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Despite the devastating effects of firearm violence on individuals, families, and communities, research on the impact of the COVID-19 pandemic on firearm violence remains at a minimum. Our study contributes to this body of research by estimating the impact of two critical pandemic era timeframes on fatal and nonfatal shooting victimizations in Detroit, Michigan, using an innovative Bayesian Structural Time Series methodology. For each timeframe, we consider the impact of the pandemic era on total shooting victimizations, shooting victimizations that occurred at a residence (or at home), and shooting victimizations that occurred elsewhere. Our findings suggest that the pandemic era contributed to all three types of shooting victimizations in Detroit. We discuss the limitations of our study, along with directions for future research. Overall, we believe that our study underscores the importance of adopting a comprehensive and evidence-based strategy to prevent firearm-related fatalities and injuries. [ FROM AUTHOR] Copyright of Journal of Crime & Justice is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Background: NFX1-type zinc finger-containing 1 (ZNFX1) is an interferon-stimulated double-stranded RNA sensor that restricts the replication of RNA viruses in mice. ZNFX1 deficiency in humans is very rare;to date, only fifteen cases have been reported by Vavassori S et al. (10.1016/j.jaci.2021.03.045). The disease presented in all cases as severe viral infections complicated by multisystem inflammation evolved to multiorgan failure with a high mortality rate. Pediatric Allergy and Immunology Section at Queen Rania Children's Hospital in Jordan had confirmed the diagnosis of ZNFX1 deficiency in an infant at his first presentation with severe viral illness based on the positive family history of one sibling death caused by complicated COVID-19 infection. Case presentation: A 12-month-old boy was born to consanguineous parents, full-term, with no NICU admission. He was doing well till the age of four months when he was admitted to the hospital with fever, hypoactivity, and maculopapular skin rash. On admission, he was ill, hypoactive, and febrile, and a physical exam showed hepatosplenomegaly and maculopapular skin rash. His lab showed thrombocytopenia, elevated transaminases, hyperferritinemia, and high CRP;he was treated with broad-spectrum antibiotics, but he continued to deteriorate, and his infectious workup was unrevealing, including COVID-19 PCR. His older sibling died at eight months in 2020 when she got a COVID-19 infection, deceased after rapid deterioration evolved to multiorgan failure. Unfortunately, she had no stored DNA, as she was treated at a peripheral hospital. Based on this presentation and the fatal COVID-19 infection, pediatric immunology service got consulted;we did an immunological workup, which showed normal lymphocyte subsets, Immunoglobulins, and bacterial antibodies. Whole exome sequencing showed a homozygous frameshift mutation in the ZNFX1 gene, protein change defect had detected;p.Tyr555MetfsTer6, and nucleotide change variant: c.1663_1665delTACinsAT. Family screening showed heterozygous for the same variant in both parents and a healthy sibling. The patient was diagnosed with the hemophagocytic lymphohistiocytosis-like disease and treated with steroids, intravenous immunoglobulin, and antimicrobials, he showed complete recovery, and we are going to do bone marrow transplantation as his brother is 8/8 HLA matched.Copyright © 2023 Elsevier Inc.
