12, 1.73], P = 0.25) and relapse rates (HR [95% CI]: 0.69 [0.31, 1.53], P = 0.36). Conclusion(s): Our results suggest that COVID-19 contraction is unlikely to increase the risk of MS progression and relapse in the following months after infection.
ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) is spreading rapidly and has affected millions of people worldwide. Comorbid diseases have complicated the course of infection and increased mortality. Myasthenia gravis (MG) affects the neuromuscular junctions (NMJs) and can compromise respiratory muscle action, leading to worse clinical outcomes in individuals infected with the COVID-19 theoretically. In this study, the aim is to assess the pattern of COVID-19 infection in patients with MG based on several factors. Methods: This was a prospective cohort study following 150 patients with MG over a six-month period. The patients were monitored for the development of signs and symptoms of the COVID-19 infection. Results: Comparison of the patients infected with COVID-19 with MG and those not infected was performed independently based on age, duration since MG diagnosis, status of thymectomy, and current clinical status of MG disease. Data analysis did not reveal increased susceptibility or increased severity of COVID-19 illness based the criteria assessed. Conclusion: COVID-19 related deaths and susceptibility were not related to age, thymectomy status, and disease duration in patients with MG. © 2021 Iranian Neurological Association, and Tehran University of Medical Sciences Published by Tehran University of Medical Sciences.
ABSTRACT
Background: Despite many studies, it is still unclear how patients with neuromyelitis optica spectrum disorder (NMOSD) would respond to coronavirus disease 2019 (COVID-19). We conducted a research on prevalence of COVID-19 in patients with NMOSD in Isfahan, Iran. We have also reviewed the recent publications on this issue. Methods: 149 patients with NMOSD who were under medications were monitored for confirmed cases of COVID-19. Prevalence of COVID-19 in addition to mean age, mean duration of disease, and mean age of onset of infected patients and uninfected patients were calculated via Microsoft Excel software. Results: The prevalence of COVID-19 in studied patients with NMOSD was 5.37%. Mean age, mean duration of disease, and mean age of onset of eight patients (male to female ratio: 1:3) diagnosed with COVID-19 were 33.62 ± 5.20 years, 6.87 ± 6.05 years, and 26.75 ± 6.94 years, respectively, while they were 39.97 ± 11.37 years, 7.50 ± 3.91 years, and 32.46 ± 11.29 years for uninfected patients with NMOSD (n = 141). No significant association was observed between the type of medications and prevalence of COVID-19 (P > 0.05). Conclusion: There is not a consensus in the literature on the prevalence of COVID-19 in patients with NMOSD and the effect of NMOSD medications on susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The prevalence of COVID-19 in our sample was 5.37%. The impact of the kind of NMOSD medication on the prevalence of COVID-19 in patients with NMOSD was found to be insignificant. Moreover, the infected patients were relatively younger, and their disease started earlier in comparison to uninfected patients. © 2021 Iranian Neurological Association, and Tehran University of Medical Sciences Published by Tehran University of Medical Sciences.
ABSTRACT
OBJECTIVES: To gather, synthesize, and meta-analyze data regarding the risk factors associated with a severe course of COVID-19 among patients with multiple sclerosis (pwMS). METHODS: MEDLINE, Embase, Scopus, and WoS were searched in May 2021. Briefly, the eligibility criteria included: 1) studies assessing COVID-19 severity among adult pwMS; 2) definitive diagnoses or high clinical suspicion of COVID-19; 3) a categorization of COVID-19 severity into at least two categories; 4) quantitative effect size and precision measurements; and 5) English language; and 6) clear effect size/precision measures. internal validity of studies was assessed using the NIH Quality Assessment Tools. A list of possible risk factors was created based on the search results and was later used in extraction, synthesis, and meta-analysis of the data. RESULTS: Thirteen studies were included in the syntheses. Outcome measures were either extracted from the papers, obtained from the primary researchers or calculated manually. The meta-analyses showed a significantly (P<0.05) increased odds of a severe COVID-19 in pwMS with all of the assessed risk factors, except smoking and most DMTs. CONCLUSION: This study facilitates evidence-based risk/benefit assessments in practice. Older men with progressive MS on anti-CD20 therapies are more at risk of an unfortunate COVID-19 outcome.
Subject(s)
COVID-19 , Multiple Sclerosis , Adult , Aged , Humans , Male , Multiple Sclerosis/complications , Multiple Sclerosis/diagnosis , Multiple Sclerosis/epidemiology , Risk Factors , SARS-CoV-2ABSTRACT
We provide a mixed-integer programming model (MIP) to assign airplane passengers to seats while preserving two types of social distancing: the distance from the passengers' seats to the aisle and the distance among groups of passengers who are not travelling together. The method assigns passengers travelling within a family group to seats near others of the same group. We present a heuristic algorithm to solve the proposed MIP. This algorithm is warm started with an initial seat assignment. Stochastic simulation experiments using the new method confirm that more passengers can be assigned safely to the seats when family groups are considered. For a certain load of passengers, as the percentage of family groups compared to singleton passengers increases, the model can practice social distancing among more passengers from different groups. The proposed model provides a superior seating assignment compared to an airline policy of blocking all middle-seats.
ABSTRACT
Background: Despite many studies, it is still unclear how patients with neuromyelitis optica spectrum disorder (NMOSD) would respond to coronavirus disease 2019 (COVID-19). We conducted a research on prevalence of COVID-19 in patients with NMOSD in Isfahan, Iran. We have also reviewed the recent publications on this issue. Methods: 149 patients with NMOSD who were under medications were monitored for confirmed cases of COVID-19. Prevalence of COVID-19 in addition to mean age, mean duration of disease, and mean age of onset of infected patients and uninfected patients were calculated via Microsoft Excel software. Results: The prevalence of COVID-19 in studied patients with NMOSD was 5.37%. Mean age, mean duration of disease, and mean age of onset of eight patients (male to female ratio: 1:3) diagnosed with COVID-19 were 33.62 +/- 5.20 years, 6.87 +/- 6.05 years, and 26.75 +/- 6.94 years, respectively, while they were 39.97 +/- 11.37 years, 7.50 +/- 3.91 years, and 32.46 +/- 11.29 years for uninfected patients with NMOSD (n = 141). No significant association was observed between the type of medications and prevalence of COVID-19 (P > 0.05). Conclusion: There is not a consensus in the literature on the prevalence of COVID-19 in patients with NMOSD and the effect of NMOSD medications on susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The prevalence of COVID-19 in our sample was 5.37%. The impact of the kind of NMOSD medication on the prevalence of COVID-19 in patients with NMOSD was found to be insignificant. Moreover, the infected patients were relatively younger, and their disease started earlier in comparison to uninfected patients.
ABSTRACT
This article has been withdrawn at the request of the authors and editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.