Effects of prone position in acute respiratory distress syndrome secondary to COVID-19: experience in a high complexity hospital

Introduction: The use of protective mechanical ventilation and prone position was recommended for the management of moderate to severe acute respiratory distress syndrome (ARDS) due to COVID-19, as a result of its reported utility on oxygenation and mortality. Our objective is to describe gasometric and mechanical behavior in subjects with ARDS due to COVID-19 managed with protective mechanical ventilation and prone position in a high complexity hospital. Method: Observational study. Subjects ≥18 years of age with ARDS due to COVID-19 were included. Protective mechanical ventilation was started from the first connection to invasive ventilation, while the prone position started with PaO2/FIO2 150. Follow-up was performed during and after the prone position. A descriptive analysis of baseline characteristics and comparison of means between groups was performed using the Dunn and Friedman test. Statistical significance corresponds to p 0.05 in all analyses. Results: 74 subjects were studied, 58% correspond to men with a mean age of 60 years. There is evidence of a significant increase in arterial oxygenation assessed by PaO2 (76 to 98 mmHg, p 0.05) and PaO2/FIO2 (100 to 161, p 0.05) during the first hour of treatment, with stability of values beyond 48 hours after supination. Pulmonary mechanics values remain constant within the established protection range (p = 0,18). Conclusion: The strategy of protective mechanical ventilation and prone position for 48 or more hours, in subjects with moderate to severe ARDS due to COVID-19, improves and maintains arterial oxygenation up to 48 hours after supination. © 2023

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

Gastrointestinal features of the multisystem inflammatory syndrome in children (MIS-C) following COVID-19 infection in selected referral hospitals in Iran

Background and purpose: Multisystem Inflammatory Syndrome in Children (MIS-C) occurs after having COVID-19. The severity and outcomes of COVID-19 with gastrointestinal symptoms are higher. The aim of this study was to investigate gastrointestinal manifestations in MIS-C patients in selected referral hospitals in Iran to obtain comprehensive information about the treatment and prevention of MIS-C. Materials and methods: In this cross-sectional study, all MIS-C patients <21 years in Dec 2019 to Oct 2021 were included. The patients were identified by the Centers for Disease Control and Prevention (CDC) checklist and data were analyzed applying t-test and Chi-square in STATA11. Results: There were 225 patients with a median age of 55 months (26-96 months), including 59.56% boys and all had fever on admission. At least one gastrointestinal symptom was seen in 200 patients and the most common symptoms were vomiting (60.9%) and abdominal pain (45.77%). Almost 60% of the patients had positive RT-PCR results. Among the patients with and without gastrointestinal symptoms 85.5% and 48% were admitted to intensive care unit (ICU), respectively. There were significant differences between the two groups in respiratory symptoms, ALT, AST, NT-pro BNP, ESR, and PLT (P < 0.05). All patients without gastrointestinal symptoms were discharged but nine patients in the group with gastrointestinal symptoms deceased. Conclusion: According to the current study, gastrointestinal symptoms are common in MIS-C patients and are associated with higher rates of death and intensive care unit admission. Therefore, in providing services to COVID-19 patients, all typical and atypical signs and symptoms should be considered to prevent unnecessary interventions.

Hospital management and game theory

The COVID-19 pandemic has highlighted that we are stronger when joined around a shared vision. A challenging task in hospitals is to define the scenarios and face change in a manner that benefits the patients, clinical practices and themselves institutions. Game theory provides frames of study for healthcare decision-making at high levels as the government and professional societies. This allows us to study and incorporate this theory to define and approach solutions that can hold the different health systems feasible and wholesome. This chapter presents a conceptual framework that sheds light on medical tutoring in a hospital. Intensive care units are the focus of this study because they have a relevant role in this scenario. The new educational challenges in critical care services must face from a perspective that provides a proper response to changing actuality. This is done through enhanced practice to make decisions using game theory. The principles of this theory predict human behaviour, helping with decision-making and describing how determined results can appear that are not optimal for the entire group. The implementation of critical thinking between an intensive care unit and another service is studied. The results obtained agree with the expected behaviour. The study indicates that game theory provides a framework which manages educational collaboration between clinical units in the hospital. It can get suitable models for strategic interactions that frequently occur in education training and application in medicine. The chapter studies the environments wherein the theory has been applied and the upcoming challenges in this sector. © 2023 Nova Science Publishers, Inc. All rights reserved.