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In the Chelyabinsk region in 2020, 33 473 cases of a new coronavirus infection (COVID-19) were detected, of which 493 were fatal. The peak incidence of COVID-19 was recorded in November-December 2020. Purpose of research: description of the course of the infectious process of COVID-19 in patients with a fatal outcome. Material and methods. A retrospective analysis of case histories of patients with COVID-19 who had a lethal outcome was carried out using the method of continuous sampling from November 2020 to April 2021 on the basis of the Regional Infectious Diseases Center of the Regional Clinical Hospital No. 3 of the Chelyabinsk Region. Diagnosis was based on a comprehensive assessment of the anamnesis, clinical, laboratory and instrumental data in accordance with the Interim Guidelines "Prevention, Diagnosis and Treatment of Novel Coronavirus Infection (COVID-19)" version 9 dated 10/26/2020. Verification of the etiological diagnosis was carried out by PCR with reverse transcription to detect SARS-CoV-2 RNA in the nasopharyngeal secretion. The autopsy material was studied in the Pathoanatomical Department No. 9 of the Chelyabinsk Regional Pathological Bureau. Results and discussion. The average age of patients with a fatal outcome was 70 years, more often they were people with blood groups I and II, not vaccinated against COVID-19, pneumococcal infection and influenza;no gender differences were found. For patients with a fatal outcome from COVID-19, the presence of concomitant pathology is most characteristic: arterial hypertension (58%), atherosclerosis (45%), type 2 diabetes mellitus (41.5%), malignant neoplasms (17%). Among the "lethal patients", 50% had lung lesions at admission CT 2 and 25% had changes in the lungs consistent with CT 1;there was also a violation of cognitive functions in 21% of individuals. When comparing the analysis of studies of autopsy material and the clinical picture of patients, it was found that the morphological substrate of COVID-19 is both diffuse alveolar damage and simultaneous damage to the vascular bed, which is accompanied by damage to various organs and systems of the patient's body.Copyright © Eco-Vector, 2022.
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The article presents two clinical cases of patients with a fatal outcome after a coronavirus infection. The first patient had sepsis and purulonecrotic phlegmon complication after radiofrequency ablation of the cavatricuspid isthmus. The second one had a complication in the form of the esophageal rupture in the middle third after transesophageal echocardiography.Copyright © 2021, NJSC Institute of Cardiological Technology (INCART). All rights reserved.
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BACKGROUND: The coronavirus pandemic has had an extremely negative impact on the patients with diabetes mellitus (DM both in terms of a more severe course of COVID-19 and an increased risk of death. AIM: Analysis of risk factors for death due to COVID-19 in patients with DM type 1 and type 2 (DM1 and DM2). MATERIALS AND METHODS: Retrospective analysis of the database of the national diabetes register (NDR), which included DM patients with COVID-19 and reported virus infection outcome (recovery/or death) in 15 712 DM1 and 322 279 DM2 patients during a 2-year follow-up period (01/02/2020 to 03/04/2022) (discharge date)). RESULTS: Case fatality rate in patients with DM, who underwent COVID-19 was 17.1% (DM1-8.8%;DM2-17.5%). As a result of multivariate regression analysis of seven significant factors in DM1 and thirteen in DM2 (evaluated by univariate anlisys), a number of the most important predictors of risk for fatal outcome were identified: in DM1 these were age >= 65 years (OR =4.01, 95% CI: 1.42-11.36), presence of arterial hypertension (AH) (OR =2.72, 95% CI: 1.03-7.16) and diabetic foot syndrome (DFS) (OR = 7.22, 95% CI: 1.98-26.29);for T2DM: age >= 65 years (OR =2.53, 95% CI: 1.96-3.27), male (OR =1.51, 95% CI: 1.23-1.84), duration DM >= 10 years (OR =2.01, 95% CI: 1.61-2.51), BMI >= 30 kg/m2 (OR =1.26, 95% CI: 1.02-1.55), ASCVD/CKD (OR =1.49, 95% CI: 1.01-2.04), history of diabetic coma (OR =12.97, 95% CI: 1.89-88.99) and presence of disability ( OR =1.40, 95% CI: 1.14-1.73). In T2DM, the type of antidiabetic therapy (ADT) prior to COVID-19 (last visit before the development of infection) had a significant impact: Insulin therapy (OR = 1.64, 95% CI: 1.30-2.07), sulfonylureas (SU) (OR =1.51, 95% CI: 1.23-1.84));dipeptidyl peptidase-4 inhibitor (iDPP-4) therapy (OR =0.57, 95% CI: 0.39-0.83) and sodium- glucose cotransporter-2 inhibitor (iSGLT2) therapy (OR =0.64, 95% CI: 0.46-0.88). Vaccination was the most important protective factor in both types of DM: DM1 OR =0.19, 95% CI: 0.06-0.59;SD2 OR =0.20, 95% CI: 0.16-0.26. CONCLUSION: The common risk factor for fatal outcome in both DM1 and DM2 was age >= 65 years;in DM1 - history of hypertension and DFS, in DM2 - male sex, diabetes duration >= 10 years, BMI >= 30 kg/m2, history of ASCVD/CKD and diabetic coma, disability. In T2DM, significant differences in risk were observed depending on the type of ADT: insulin and SU therapy were factors that increased the risk of death, whereas therapy with iDPP-4 and iSGLT2 reduced the risk of death. Vaccination reduced the risk of death in DM1 and DM2 by 5.2 and 5-fold, respectively.
