CLINICIANS' PERSPECTIVES ON THE EVOLUTION OF PALLIATIVE CARE DELIVERY DURING THE COVID-19 PANDEMIC

Palliative care has been primarily delivered to patients in person since its inception. During the Covid-19 pandemic, providing palliative care was especially challenging for clinicians due to public health measures to contain the virus that required them to interact virtually with patients and families. While the rapid implementation of telehealth has been examined in other clinical contexts, limited research has studied the impact of the pandemic on palliative care delivery. This study examined the experiences clinicians faced when providing palliative care to older adult patients during the pandemic. Between April 2021 and March 2022, we interviewed 29 geriatricians and palliative care specialists from 11 institutions across the US. We asked clinicians about their experiences with palliative care during the pandemic, including challenges and opportunities related to the changing nature of palliative care delivery. We analyzed interviews using reflexive thematic analysis. The following three themes emerged from the analysis: (1) Clinicians' challenges adjusting to virtual care;(2) System-level barriers, restrictions, and uncertainties about Covid-19;and (3) Older adult patients' context and vulnerability (i.e., loss of social engagement, isolation, loneliness, delayed access to care) that increased the complexity of their health conditions. In conclusion, clinicians' experiences during the pandemic shed light on the evolution of palliative care delivery and the importance of preparing them for new care models that account for virtual delivery and that address the diverse needs of older adults that emerge during public health crises.

MAIN BLOOD SERUM METABOLITES RESPONSIBLE FOR THE DISCRIMINATION OF HOSPITALIZED PATIENTS WITH MODERATE SYMPTOMS WITH OR WITHOUT COVID-19

Introduction: The variation in human blood serum metabolites resulting from an infection can assist in understanding mechanisms of pathogen action and body response and improve diagnosis. Aim: To map serum signatures of hospitalized symptomatic patients, positive or negative to SARS-CoV-2. Methods: Patients (n = 64) admitted to Anhembi Field Municipal Hospital, a hospital set up for initial care to patients with moderate symptoms, were analyzed being discriminated in positive (n = 32) or negative patients. Age and gender were matched to ensure homogeneity in the basal metabolic rates. Three Nuclear Magnetic Resonance (NMR) data set were recorded on Bruker AVANCE III 600 MHz spectrometer for serum samples analyzed in MetaboAnalyst 5.0 software platform. Results and discussion: The mean age of groups was 54.92 ±12.41 and 54.30 ±12.15, for positive and negative patients, divided in 16 female and 16 male. The ethnicity was 56.2% vs 46.8% caucasian, 34.3% mixed race in both groups, and 9.3% 12.5% vs black in positive and negative groups, respectively. BMI was 24 ±6.93 vs 33.5 ±7.85 in comparison to positive and negative patients, respectively. In both groups 50% of patients presented alveolar infiltrate. Although the groups were not paired by comorbidities, they were homogeneous ensuring that the metabolic variation is due to COVID-19 as similar percentage of patients with arterial hypertension, diabetes and dyslipidemia. Clinical symptoms were also remarkably similar between the groups in relation to: fever, dry cough, dyspnea and myalgia. The Partial Least Squares - Discriminant Analysis (PLS-DA) performed onto noesy1d data discriminated positively from negative patients. Also, it covered lower variance. Combining NMR techniques, it was possible to depict the main metabolites that distinguished the COVID-19 signatures. Alanine, glucose, cholesterol, and glutamine were increased, and lactate decreased in COVID-19. Conclusion: These results suggest NMR as an excellent tool to differentiate hospitalized patients with moderate symptoms as COVID-19 positive or negative. The Ethics Research Committee of the University of Campinas approved all of the experimental procedures, and all individuals signed the informed consent form.

HEMOSTASTATIC ALTERATIONS WITH DISEASE SEVERITY IN THE EARLY SYMPTOMATIC PHASE OF COVID-19

Introduction: The main factors associated with disease severity in Covid-19 are age, sex, body weight, hypertension, and diabetes. Biomarkers of hemostatic activation have been shown to be independent predictors of disease severity in different populations. Aim: To evaluate whether biomarkers of hemostatic activation were associated with clinical outcomes in patients admitted to a field hospital set up to provide initial care to patients in the early symptomatic phase of Covid-19. Methods: Data and samples were obtained from June to September 2020. Laboratory evaluation included complete blood counts, PT, aPTT, fibrinogen, D-dimer, factor VIII activity, Von Willebrand Factor (VWF) (activity and antigen), C reactive protein (CRP) and P-selectin (ELISA). Patients were segregated by outcome, with clinical worsening defined as need for ICU, mechanical ventilation, pulmonary embolism, deep vein thrombosis or death. Results and discussion: In total 209 were enrolled in the study, of which 24 presented clinical deterioration (11.5%). In both groups there was more male patients. In the group of clinical worsening the mean age was 58.1 and improvement was 53.6 years old. Concerning smoking, 3.2% of patients that improved smoke. Regarding pulmonar infiltrate, it was verified in 50% in the group that worsening versus 41% in clinical improvement. No differences could be observed between patient subgroups regarding the presence of fever (63.2% vs. 62.5%), dry cough (75.1% vs. 87.5%) and dyspnea (65.9% vs. 54.2%) at admission. As main comorbidities, the groups presented chronic obstructive pulmonary disease (2.2% vs 8.3%), asthma (3.2% vs 4.2%), chronic heart failure (1.1% vs 8.3%), arterial hypertension (46% vs 41.7%) and diabetes (28.1% vs 33.3%) in comparing improved with clinical deterioration patients. In general, it was verified a significant decrease in platelet number (p = 0.0426), and an increase in the parameters of aPTT (0.0084), CRP (p = 0.0450), vWF antigen (p = 0.0022) and ristocetin cofactor (p = 0.0032). Conclusion: Our results demonstrate that hemostasis activation is associated with clinical deterioration even at the early phases of Covid-19. The Ethics Research Committee of the University of Campinas approved all of the experimental procedures, and all individuals signed the informed consent form.