Three Organizational Perspectives on the Adoption of Telework

Over the past 20-30 years, many public sector organizations have adopted organizational forms that include multi-located organizational units, in which leaders and part of their subordinates work in different geographical locations. The COVID-19 lockdowns have caused a similar trend with an increased use of home offices. Consequently, many leaders today have people working from different geographical locations, and virtual leadership (distant leadership) has become the possible normal practice. The situation before, during and after the COVID-19 pandemic can be understood from multiple theoretical perspectives within organizational research: the technological, the performance gap and the institutional perspective. The purpose of this chapter is to present, illustrate and discuss these three organizational perspectives on the adoption of-and changes related to-telework and virtual leadership. The illustrations of these perspectives are conducted to the old normal and the lockdown period, while the discussion is in relation to possible "new normal practices.” The illustrations are drawn from Norwegian public organizations, and the perspectives build on classic and new contributions within organizational research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Factors for prolonged hospital length of stay in discharged patients with COVID-19

Hospitals were overburdened during peak periods of Coronavirus disease 2019 (COVID-19) pandemic, and bed occupancy was full. The ability to predict and plan patients' hospital length of stay allows predictability in terms of the free capacity of hospital facilities. The purpose of this article is to evaluate the factors that influence the hospital length of stay among discharged (recovered) from COVID-19 patients. This will allow the prediction of the likely number of bed days in the conditions of intensive workload of medical facilities for hospital care. A total of 441 discharged after hospital treatment for COVID-19 patients are followed up. Factors for prolonged hospital length of stay are searched among the indicators recorded at admission. Median hospital length of stay of the patients discharged from COVID-19 ward is 9 days (IQR 6-12) and in the COVID-19 intensive care unit 12 days (IQR 9.75-18.75). The median length of stay assessed by a survival analysis is 35 days in the COVID-19 unit and only 8 days in intensive care, due to the high mortality in the intensive care unit. The longer hospital length of stay of patients discharged from the COVID-19 wards is associated with the presence of hypertension (median 10 vs. 8 days for patients without the disease, p=0.006), ischemic heart disease (10 vs. 8 days, p<0.001), cerebrovascular disease (10 vs. 8 days, p=0.061 - did not reach significance), peripheral arterial disease (12 vs. 8 days, p=0.024), chronic renal failure or chroniodialysis (14 vs. 8 days, p<0.001), oncological illness (11 vs. 8 days, p=0.024), presence of at least one comorbidity (9 vs. 8 days, p=0.006), arrival at the hospital by ambulance vs. the patient's own transport (11 vs. 8 days, p=0.003), severe lung involvement shown on X-ray (10 vs. 8 days, p=0.030) or CT (18 vs. 10 days, p=0.045). Prolonged hospital length of stay is associated with older age (Spearman's rho=0.185, p<0.001), greater number of comorbidities (Spearman's rho=0.200, p<0.001), lower oxygen saturation on admission (Spearman's rho=- 0.294, p<0.001) and lower lymphocytes count (Spearman's rho=-0.209, p<0.001), as well as higher CRP (Spearman's rho=0.168, p<0.001), LDH (Spearman's rho=0.140, p=0.004), ferritin (Spearman's rho=0.143, p=0.004) and d-dimer (Spearman's rho=0.207, p<0.001). The multiple linear regression model found that the increase in the number of bed days of discharged from COVID-19 unit patients depends on the way the patient arrived at the Emergency Department (by ambulance instead of on their own transportation) and the presence of an accompanying oncological disease (R2=0.628, p<0.001). The hospital length of stay of patients discharged from COVID-19 intensive care unit is associated with the presence of hypertension (median 14 vs. 9 days for patients without the disease, p=0.067 - significance not reached) and at least one comorbidity (14 vs. 9 days, p=0.067 - significance not reached). The number of bed days is higher when recorded more comorbidities (Spearman's rho=0.818, p=0.004), lower oxygen saturation (Spearman's rho=-0.605, p=0.067 - significance not reached) and higher leukocytes count (Spearman's rho=0.546, p=0.102 - significance not reached). A multiple linear regression model demonstrated the hospital length of stay of patients in the COVID-19 intensive care unit as an outcome of the number of comorbidities only (R2=0.826, p=0.003). The ability to estimate and forecast quickly the number of bed-days based on a small number of variables would help reduce the burden on the healthcare system during a pandemic.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