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Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc. © 2023 IEEE.
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Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.
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BACKGROUND: The coronavirus pandemic has had an extremely negative impact on the patients with diabetes mellitus (DM both in terms of a more severe course of COVID -19 and an increased risk of death. AIM: Analysis of risk factors for death due to COVID -19 in patients with DM type 1 and type 2 (DM1 and DM2). MATERIALS AND METHODS: Retrospective analysis of the database of the national diabetes register (NDR), which included DM patients with COVID-19 and reported virus infection outcome (recovery/or death) in 15 712 DM1 and 322 279 DM2 patients during a 2-year follow-up period (01/02/2020 to 03/04/2022) (discharge date)). RESULT(S): Case fatality rate in patients with DM, who underwent COVID -19 was 17.1% (DM1-8.8%;DM2-17.5%). As a result of multivariate regression analysis of seven significant factors in DM1 and thirteen in DM2 (evaluated by univariate anlisys), a number of the most important predictors of risk for fatal outcome were identified: in DM1 these were age >=65 years (OR =4.01, 95% CI: 1.42-11.36), presence of arterial hypertension (AH) (OR =2.72, 95% CI: 1.03 -7.16) and diabetic foot syndrome (DFS) (OR = 7.22, 95% CI: 1.98-26.29);for T2DM: age >= 65 years (OR =2.53, 95% CI: 1.96-3.27), male (OR =1.51, 95% CI: 1.23-1.84), duration DM >=10 years (OR =2.01, 95% CI: 1.61-2.51), BMI >= 30 kg/m2 (OR =1.26, 95% CI: 1.02-1.55), ASCVD/CKD (OR =1.49, 95% CI: 1.01-2.04), history of diabetic coma (OR =12.97, 95% CI: 1.89-88.99) and presence of disability (OR =1.40, 95% CI: 1.14-1.73). In T2DM, the type of antidiabetic therapy (ADT) prior to COVID -19 (last visit before the development of infection) had a significant impact: Insulin therapy (OR = 1.64, 95% CI: 1.30-2.07), sulfonylureas (SU) (OR =1.51, 95% CI: 1.23-1.84));dipeptidyl peptidase-4 inhibitor (iDPP-4) therapy (OR =0.57, 95% CI: 0.39-0.83) and sodium-glucose cotransporter-2 inhibitor (iSGLT2) therapy (OR =0.64, 95% CI: 0.46-0.88). Vaccination was the most important protective factor in both types of DM: DM1 OR =0.19, 95% CI: 0.06-0.59;SD2 OR =0.20, 95% CI: 0.16-0.26. CONCLUSION(S): The common risk factor for fatal outcome in both DM1 and DM2 was age >=65 years;in DM1 - history of hypertension and DFS, in DM2 - male sex, diabetes duration >=10 years, BMI >=30 kg/m2, history of ASCVD/CKD and diabetic coma, disability. In T2DM, significant differences in risk were observed depending on the type of ADT: insulin and SU therapy were factors that increased the risk of death, whereas therapy with iDPP-4 and iSGLT2 reduced the risk of death. Vaccination reduced the risk of death in DM1 and DM2 by 5.2 and 5-fold, respectively.Copyright © Endocrinology Research Centre, 2022.
