The Perceptions of Families of Comatose Patients in the Intensive Care Unit: A Qualitative Study

Background: There are specific challenges regarding the perceptions of families of comatose patients in Intensive Care Units (ICUs). Identifying these perceptions may attract the cooperation of families with nurses and provide better care for patients. This study aimed to explore the perceptions of families of comatose patients in ICUs. Methods: This was a qualitative content-analysis study. Seventeen families with comatose patients were recruited by the purposive sampling technique. The necessary data were generated by semi-structured interviews, continued until data saturation, and concurrently analyzed by an inductive content analysis method. Results: Four main categories were manifested, including shock and disbelief, the effort for adaptation, exhaustion, and burnout, as well as hope and support. Conclusion: The obtained results signified the importance of nurses’ awareness concerning the family members’ perceptions of their comatose patient status in ICUs. The relevant findings reflected the need for nurses to pay attention to the feelings and emotions of the families of these patients.

Alerts as Coordination Mechanisms

In this study, we explore how clinical decision support features can be designed to aid teams in caring for patients during time-critical medical emergencies. We interviewed 12 clinicians with experience in leading pediatric trauma resuscitations to elicit design requirements for decision support alerts and how these alerts should be designed for teams with shared leadership. Based on the interview data, we identified three types of decision support alerts: reminders to perform tasks, alerts to changes in patient status, and suggestions for interventions. We also found that clinicians perceived alerts in this setting as coordination mechanisms and that some alert preferences were associated with leader experience levels. From these findings, we contribute three perspectives on how alerts can aid coordination and discuss implications for designing decision support alerts for shared leadership in time-critical medical processes.

Application of Machine Learning in Data Analytics on COVID-19 First Wave in India

As there are several data sets available, this chapter gives insight on which regions of India have been heavily impacted during the first wave of COVID-19 and the classification of patient status using an ML-based data analytics algorithm. The chapter provides a greater insight on the background work and the reports generated based on the analytical results gathered from the data set. In this pandemic situation, such reports will be a great benefit to assess the history of occurrence and the current status of the COVID-19 situation in India.

COVID-19 Detection and Analysis Using Chest X-Ray and CT- Scan Images

COVID-19 seems to be the most devastating and lethal illness characterized by an unique coronavirus for the human body. Coronavirus, which is considered to have originated in Wuhan, China, and is responsible for a huge number of deaths, spread swiftly around the world in December 2019. Early discovery of COVID-19 by proper diagnosis, especially in situations with no evident symptoms, could reduce the death rate of patients. The primary diagnostic tools for this condition are chest Xrays and CT scans. COVID-19 may be detected using a machine vision technique from chest X-ray pictures and CT scans, according to this study.The model's performance was evaluated using generalised data throughout the testing step. According to recent studies gained using radiological imaging techniques, such images convey crucial data about the COVID-19 virus. This proposed approach, which makes use of modern artificial intelligence (AI) techniques, has shown to be effective in recognising COVID-19, and when combined with radiological imaging, can aid in the correct detection of this disease. The proposed approach was created in order to provide accurate assessments for COVID and non-COVID patients.The results demonstrate that VGG-16 is the best architecture for the reference dataset, with 98.87 percent accuracy in network evaluations and 95.91 percent success in patient status identification. Convolutional layers were developed, with distinct filtering applied to each layer. As a result, the VGG-16 design performed well in the classification of COVID-19 cases. Nevertheless, by modifying it or adding a preprocessing step on top of it, this architecture allows for significant gains. Our methodology can be used to help radiologists validate their first screenings and can also be used to screen patients quickly via the cloud.

Analisis Survival dengan Menggunakan Metode Kaplan Meier pada Penderita Penyakit Demam Berdarah Dengue (DBD)

The Kaplan Meier method is one of the methods in survival analysis that is used to find out how big the chances of survival of a person suffering from a certain disease. The Kaplan Meier method calculates the patient's life by providing a definite survival proportion. The purpose of this study was to determine the relationship between age and length of treatment and patient status. The life chances of DHF patients using the Kaplan Meier method at the Lasinrang District Hospital. Pinrang. The results showed that there was no relationship between age and length of treatment as well as the status of DHF patients in Lasinrang Hospital Kab. Pinrang. The conclusion of this study is that the chance of survival of DHF patients before a patient dies is 100%. However, the chance of patient survival after 1 patient died during the observation was 99.1%. It is recommended that clinical monitoring should always be carried out, to predict the correct diagnosis of Dengue Virus Infection. And sero-epidemiological studies should continue to be carried out in many island capitals in Indonesia.