The Associations of Antihypertensive Medications, Steroids, Beta Blockers, Statins and Comorbidities with COVID-19 Outcomes in Patients with and without Chronic Kidney Disease: A Retrospective Study

(1) Background: Data on COVID-19 outcomes and disease course as a function of different medications used to treat cardiovascular disease and chronic kidney disease (CKD), as well as the presence of different comorbidities in primarily Black cohorts, are lacking. (2) Methods: We conducted a retrospective medical chart review on 327 patients (62.6% Black race) who were admitted to the Detroit Medical Center, Detroit, MI. Group differences (CKD vs. non-CKD) were compared using the Pearson χ2 test. We conducted univariate and multivariate regression analyses for factors contributing to death during hospitalization due to COVID-19 (primary outcome) and ICU admission (secondary outcome), adjusting for age, sex, different medications, and comorbidities. A sub-analysis was also completed for CKD patients. (3) Results: In the fully adjusted model, a protective effect of ACEi alone, but not in combination with ARB or CCB, for ICU admission was found (OR = 0.400, 95% CI [0.183–0.874]). Heart failure was significantly associated with the primary outcome (OR = 4.088, 95% CI [1.1661–14.387]), as was COPD (OR = 3.747, 95% CI [1.591–8.828]). (4) Conclusions: Therapeutic strategies for cardiovascular disease and CKD in the milieu of different comorbidities may need to be tailored more prudently for individuals with COVID-19, especially Black individuals. [ FROM AUTHOR] Copyright of COVID is the property of MDPI 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.)

Effect of Policies Aiming to Restrict Ultrasound Use

INTRODUCTION: During the early months of the COVID-19 pandemic, policies were implemented that sought to reduce in-person prenatal visits and ultrasounds. We sought to evaluate the effect of those policies on the rate of diagnosed fetal growth restriction and infant low birth weight. METHODS: We performed a cohort study of patients delivered at an academic center. Participants who received prenatal care during the time period of restricted visits were matched in a 1:1 ratio to patients receiving care during an equivalent time period when there were no such restrictions (group I: July 1, 2019, to December 31, 2019;and group II: March 23, 2020, to September 23, 2020). Medical records were reviewed for clinical and demographic characteristics. Neonatal morbidity was defined as any of the following: stillbirth, neonatal death, preterm birth, neonatal intensive care unit admission, low birth weight. Data were analyzed using chi-square and Mann-Whitney U test where appropriate. P <.05 was significant. RESULTS: Our cohort included 580 patients. Overall, the group had a 13% preterm birth rate, 8.2% were diagnosed with fetal growth restriction, and 26% had the composite neonatal morbidity. All patients in the cohort had at least one ultrasound. Compared to group I, group II had more individuals who had only one ultrasound during the pregnancy (3.1 versus 0%, P =.004), but overall the group had more total ultrasounds performed (1.5 [1–3] versus 1 [1–2], P =.02) and had more patients who required fetal surveillance for maternal and fetal conditions (56 versus 44, P =.014). Group II was more likely to have a neonatal demise (1.4 versus 0.3%, P <.01), but there was no difference in the rate of prenatal diagnosis of fetal growth restriction or low birth weight. CONCLUSION: Policies to reduce prenatal ultrasounds were not effective in reducing ultrasounds performed, and there was no difference in the diagnosis of fetal growth restriction despite an increase in comorbidity. [ FROM AUTHOR] Copyright of Obstetrics & Gynecology is the property of Lippincott Williams & Wilkins 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.)

[Castellon Field Hospital: comprehensive analysis of its use during the SARS-CoV-2 pandemic.]