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BackgroundSince the announcement of COVID-19 as a pandemic infection, several studies have been performed to discuss the clinical picture, laboratory finding, and imaging features of this disease. The aim of this study is to demarcate the imaging features of novel coronavirus infected pneumonia (NCIP) in different age groups and outline the relation between radiological aspect, including CT severity, and clinical aspect, including age, oxygen saturation, and fatal outcome. We implemented a prospective observational study enrolled 299 laboratory-confirmed COVID-19 patients (169 males and 130 females;age range = 2–91 years;mean age = 38.4 ± 17.2). All patients were submitted to chest CT with multi-planar reconstruction. The imaging features of NCIP in different age groups were described. The relations between CT severity and age, oxygen saturation, and fatal outcome were evaluated.ResultsThe most predominant CT features were bilateral (75.4%), posterior (66.3%), pleural-based (93.5%), lower lobe involvement (89.8%), and ground-glass opacity (94.7%). ROC curve analysis revealed that the optimal cutoff age that was highly exposed to moderate and severe stages of NCIP was 38 years old (AUC = 0.77, p < 0.001). NCIP was noted in 42.6% below 40-year-old age group compared to 84% above 40-year-old age group. The CT severity was significantly related to age and fatal outcome (p < 0.001). Anterior, centrilobular, hilar, apical, and middle lobe involvements had a significant relation to below 90% oxygen saturation. A significant negative correlation was found between CT severity and oxygen saturation (r = − 0.49, p < 0.001). Crazy-paving pattern, anterior aspect, hilar, centrilobular involvement, and moderate and severe stages had a statistically significant relation to higher mortality.ConclusionThe current study confirmed the value of CT as a prognostic predictor in NCIP through demonstration of the strong relation between CT severity and age, oxygen saturation, and the fatal outcome. In the era of COVID-19 pandemic, this study is considered to be an extension to other studies discussing chest CT features of COVID-19 in different age groups with demarcation of the relation of chest CT severity to different pattern and distribution of NCIP, age, oxygen saturation, and mortality rate.
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This report shows the epidemiological situation of COVID-19 in the long-term care facilities (LTCF) for elderly throughout 2021 and until the 17th week of 2022. Since its start-up in January 2021, this surveillance has provided aggregated national weekly data, with information on the proportion of LTCF for elderly affected by COVID-19, the incidence of COVID-19 and fatal cases among a vulnerable population where the effects of the pandemic have been and are being especially severe. The comparison with the incidence and mortality of COVID-19 cases in noninstitutionalized elderly shows how the impact of COVID-19 is being greater among the elderly living in nursing homes, with more fatal cases than the rest of the population of the same age.
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BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear. METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed. RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593. CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.
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Background and purpose: There are limited studies on co-infection of COVID-19 and tuberculosis (TB). This study aimed to describe the clinical, radiological, laboratory characteristics, treatment and outcome of patients admitted with tuberculosis and COVID 19 co-infection. Materials and methods: In this retrospective study, we investigated all patients with either active TB or old TB and COVID-19 admitted to Qaemshahr Razi Teaching Hospital between 2020 and 2022. Results: A total of 9251 patients with COVID-19 were admitted to our hospital between February 2020 and May 2022. There were eight patients with pulmonary tuberculosis and COVID-19 co-infection, including five (62.5%) male patients. The mean age of these patients was 61.13..22.63 years old. The mean time of symptom onset to hospital admission was 15.13..30.56 days and 50% were diagnosed with active TB and other half had old TB. Four patients were admitted to the ICU, three of whom required ventilation. Finally, four (50%) patients deceased. In this study, among factors that influence patients' outcomes, only underlying diseases were significantly associated with death. Conclusion: Tuberculosis is assumed to cause a higher mortality risk in COVID-19 patients, especially in those with chronic underlying diseases.
