Personalized approach to the management of a patient with COVID-19 with a complicated comorbid background

The aim of the work is to form the principles of a personalized approach to the management of patients with COVID-19 with a complicated comorbid background. Material and methods. The article describes a clinical case of successful recovery of an 87-year-old patient from a new coronavirus infection COVID-19, complicated by pneumonia involving 36% of the lung parenchyma area. Along with age, the situation was aggravated by the comorbid status of the patient: the presence of chronic lymphocytic leukemia, hypertension, mechanical prostheses of the mitral and aortic valves, postinfarction cardiosclerosis, paroxysmal atrial fibrillation, type 2 diabetes mellitus, stage 4 CKD, anemic syndrome, and subclinical hypothyroidism. Results. The C-reactive protein level at admission was 114.46 mg/L. The patient refused hospitalization. Baricitinib 4 mg, favipiravir according to the scheme, vitamin D 2000 units were prescribed for the previously taken therapy. Already after 3 days, C-reactive protein decreased by 4.6 times, and by the 8th day by 15.5 times and amounted to 7.38 mg/ml. The temperature returned to normal on day 2 from the start of baricitinib. In dynamics, a decrease in creatinine level to 177.0 mumol/l was noted, the glomerular filtration rate increased to 30 ml/min/1.73 m2, which corresponded to stage 3b of CKD (a pronounced decrease in glomerular filtration rate). Conclusion. Despite the age of the patient, many comorbidities, each of which could be fatal, the timely use of baricitinib on an outpatient basis made it possible to stop the progressive course of the disease.Copyright © Eco-Vector, 2023. All rights reserved.

Personalized approach to the management of a patient with COVID-19 with a complicated comorbid background.

The aim of the work is to form the principles of a personalized approach to the management of patients with COVID-19 with a complicated comorbid background. Material and methods. The article describes a clinical case of successful recovery of an 87-year-old patient from a new coronavirus infection COVID-19, complicated by pneumonia involving 36% of the lung parenchyma area. Along with age, the situation was aggravated by the comorbid status of the patient: the presence of chronic lymphocytic leukemia, hypertension, mechanical prostheses of the mitral and aortic valves, postinfarction cardiosclerosis, paroxysmal atrial fibrillation, type 2 diabetes mellitus, stage 4 CKD, anemic syndrome, and subclinical hypothyroidism. Results. The C-reactive protein level at admission was 114.46 mg/L. The patient refused hospitalization. Baricitinib 4 mg, favipiravir according to the scheme, vitamin D 2000 units were prescribed for the previously taken therapy. Already after 3 days, C-reactive protein decreased by 4.6 times, and by the 8th day by 15.5 times and amounted to 7.38 mg/ml. The temperature returned to normal on day 2 from the start of baricitinib. In dynamics, a decrease in creatinine level to 177.0 mumol/l was noted, the glomerular filtration rate increased to 30 ml/min/1.73 m2, which corresponded to stage 3b of CKD (a pronounced decrease in glomerular filtration rate). Conclusion. Despite the age of the patient, many comorbidities, each of which could be fatal, the timely use of baricitinib on an outpatient basis made it possible to stop the progressive course of the disease.Copyright © Eco-Vector, 2023. All rights reserved.

A Deep Learning Model for Segmentation of COVID-19 Infections Using CT scans

Computed tomography is critical in diagnosing and assessing COVID-19 infection. Coronavirus (COVID-19) spread around the world in 2020, leaving the world facing an acute health crisis. The automatic deletion of lung infection on computed tomography scan (CT) images offers great potential for improving traditional healthcare strategies for treating COVID-19. However, the detection of lesions via CT imaging faces many challenges, including high contrast in infection characteristics and low contrast intensity between infection and normal tissues. Early diagnosis is an effective way to treat this condition. Then offered a deep learning pipeline consists of three different deep learning structures for generating and segmenting computed tomography of lungs and COVID-19 infection. In addition to this image pre-processing, image magnification and parameter correction based on the color model and model similarity were used to improve the diagnostic process (medium and strong infection areas). © 2022 American Institute of Physics Inc.. All rights reserved.

Effect of the COVID-19 pandemic on ICU ischemic and hemorrhagic stroke outcomes

Objective: Evaluate impact of COVID-19 pandemic on mortality and care limitations in critically-ill stroke patients. Background: COVID-19 pandemic overwhelmed medical systems leading to resource shortages in many regions, which may impact care limitations and mortality in non-COVID patients. This is of particular concern in severe stroke population where perceived poor prognosis can lead to early care limitations and the self-fulfilling prophecy of worse outcomes. Design/Methods: During first 3 months of COVID-19 pandemic (03/28/30-06/28/20) we prospectively enrolled consecutive adults with acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) meeting pre-pandemic criterial for intensive care unit (ICU) admission at single comprehensive stroke center, systematically recorded COVID-19 status, pre-existing code status, disease severity, transition to do-notresuscitate (DNR), do-not-intubate (DNI), and comfort measures (CMO) code status and inhospital mortality. Results were compared with a 3-months retrospective cohort from prior to global COVID-19 pandemic (10/1/19-12/31/19). Results: Pandemic cohort (N=196, mean age 63, 48% female, 60% AIS, 26% ICH, 14% SAH, 22% COVID-19 person-under-investigation) and pre-pandemic cohort (N=199, mean age 63, 46% female, 58% AIS, 26% ICH, 16% SAH) were similar. Our hospital did not experience resource shortages during peak pandemic. Compared with the pandemic cohort, pre-pandemic cohort had similar stroke severity scores but more pre-existing care limitations at admission (90% vs. 98% full code, p=0.005), more frequent transition to DNR (13% vs. 5%, p=0.0025), DNI (10% vs. 3%, p=0.0078), and higher in-hospital mortality (21% vs. 9%, p=0.0012). Conclusions: COVID-19 pandemic was associated with lower incidence of care limitations and in-hospital mortality in severe stroke patients at a stroke center that did not experience resource shortages. Further studies are needed to determine whether these results are due to in-person family visit restrictions during the pandemic. Multicenter studies are needed to determine whether these observations hold true in centers impacted by resource shortages.