OBJECTIVE: Field hospitals, also known as alternative care sites, have been an important healthcare reinforcement during the SARS-CoV-2 pandemic worldwide. In the Valencian Community, three of these hospitals were opened, one for each province. Our study aimed to make a comprehensive analysis of this resource in Castellon. METHODS: A retrospective observational study was carried out with an analytical and statistical component of 3 aspects: infrastructure, satisfaction and clinical data from COVID-positive hospitalized patients. The sources of information were primary, institutional for the infrastructure and personal for the satisfaction surveys and clinical data. RESULTS: A set of 6x3 metres polyvalent tents was chosen, which joined formed a single-floor area of about 3.500 m2. Although hospital opened for approximately a year and a half with multiple uses, most in relation to the COVID pandemic (vaccination center, emergency room observation, hospital assistance, warehouse...), reception of positive patients for the virus began during the third wave of the pandemic, remaining active for eleven days. A total of thirty-one patients with a mean age of 56 years were admitted. 41.9% did not have any comorbidity and 54.8% needed treatment with oxygen therapy. Furthermore, the length of stay was three days, finding a significant relationship between this one, the oxygen flow required during admission and the age. Satisfaction was measured by a survey of seventeen questions where an average satisfaction of 8.33/10. CONCLUSIONS: This is one of the few studies in the literature in which a field hospital is analyzed from such different points of view. After this analysis, it is concluded that it is an extraordinary and temporary resource whose use is useful without reflecting an increase of morbidity/mortality among our patients and with a very favorable subjective assessment.

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Transthoracic echocardiography outcomes in critical COVID-19 and association with symptom burden - a longitudinal cohort study

Objective: To look for any relationship between severe/critical coronavirus disease 2019 (COVID-19) illness and post-discharge cardiac function, and also assess any correlation between this and post-COVID symptom burden. Design: Observational cohort study with both retrospective and prospective components. Setting: Intensive Care Unit (ICU) and subsequent outpatient clinic at a tertiary hospital in Western Sydney, New South Wales (NSW), Australia. Patients: All patients admitted to the ICU with COVID-19 infection between 01 July 2021 and 31 December 2021 were included (n=89). Interventions: The cohort was divided into survivors (n=61) and non-survivors (n=28). Those who underwent transthoracic echocardiography (TTE) (survivors, n=22;and non-survivors, n=23). The survivors who had an inpatient TTE were invited back for a repeat TTE and standardised symptom assessment questionnaire (COVID-19 Yorkshire Rehabilitation Scale [C19-YRS]). For all patients, demographic, clinical, biochemical, and pharmacologic data was collected. Measurements and results: Eighty-nine patients were included in the initial dataset, of which 45 had a TTE whilst acutely unwell, and 22/45 survived to hospital discharge. There were no significant differences in the measured TTE parameters between survivors and non-survivors. Of the survivors with a follow-up TTE, the majority of the changes seen in the initial study had resolved. Despite this, there was still an appreciable symptom burden in the domains of fatigue, breathlessness, ability to independently do activities of daily living, and overall reduced perception of health. Conclusions: In a cohort of critically unwell COVID-19 patients, there were no significant echocardiographic differences between survivors and non-survivors. For the survivors, whilst the majority of acute cardiac changes associated with COVID-19 infection resolved over time, however, there remained a significant symptom burden, including breathlessness and fatigability, suggesting a non-cardiac aetiology of these symptoms.

Longitudinal robustness of a thoracic radiographic AI model for COVID-19 severity prediction