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BACKGROUND: The SARS-nCoV-2019 epidemic has spread since December 2019, quickly gaining worldwide attention. Symptoms consist of fever, cough and breathing difficulties. An increasing number of studies are focusing on neurological manifestations. In addition to the typical ageusia and anosmia, up to 30% of cases can present headache, nausea and vomiting. More serious neurological manifestations, such as encephalitis, thrombosis and cerebral haemorrhage have been reported. CASE DESCRIPTION: We described the case of a 47-year-old man who tested positive for COVID-19 virus in early March 2020. After two negative nasopharyngeal swabs, 41 days after the diagnosis of COVID-19 infection, he developed intense headache with fever, and he was hospitalized. He had subsequent generalized epileptic seizures and intubation was necessary. Contrast Head MRI was negative for brain abscesses or tumours but detected severe vasogenic oedema of the white matter with 10 mm shift of the midline and compression of the right lateral ventricle. Massive cortisone support therapy was ineffective. We diagnosed brain death on day 43 from the infection diagnosis. DISCUSSION: COVID-19 virus can reach the brain, penetrating into the neuronal cells through the interaction between the spike protein S1 and the host ACE-2 receptor, expressed in the capillary endothelium. We believe that in this infection, the pro-inflammatory state induced by the cytokine storm can cause a cerebral cell-mediated response, with subsequent vasodilatation and brain oedema. CONCLUSION: To our knowledge, this is the first description of a delayed onset cell-mediated encephalitis caused by COVID-19 virus after more than 40 days from the diagnosis.
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OBJECTIVE: The present study examines the impact of the COVID-19 stay-at-home order on gun violence in Buffalo, New York: fatal shootings, all non-fatal shootings, non-fatal shootings with injury, and non-fatal shootings without injury. It also estimated its impact on gang and non-gang related shootings. METHODS: Weekly crime data are analyzed at the city level using ARIMA and poisson models. Forecasting is used to verify the validity of both ARIMA and poisson models. RESULTS: The effect of the pandemic was conditional upon the types of gun violence and impact models of intervention. The pandemic caused a temporary increase in fatal shootings while leading to a long-term increase in all non-fatal shootings, non-fatal shootings with injury, non-fatal shootings without injury, and gang related shootings. CONCLUSIONS: The pandemic has changed the volume of gun violence possibly due to increased strain and/or changed routine activities. This study not only promotes further research but also has policy implications for public health and safety. From a public policy perspective, criminal justice agencies should focus more attention and resources on gun violence resulting from a sense of strain and fear among individuals during the pandemic.
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OBJECTIVES: Omicron lineages BA.1/2 are considered to cause mild clinical courses. Nevertheless, fatal cases after those infections are recognized but little is known about risk factors. METHODS: A total of 23 full and three partial autopsies in deceased with known Omicron BA.1/2 infections have been consecutively performed. The investigations included histology, blood analyses, and molecular virus detection. RESULTS: COVID-19-associated diffuse alveolar damage was found in only eight cases (31%). This rate is significantly lower compared with previous studies, including non-Omicron variants, where rates between 69% and 92% were observed. Neither vaccination nor known risk factors were significantly associated with a direct cause of death by COVID-19. Only those patients who were admitted to the clinic because of COVID-19 but not for other reasons had a significant association with a direct COVID-19 -caused death (P >0.001). CONCLUSION: Diffuse alveolar damage still occurred in the Omicron BA.1/BA.2 era but at a considerably lower frequency than seen with previous variants of concern. None of the known risk factors discriminated the cases with COVID-19-caused death from those that died because of a different disease. Therefore, the host's genomics might play a key role in this regard. Further studies should elucidate the existence of such a genomic risk factor.
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COVID-19 , Humans , Autopsy , Research Design , Ambulatory Care Facilities , GenomicsABSTRACT
OBJECTIVE: To determine the incidence of MIS-C in Brazil, describe the clinical and sociodemographic characteristics of the pediatric population affected by MIS-C and compare mortality and lethality outcomes with isolated Covid-19 and MIS-C cases. METHODS: Observational and retrospective cohort study of cases of MIS-C associated with Covid-19 in the Brazilian population between 04/01/2020 and 04/17/2021. Data from the Ministry of Health's epidemiological bulletin up to the 15th epidemiological week of 2021, were used. The analyzes were descriptive through absolute and relative frequencies. The significance level is 5% in Stata 16.0 package. RESULTS: Between 04/01/2020 and 04/07/2021, 903 cases of MIS-C associated with Covid-19 were notified in Brazil, of which, the largest part (55.26%) were male, between 0 and 4 years old (45.29%), from the Southeast region (38.76%). The deaths (61; 6.7%) were higher in the female gender, between 0 and 4 years old (47.54%) and in the Southeast region (34.43%). It was identified that the risk of death by MIS-C related to Covid-19 is 5.29 (CI = 2.83; 9.87 and P-value = <0.001) times higher in adolescents from 15-19 years old than in other age groups when compared to 0-4 years old children. Also, the residency in North region was as risk factor to death (RR = 3.72, IC = 1.29; 10.74 e P-value = 0.008). CONCLUSION: In this study, despite the numbers showing more deaths from zero to 4 years old, the risk for teenagers is notably higher. In addition, Brazil's Northern region is a risk factor that reaffirms social inequality and poor access to health.