Artificial neural networks to predict COVID-19 progression of moderate hospitalized patients using early clinicalparameters and blood tests

Background : The early prediction of Covid-19 progression could improve patient's treatment. It is important to develop mathematical models to perform this task using simple blood tests. Aims : To obtain a neural network (ANN) to predict the progression (death vs discharge and intubation vs discharge) of Covid-19 in patients with confirmed diagnosis. Methods : The patients included in this work were diagnosed with Covid-19 by RT-PCR. All data were collected from hospitalized patients admitted to Anhembi Field Municipal Hospital (São Paulo-Brazil), a hospital set up for initial care to patients with moderate symptoms during the pandemic, between June/2020 and October/2020. Blood was collected at the patient's admission. The inputs considered were: sex, age, ethinicity, body mass index, tabagism, ex-tabagism, alveolar infiltrate, arterial hypertension, diabetes, heart rate, respiration rate, body temperature, oxygen saturation, D-dimer, activated partial thromboplastin time, prothrombin time, levels of: hemoglobin, platelet, leukocytes, lymphocytes, monocytes, neutrophils, lactate dehydrogenase, C-reactive protein, and creatinine. Two ANNs were proposed, as shown at Table 1. The best ANN was defined by a 5-fold cross-validation scheme. Finally, a test step was performed to verify the ANN performance. ANNs with one and two hidden layers were tested. The number of neurons ranged from 5 to 35. Results : The main results are shown at Table 2. The best models were obtained with different ANN's structures, which show the influence of the different outcome. The models presented high ACC, AUC, PPV, NPV, and TNR. The ANN 2 presented better performance than ANN 1. We believe that this may be due the data homogeneity that rises from the inclusion criteria adopted in the study. Conclusions : The results showed that the ANNs could be used to predict the progression of Covid-19 based on simple blood tests. The models could be used in the future after an external validation with high number of patients.

Combining artificial neural networks and blood tests to diagnose COVID-19 infection

Background : Fast and accurate diagnosis of COVID-19 is important to prevent dissemination and disease progression. Artificial Intelligence is known as a universal fitting tool and can be used on the formulation of predictive models for the disease's diagnosis. Aims : Obtain a neural network (ANN) to diagnose patients as positive or negative COVID-19 based on patient data and blood tests. Methods : Data from 678 patients with moderate symptoms from the Anhembi Field Municipal Hospital (São Paulo-Brazil), followed between June/2020 and October/2020 were used. Covid-19 by RTPCR was confirmed in 460 patients. The inputs considered were: sex, age, ethinicity, body mass index, tabagism, ex-tabagism, alveolar infiltrate, arterial hypertension, diabetes, heart rate, respiration rate, body temperature, oxygen saturation, D-dimer, activated partial thromboplastin time, prothrombin time, levels of: hemoglobin, platelet, leukocytes, lymphocytes, monocytes, neutrophils, lactate dehydrogenase, C-reactive protein, and creatinine. Blood was collected at the patient's admission. The ANNs had 25 inputs and the output was the Covid-19 diagnosis. The best ANN was defined by a 5-fold cross-validation scheme. Then, a test step was performed to assess the model's performance. ANNs with one and two hidden layers were proposed. The number of neurons ranged from 5 to 35. Results : The best result was obtained with an ANN containing 25 and 30 neurons in the first and second hidden layers, respectively. All the statistical parameters found for the best model are shown at Table 1. The model presented accuracy of 83.3 %, high capacity for the prediction of true positives (PPV = 0.917 and LR+ = 5.188), and moderate probability to indicate false negatives (LR-= 0.202). Conclusions : The results showed that the ANNs are promising to diagnose Covid-19 based on clinical parameters and blood tests. After future refinements and proper validation, this model could be used to diagnose Covid-19 on daily basis.

Return to class: Between presential teaching and distance education, new trends

The subject of the article is about face-To-face and distance learning, which will be a challenge for teachers in returning to classes after the coronavirus pandemiciBased on this scenario, the study aims to reflect on the return to classes and possible teaching trends in elementary schools in the municipality of Canoas in the state of Rio Grande do SuliThe methodological approach of this research is qualitative and is characterized as a literature review from books and scientific articles from the last 5 yearsiThe theoretical framework of the research is supported by authors such as Moran, 2015), Förh, 2019), Santana and Sales, 2020), Horn and Staker, 2015)iThe results show that the Covid-19 pandemic accelerated the innovation processes in municipal schools of basic education, modifying the means of teaching and learningiWe understand that in this new paradigm digital education emerges with an emphasis on digital, personalized and collective literacy: hybrid education. © 2021 UNIVERSIDADE FEEVALE . All Rights Reserved.