The 'ging' of artificial intelligence/machine learning (AI/ML) models after initial development and evaluation is known to frequently occur and can pose substantial problems. When there are changes in population, disease characteristics, imaging equipment, or protocols, model performance may start to deteriorate, and the performance predicted in a research setting may no longer hold after deployment (either in a clinical setting or in further research). This data shift phenomenon is a common problem in AI/ML. We trained and evaluated a previously in-house developed AI/ML model for COVID severity prediction using two COVID-19-positive consecutive adult patient cohorts from a single institution. The first cohort was from the time that the Delta strain was dominant accounting for <95% of cases (June 24-December 11, 2021, 820 patients, 1331 chest radiographs (CXRs)) and the second cohort was from the time that the Omicron variant was dominant (Jan 1-21, 2022, 656 patients, 970 CXRs). Inclusion criteria were COVID-positivity and the availability of CXR imaging exams, in general for patients not admitted to ICU and prior to ICU admission for those patients admitted to ICU as part of their treatment. Exclusion criteria were image acquisition in ICU or the presence of mechanical ventilation. Our image-based AI/ML model was trained to predict, based on each frontal CXR from a COVID-positive patient, whether this patient would be admitted to ICU within a 24, 48, 72, or 96-hour window. The model was evaluated 1) in a cross-sectional test when trained on a subset/tested on an independent subset of the Delta cohort, 2) similarly for the Omicron cohort, and 3) in a longitudinal test when trained on the Delta cohort/tested on the Omicron cohort. Cohorts were similar in ICU admission rate and fraction of portable CXRs, while immunization rate was higher for the Omicron cohort. The model did not demonstrate signs of aging with performances in the longitudinal test being very similar to those within the Delta cohort, e.g., an area under the ROC curve in the task of predicting ICU admission within 24 hours of 0.76 [0.68;0.84] when trained/tested within the Delta cohort and 0.77 [0.73;0.80] for the longitudinal test (p>0.05). The performance within the Omicron cohort was similar as well, at 0.76 [0.66;0.84]. Our AI/ML model for COVID-severity prediction did not demonstrate signs of aging in a longitudinal test when trained on the Delta cohort and applied as-is to the Omicron cohort. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

The Impact of the COVID-19 Pandemic on Financial Condition and Mortality in Polish Regions

The study aimed to assess the impact of the COVID-19 pandemic on the financial condition and mortality in Polish voivodeships. To achieve this objective, the relationship between the number of deaths before and during the pandemic and the financial condition of the provinces in Poland was studied. The study covered the years 2017–2020, for which a one-way ANOVA was used to verify whether there was a relationship between the level of a province's financial condition and the number of deaths. The results of the study are surprising and show that before the COVID-19 pandemic, there was a higher number of deaths in provinces that were better off financially, but the relationship was not statistically significant. In contrast, during the pandemic, a statistically significant strong negative correlation between these values was proven, which, in practice, shows that regions with better financial conditions had a higher number of deaths during COVID-19.

PATIENTS WITH AXIAL SPONDYLOARTHRITIS HAVE BETTER SARS-CoV-2 OUTCOMES COMPARED WITH PATIENTS WITH RHEUMATOID ARTHRITIS: DATA FROM THE NATIONAL SAR-COVID REGISTRY