Post CO VID-19 Encephalopathy Treatment

Aim. To evaluate the severity of encephalopathy and the efficacy of the second stage of rehabilitation in patients with post-infectious encephalopathy. Material and methods. The study included 92 patients with post-infectious encephalopathy, who underwent the second stage of rehabilitation after СOVID-19 infection. All patients were divided into 2 groups: those, who were referred to rehabilitation after the end of the treatment (n=54) and those, who refused to undergo rehabilitation (n=38). In all cases the severity of coronavirus infection, percentage of pulmonary involvement (based on chest CT-findings) and the presence of comorbid pathology were assessed. The severity of the prevailing syndromes was assessed using the International Classification of Functioning, Disability and Health (ICF). Patient status dynamics was assessed using Rehabilitation routing scale (RRS), Rivermead, Holden and Hauser scales. Results and discussion. Post-infectious encephalopathy was mainly accompanied by vestibular ataxia syndrome in 51 (55.4%) patients (in 51.9% and 60.5% patients in groups 1 and 2, respectively) and cerebro-asthenic syndrome in 73 (79.3%) patients (in 72.2% and 89.5% patients in groups 1 and 2, respectively). Comprehensive rehabilitation process is characterized by staging aimed at compensating for all components of pathogenesis that affected during the period of severe course of COVID-19 infection. Rehabilitation was an effective measure for the compensation of neurologic complications of COVID-19 infection. After 2 weeks, cerebro-asthenic syndrome was observed in 24.1% patients in group 1 and 71.0% in group 2 (p <0.05), vestibular ataxia syndrome – in 18.5% and 28.9% (p<0.05) of patients in groups 1 and 2, respectively. The current pandemic is highly likely to be accompanied by a significant increase in the prevalence of encephalopathy affecting the ability to return to daily functioning. Conclusion. The main manifestations of post-covid encephalopathy are cerebro-asthenic (79.3%) and vestibular ataxia (55.4%) syndromes. Therapeutic and rehabilitation measures carried out at the 2nd stage of rehabilitation is an effective measure to compensate for the severity of post-covid encephalopathy.

Clinical characteristics of, prognostic factors for, and long-term outcome of dogs with multiple acquired portosystemic shunts: 72 cases (2000–2018)

OBJECTIVE To identify clinical characteristics of, prognostic factors for, and long-term outcome of dogs with multiple acquired portosystemic shunts (MAPSSs) and determine whether survival time was associated with previous portosystemic shunt attenuation. ANIMALS 72 client-owned dogs with MAPSSs. PROCEDURES Medical records of dogs in which MAPSSs had been diagnosed between January 2000 and August 2018 were reviewed for signalment, historic and diagnostic findings, management methods, and outcome. RESULTS Median survival time of dogs (n = 23) that died of causes related to MAPSSs was 580 days (range, 156 to 1,363 days). Factors significantly associated with dying of MAPSS-related versus unrelated causes included body weight, albumin concentration at the first and last recheck examinations, and cholesterol, total solids, and glucose concentrations at the last recheck examination. Dogs not receiving medical management or without signs of depressed mentation at the time of initial presentation were less likely to die of causes related to MAPSSs. Patient status (alive vs dead of causes related to MAPSSs vs dead of causes unrelated to MAPSSs vs dead of unknown causes) was not significantly associated with survival time. CONCLUSIONS AND CLINICAL RELEVANCE Survival time for dogs with MAPSSs was not shortened by previous portosystemic shunt attenuation surgery and was not different when death was versus was not related to MAPSSs. Dogs with MAPSSs that had progression of biochemical changes consistent with liver dysfunction were more likely to die of causes related to MAPSSs and were unlikely to live a normal lifespan.