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BACKGROUND: The coronavirus pandemic has had an extremely negative impact on the patients with diabetes mellitus (DM both in terms of a more severe course of COVID -19 and an increased risk of death. AIM: Analysis of risk factors for death due to COVID -19 in patients with DM type 1 and type 2 (DM1 and DM2). MATERIALS AND METHODS: Retrospective analysis of the database of the national diabetes register (NDR), which included DM patients with COVID-19 and reported virus infection outcome (recovery/or death) in 15 712 DM1 and 322 279 DM2 patients during a 2-year follow-up period (01/02/2020 to 03/04/2022) (discharge date)). RESULT(S): Case fatality rate in patients with DM, who underwent COVID -19 was 17.1% (DM1-8.8%;DM2-17.5%). As a result of multivariate regression analysis of seven significant factors in DM1 and thirteen in DM2 (evaluated by univariate anlisys), a number of the most important predictors of risk for fatal outcome were identified: in DM1 these were age >=65 years (OR =4.01, 95% CI: 1.42-11.36), presence of arterial hypertension (AH) (OR =2.72, 95% CI: 1.03 -7.16) and diabetic foot syndrome (DFS) (OR = 7.22, 95% CI: 1.98-26.29);for T2DM: age >= 65 years (OR =2.53, 95% CI: 1.96-3.27), male (OR =1.51, 95% CI: 1.23-1.84), duration DM >=10 years (OR =2.01, 95% CI: 1.61-2.51), BMI >= 30 kg/m2 (OR =1.26, 95% CI: 1.02-1.55), ASCVD/CKD (OR =1.49, 95% CI: 1.01-2.04), history of diabetic coma (OR =12.97, 95% CI: 1.89-88.99) and presence of disability (OR =1.40, 95% CI: 1.14-1.73). In T2DM, the type of antidiabetic therapy (ADT) prior to COVID -19 (last visit before the development of infection) had a significant impact: Insulin therapy (OR = 1.64, 95% CI: 1.30-2.07), sulfonylureas (SU) (OR =1.51, 95% CI: 1.23-1.84));dipeptidyl peptidase-4 inhibitor (iDPP-4) therapy (OR =0.57, 95% CI: 0.39-0.83) and sodium-glucose cotransporter-2 inhibitor (iSGLT2) therapy (OR =0.64, 95% CI: 0.46-0.88). Vaccination was the most important protective factor in both types of DM: DM1 OR =0.19, 95% CI: 0.06-0.59;SD2 OR =0.20, 95% CI: 0.16-0.26. CONCLUSION(S): The common risk factor for fatal outcome in both DM1 and DM2 was age >=65 years;in DM1 - history of hypertension and DFS, in DM2 - male sex, diabetes duration >=10 years, BMI >=30 kg/m2, history of ASCVD/CKD and diabetic coma, disability. In T2DM, significant differences in risk were observed depending on the type of ADT: insulin and SU therapy were factors that increased the risk of death, whereas therapy with iDPP-4 and iSGLT2 reduced the risk of death. Vaccination reduced the risk of death in DM1 and DM2 by 5.2 and 5-fold, respectively. Copyright © Endocrinology Research Centre, 2022.
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Objective:In this study, to evaluate the clinical course and prognosis in COVID-19 patients, to evaluate the hematological and biochemical parameters at the time of admission to the hospital.