BackgroundPatients with rheumatic diseases may present more severe SARS-CoV-2 infection compared to the general population. However, in some studies, hospitalization and mortality due COVID-19 were lower in patients with axial spondyloarthritis (axSpA) compared to other rheumatic diseases.ObjectivesTo assess the severity of SARS-CoV-2 infection in patients with axSpA from the SAR-COVID registry, comparing them with patients with rheumatoid arthritis (RA), and to determine the factors associated with poor outcomes and death.MethodsPatients ≥18 years old from the SAR-COVID national registry with diagnosis of AxSpA (ASAS criteria 2009) and RA (ACR/EULAR criteria 2010) who had confirmed SARS-CoV-2 infection (RT-PCR or positive serology), recruited from August 2020 to June 2022 were included. Sociodemographic and clinical data, comorbidities, treatments and outcomes of the infection were collected. Infection severity was assessed using the WHO-ordinal scale (WHO-OS)[1]: ambulatory [1], mild hospitalizations (2.3 y 4), severe hospitalizations (5.6 y 7) and death [8].Statistical analysisDescriptive statistics. Chi[2] or Fischer test and Student T or Mann-Whitney as appropriate. Poisson generalized linear model.ResultsA total of 1226 patients were included, 59 (4.8%) with axSpA and 1167 (95.2%) with RA. RA patients were significantly older, more frequently female, and had a longer disease duration. More than a third of the patients were in remission. 43.9 % presented comorbidities, arterial hypertension being the most frequent. At the time of SARS-Cov-2 diagnosis, patients with RA used glucocorticoids and conventional DMARDs more frequently than those with axSpA, while 74.6% of the latter were under treatment with biological DMARDs being anti-TNF the most used (61%).94.9 % of the patients in both groups reported symptoms related to SARS-CoV-2 infection. Although the differences were not significant, patients with RA presented more frequently cough, dyspnea, and gastrointestinal symptoms, while those with axSpA reported more frequently odynophagia, anosmia, and dysgeusia. During the SARS-CoV-2 infection, 6.8% and 23.5% of the patients with axSpA and RA were hospitalized, respectively. All of the patients with axSpA were admitted to the general ward, while 26.6% of those with RA to intensive care units. No patient with axSpA had complications or severe COVID-19 (WHO-OS>=5) or died as a result of the infection while mortality in the RA group was 3.3% (Figure 1).In the multivariate analysis adjusted to poor prognosis factors, no association was found between the diagnosis of axSpA and severity of SARS-CoV-2 infection assessed with the WHO-OS (OR -0.18, IC 95%(-0.38, 0.01, p=0.074).ConclusionPatients with EspAax did not present complications from SARS-CoV-2 infections and none of them died due COVID-19.Reference[1]World Health Organization coronavirus disease (COVID-19) Therapeutic Trial Synopsis Draft 2020.Figure 1.Outcomes and severity of SARS-CoV-2 infection in patients with axSpA and RA.[Figure omitted. See PDF]Acknowledgements:NIL.Disclosure of InterestsAndrea Bravo Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Tatiana Barbich Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Carolina Isnardi Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretati n, or writing the report. They do not have access to the information collected in the database., Gustavo Citera Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Emilce Edith Schneeberger Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Rosana Quintana Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Cecilia Pisoni Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Mariana Pera Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Edson Velozo Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Dora Aida Pereira Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Paula Alba Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Juan A Albiero Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Jaime Villafañe Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Hernan Maldonado Ficco Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Veronica Sa io Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Santiago Eduardo Aguero Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Romina Rojas Tessel Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Maria Isabel Quaglia Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., María Soledad Gálvez Elkin Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access tothe information collected in the database., Gisela Paola Pendon Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Carolina Aeschlimann Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Gustavo Fabian Rodriguez Gil Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Malena Viola Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Cecilia Romeo Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Carla Maldini Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Silvana Mariela Conti Grant/research support from: SAR-COVID is a multi-sponsor re istry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Rosana Gallo Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Leticia Ibañez Zurlo Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Maria Natalia Tamborenea Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Susana Isabel Pineda Vidal Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Debora Guaglianone Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Jonatan Marcos Mareco Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Cecilia Goizueta Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Elisa Novatti Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Fernanda Guzzanti Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Gimena Gómez Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Karen Roberts Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of t em participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database., Guillermo Pons-Estel Grant/research support from: SAR-COVID is a multi-sponsor registry, where Pfizer, Abbvie, and Elea Phoenix provided unrestricted grants. None of them participated or influenced the development of the project, data collection, analysis, interpretation, or writing the report. They do not have access to the information collected in the database.

Predicting Intensive Care Unit Admission Using Machine and Deep Learning: COVID-19 Case Study

The COVID-19 pandemic has affected the entire world by causing widespread panic and disrupting normal life. Since the outbreak began in December 2019, the virus has killed thousands of people and infected millions more. Hospitals are struggling to keep up with large patient flows. In some situations, hospitals are lacking enough beds and ventilators to accommodate all of their patients or are running low on supplies such as masks and gloves. Predicting intensive care unit (ICU) admission of patients with COVID-19 could help clinicians better allocate scarce ICU resources. In this study, many machine and deep learning algorithms are tested over predicting ICU admission of patients with COVID-19. Most of the algorithms we studied are extremely accurate toward this goal. With the convolutional neural network (CNN), we reach the highest results on our metrics (90.09% accuracy and 93.08% ROC-AUC), which demonstrates the usability of these learning models to identify patients who are likely to require ICU admission and assist hospitals in optimizing their resource management and allocation during the COVID-19 pandemic or others. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The effect of having COVID-19 on anti-vaccination: observational prospective study