COVID-19 Outcome Relates With Circulating BDNF, According to Patient Adiposity and Age

Background and Aims: We evaluated adipose tissue-derived hormones, body composition, serum metabolic profile, levels of brain-derived neurotrophic factor (BDNF), and the association of these parameters with the clinical outcome in patients with COVID-19. We sought to examine whether obesity, sex, and age influence the adipose tissue endocrine response to the disease.Methods: This prospective study investigated 145 hospitalized patients with COVID-19. Patients were categorized based on their body mass index (BMI), sex and age, and were also classified regarding their outcome after hospitalization as: (a) Non-ICU: patients hospitalized who did not receive intensive care; (b) ICU-survivor: patients admitted to the intensive care unit and discharged; (c) ICU-death: patients who died. Blood samples were collected by the hospital staff between the first and third day of hospitalization. Serum leptin, adiponectin and BDNF concentrations, triglycerides, total cholesterol and cholesterol fractions were performed following the manufacturer's guidelines.Results: We demonstrate that BDNF levels predict intensive care (IC) need (p &lt; 0.01). This association was found to be stronger in patients &gt;60y (p = 0.026). Neither leptin nor adiponectin concentration was associated with IC requirement or with patient's outcome, while the BDNF/adiponectin ratio was closely associated with worsened outcomes (p &lt; 0.01). BDNF concentration was similar between sexes, however tended to be lower in male patients (p = 0.023). In older patients, BDNF concentration was lower than that of younger patients (p = 0.020). These age and sex-specific differences should be considered when employing these potential markers for prognosis assessment. While appetite and body composition regulating hormones secreted by the white adipose tissue are not reliable predictors of disease severity, the ratio BDNF/adiponectin was indicative of patient status.Conclusion: Thus, we propose that serum BDNF content and BDNF/adiponectin ratio may serve as tools predicting worsened prognosis in COVID-19, especially for male patients.

Non-Invasive Myocardial Infarction Ischemic Area Estimation Through Four-Dimensional Ultrasound

Background/Objective: Following myocardial infarction, infarct size and cardiac function are significant predictors of long-term prognosis. Most echocardiography studies rely on two-dimensional analysis for estimation of left ventricular function and electrical activity analysis for estimation of infarct area. Other imaging modalities, such as cardiac magnetic resonance imaging, are limited by time, cost, availability, patient tolerance, and incompatible implantable devices. Using an experimental mouse model of myocardial infarction, we hypothesize that four-dimensional ultrasound offers a possible alternative for easy, quick, and reliable estimation of infarct size. Methods: A cohort of 10 mice underwent four-dimensional cardiac imaging at baseline using a small animal high frequency ultrasound. A thoracotomy was subsequently performed, and a suture placed to ligate the left coronary artery approximately midway down the left ventricle. Sequential four-dimensional ultrasound was performed at six time points over 28 days, following which the mice were euthanized. The hearts were then removed and sent for embedding and sectioning into seven uniform segments stained using both H&E and Masson’ s Trichrome. Results: Thus far, we have segmented the imaging and collected end diastolic volume, peak systolic volume, stroke volume, ejection fraction, transmural thickness, and circumferential strain. Additionally, four-dimensional models of the left ventricles have been rendered. Histological embedding, sectioning, and staining is still in progress, and therefore validation against the gold standard is still in process. Conclusion and Impact: Treatment and monitoring of myocardial infarction patients is reliant upon accurate assessment of patient status and prognosis. This study provides initial evidence for the validity of four-dimensional ultrasound as a tool for estimation of myocardial infarction size, providing an alternative to current two-dimensional methods that are less accurate and a more accessible alternative to highly specialized and costly equipment. Improved and accessible imaging methods have the potential to enhance patient care, ultimately improving overall health outcomes.

SARS-CoV-2 genetic variations associated with COVID-19 pathogenicity

In this study, we performed genome-wide association analyses on SARS-CoV-2 genomes to identify genetic mutations associated with pre-symptomatic/asymptomatic COVID-19 cases. Various potential covariates and confounding factors of COVID-19 severity, including patient age, gender and country, as well as virus phylogenetic relatedness were adjusted for. In total, 3021 full-length genomes of SARS-CoV-2 generated from original clinical samples and whose patient status could be determined conclusively as either ‘pre-symptomatic/asymptomatic’ or ‘symptomatic’ were retrieved from the GISAID database. We found that the mutation 11 083G>T, located in the coding region of non-structural protein 6, is significantly associated with asymptomatic COVID-19. Patient age is positively correlated with symptomatic infection, while gender is not significantly correlated with the development of the disease. We also found that the effects of the mutation, patient age and gender do not vary significantly among countries, although each country appears to have varying baseline chances of COVID-19 symptom development.