Neurologic manifestations associated with COVID-19 in hospitalized patients

Objective: To identify major phenotypes of neurologic manifestations and their prevalence in hospitalized patients infected with severe acute respiratory syndrome corona virus disease 2019 (COVID-19). Background: Emerging evidence suggests COVID-19 presentation is not limited to the respiratory system but may have multi-organ involvement including dysfunctions of the nervous system. However, little is known about the major phenotypes, prevalence, and impact of nervous system involvement on patient outcomes. Design/Methods: We are the coordinating center and part of the GCS-Neuro COVID consortium tier 1 pragmatic study. We prospectively screened 127 consecutive patients admitted to a large academic hospital from 03/22/2020 to 09/05/2020. Adults age ≥ 18 years old admitted to the hospital with suspected or confirmed COVID19 infection were included. Eight patients met exclusion criteria of severe pre-existing baseline neurologic dysfunction such as coma or vegetative state that limit detection of new or worsening neurologic symptoms. Results: Of the total cohort of 119 patients (mean age 63.4 years, 48% women), 73 (61.3%) exhibited new/worsening neurologic symptoms. The most common phenotype was acute encephalopathy (44%), followed by headache (40%), abnormal smell/taste (23%), and new movement abnormalities (21%). Other neurologic manifestations included clinical or electrographic seizures (10%), coma (4%), and intracerebral hemorrhage (3%). Neurologic symptoms began an average of 6.2 days after respiratory symptoms (range 0 to 48), although 2 patients developed neurologic symptoms before respiratory symptoms. COVID-19 patients with neurologic symptoms were less likely to have a favorable outcome at discharge (24.6%) with mRS (0-1) compared to those without neurological symptoms (61.9%). Conclusions: Neurologic manifestations in patients infected with COVID-19 are prevalent and have significant impact on patient outcomes at acute hospital discharge in this single-center study. Further studies are underway to better characterize neurologic symptoms as well as follow-up to determine the long-term impact of COVID-19 on patient outcome and recovery.

Acute hospital outcomes COVID-19 associated neurological dysfunction

Objective: Determine outcomes of hospitalized adult patients with severe acute respiratory syndrome coronavirus disease-2019 (COVID-19) and neurological dysfunction. Background: Emerging data suggest a wide range of neurological symptoms associated with COVID-19. Outcomes of patients with neurological dysfunctions and COVID-19 is unknown. Design/Methods: A prospective cohort of 127 consecutive adult (age≥18) patients admitted with suspected or confirmed COVID-19 infection to single academic hospital from 30/22/2020-09/05/2020 were included. We are part of the GCS-NeuroCOVID consortium. Eight patients met exclusion criteria of severe pre-existing baseline neurologic dysfunction that limit detection of new or worsening symptoms. Neurological dysfunctions were systematically recorded. Global outcome is measured by modified Rankin Score (mRS) at hospital discharge. Between-group differences were compared using parametric or non-parametric test based on data distribution. Results: The final cohort consisted of 119 COVID-19 subjects where 73 (61%) had new neurological dysfunction. Patients with neurological dysfunctions had similar mean age (63.4 vs. 59.3 years) and gender distribution (52% vs. 50% male) compared to those without neurological dysfunction. Presence of neurological dysfunction is associated with need for mechanical ventilation (39.7% vs. 10.9%, p=.0007), longer median ICU (4 vs. 0 days, p=.0004) and hospital lengths of stay (12.5 vs. 6 days, p=.0007), worse functional outcome at discharge (mRS 3 vs. 1, p=.002) and non-home discharge destination (43% vs. 70%, p=.002). Neurological symptoms may be associated with higher incidence of do-not-resuscitate code status (27% vs. 13%, p=0.058) but did not impact in-hospital mortality (17.8% vs. 8.7%, p=0.19). Conclusions: COVID-19 patients with new or worsened neurological dysfunction are more likely to require mechanical ventilation, had longer ICU and hospital length of stay, and worse global functional outcome at discharge. Relatively low mortality rate makes this study underpowered to detect a between-group mortality difference. Future studies are needed to determine long-term outcome impacts of neurological dysfunction associated with COVID-19.

Segmentation and Measurement of Lung Pathological Changes for COVID-19 Diagnosis Based on Computed Tomography

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19. © 2021. All Rights Reserved.