Objective: In this study, the effect of having had coronavirus disease-2019 (COVID-19) disease on anti-vaccination was investigated. Material and Methods: The study was conducted between February 2022-August 2022 in the COVID chest diseases clinic in our hospital. The cases who were COVID-19 vaccine hesitancy and not vaccinated against COVID-19 hospitalized in our clinic were included in the study. The level of anti-vaccination of the cases was measured with the Vaccine Hesitancy Scale (VHS). In addition, demographic informations such as age, gender, educational status, marital status, number of people living in the household, average monthly income, smoking history, and additional chronic diseases were recorded. Routine radiological and laboratory examinations, follow-up times in the clinic, and treatment results were recorded for cases like all patients hospitalized in our COVID chest diseases clinic. Results: 46 cases were included in the study. The mean age of the cases was 54.63+or-14.81 years, 24 (52.1%) were female. VHS was applied to all cases at the time of hospitalization. Since 6 cases were referred to the intensive care unit due to respiratory failure, the second VHS could not be applied to these cases, and these 6 cases were excluded from the study. A 2nd VHS was performed in the remaining 40 patients just before discharge. Each question score, A-B-C section score and total scale score were compared for the pre-COVID-19 and post-COVID-19 cases. While the VHS total score before COVID-19 infection was 36.48+or-7.36, the post-COVID-19 total score was found to be 25.65+or-9.10, a statistically significant decrease was observed (p < 0.001). It was observed that the mean scores of A-B-C decreased statistically in all sections (p < 0.001). Conclusion: As a result of our study, we found that the degree of anti-vaccine resistance decreased after the patients who were against the COVID-19 vaccine had the disease. As a result of our study, we found that the degree of COVID-19 vaccine hesitancy of patients decreased after they had the disease. We believe that conducting similar studies and sharing their results through mass media, can change the perspective of vaccine hesitancy individuals in society on this situation, especially during pandemic periods.

Brixia score as predictor of D-Dimer levels of COVID-19 patients in intensive care unit

Background: COVID-19 is caused by a novel virus that can cause lung abnormalities which can be measured with new chest x-ray scoring system named Brixia score. In COVID-19 patients, coagulation disorders are often found that can be seen through D-Dimer levels. This study aimed to prove the Brixia Score as a predictor of D-Dimer levels. Subjects and Method: This study was an observational analytic study with a cross-sectional approach. The subjects were 94 COVID-19 patients which taken from ICU Melati 1 Dr. Moewardi General Hospital, Surakarta from March 2021 to August 2021 who met the exclusion and inclusion criteria. The independent variable is the Brixia score performed by radiologists and the dependent variable is D-Dimer levels taken from laboratory results. Sampling was obtained by purposive sampling and the data were investigated using the receiver operating characteristic (ROC) curve. Results: 94 samples were obtained for analysis. The average Brixia Score of patients with D-Dimer <2 micro g/mL was Mean= 15.85;SD= 1.43 and D-Dimer 2 micro g/mL was Mean= 17.29;SD= 0.96. There was a significant difference between the Brixia Score of patients with D-Dimer <2 micro g/mL and D-Dimer 2 micro g/mL (p<0.001). Analysis with the ROC curve shows an area under the curve (AUC) of 0.793. The optimal cutoff value of the Brixia Score for predicting D-Dimer levels was 16.5 (sensitivity 77.9%, specificity 73.1%). Conclusion: Brixia Score proved to be a predictor of D-Dimer levels of COVID-19 patients in ICU care.

The Experiences of COVID-19 Patients in Intensive Care Units: A Qualitative Study.

This exploratory qualitative study explores the experiences of COVID-19 patients in intensive care units and after discharge. Semi- structured telephone interviews were conducted with 18 COVID-19 patients admitted to and discharged from intensive care units between March and September in 2020. The themes of this study were determined as "feelings about the illness and intensive care," "psychological and physical damages," "nurses' efforts and the importance of care.", and "protecting health and life". COVID-19 patients in intensive care units may experience permanent physical and psychological damages. The findings suggest that the first step in carrying out interventions in the intensive care units is to ensure that continuous communication with patients is maintained so that their orientation to the new circumstances can be achieved. Nursing interventions to patients missing their families can have compensated for the loss of family support and care during their critical illness.