Confirmed COVID-19 Patients Research Articles

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization.

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectivesThe computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors. Materials and methodsThis retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0–3 days), intermediate phase (4–7 days), and late phase (8–14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination. ResultsA total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88–0.94), 0.82 (95% CI 0.76–0.87), and 0.83 (95% CI 0.77–0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94–0.98), 0.90 (95% CI 0.87–0.94), and 0.89 (95% CI 0.84–0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed. ConclusionThe time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Anosmia in COVID-19: Investigating the Role of Paranasal Sinus Mucosal Thickening.

Background Anosmia has been identified as a distinctive symptom of COVID-19, leading to hypotheses about its pathophysiological underpinnings, including the potential role of paranasal sinus mucosal thickening. Objective To investigate the association between paranasal sinus mucosal thickening and anosmia in COVID-19 patients, providing insights into the complex clinical manifestations of the disease. Methods This retrospective cohort study analyzed CT paranasal sinus from 270 confirmed COVID-19 patients, divided into those with anosmia (n = 23, 8.52%) and those without anosmia (n = 247, 91.48%). Statistical analysis, including independent t-tests, was employed to compare mucosal thickening between the groups. Results The study found an average mucosal thickening of 0.03in patients with anosmia and 0.02in those without, with no statistically significant difference between the groups (p = 0.480, which is greater than 0.05). The findings suggest that mucosal thickening in the paranasal sinuses does not serve as a definitive correlate of anosmia among COVID-19 patients. Conclusion The absence of a significant correlation between paranasal sinus mucosal thickening and anosmia in COVID-19 patients indicates that the pathophysiology of anosmia may involve factors beyond anatomical changes, including direct viral effects and systemic inflammatory responses.

Assessment of concentrations of interleukin IL-1β, interleukin 2, interleukin 15 and level of Testosterone hormone in COVID-19 infertile patients

The male reproductive system relies on the proper production of sperm. Spermatogenesis is a complex process that calls for the cooperation of many different elements. The presence of pro-inflammatory cytokines such as interleukin 1 beta (IL-1β), interleukin 2, and interleukin 15 in the male reproductive tract may be responsible for the manifestation of physiological processes in the testes, epididymis, and sperm of the male reproductive system. However, The present study aimed to determine the effect of Coronavirus disease (COVID-19) on the concentration levels of IL-1β, interleukin 2, and interleukin 15 in males. Moreover, to assess its impact on the hormone. A total of the n=95 clinical samples from the infertile patients confirmed COVID-19 patients involved the analysis of serum or plasma samples from 95 patients to measure levels of Testosterone hormone, IL15, IL-1B, and IL-2 by using an ultrasensitive enzyme-linked immunoabsorbent. The results showed a significant increase (p&lt;0.05) of IL-1B, IL-15 and a decrease in IL-2 level in infertile; also, testosterone evaluated in the serum and plasma of SARS-CoV-2 patients showed a positive connection between the level of hormone and IL-1B, IL-15, While negatively related with IL-2 patients infected with coronavirus compared with non-infected infertile patients. Male individuals with COVID-19 who experience infertility exhibited a combination of various factors that are linked to reduced testosterone hormone levels. These factors include advancing age, obesity, and cytokine alteration; there is an observed increase in IL-1B and IL-15 levels and a decrease in IL-2 concentration in these patients.

Studying the correlation of inflammatory cytokines to COVID-19 disease

Extreme response of the immune system develops cytokine storm which might be crucial in the pathology of COVID-19. The research aims to evaluate the serum level of IL-6, TNF-α, and IP-10 in severe, mild, and pre-vaccinated one-dose COVID-19 patients and investigate their clinical value and effect in the disease development among different groups of patients. A total of 72 samples were collected 18 as healthy control and 54 from confirmed COVID-19 patients including 18 mild, 18 severe, and 18 pre-vaccinated (one dose). It was confirmed that the severe group of COVID-19 patients had the highest circulating IL-6, TNF- α, and IP-10. IL-6 level in mild and pre-vaccinated (one dose) was significantly lower than in severe. In conclusion, IL-6, TNF-α, and IP-10 are associated with the pathogenicity of COVID-19, furthermore, vaccination could help to control severity of the disease.

Humoral and Cellular Immune Response on COVID-19 Patients and Sinovac Vaccine Participants

Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-2 is a new SARS-CoV virus. A person who is infected with this virus will induce both humoral and cellular immune responses. Herd immunity can be achieved through vaccination. The purpose of vaccination is the formation of antibodies capable of neutralizing coronavirus against the receptor binding domain. This study aimed to determine the differences between humoral and cellular immune responses between confirmed COVID-19 patients and Sinovac vaccine participants. This observational analytic study with a prospective cohort approach was conducted between March to October 2021. Fifty subjects (25 officers who had received vaccinations for COVID-19 patients and 25 COVID-19 patients treated at the Dr. Moewardi General Hospital) and met the inclusion and exclusion criteria were enrolled. Different tests were carried out to see the difference between the levels of CD8+ T cells and anti-SARS-CoV-2 antibodies in the vaccine group and the COVID-19 patient group. There was no significant difference in humoral immune response (anti-SARS-CoV-2) between the vaccine group and COVID-19 patient group [33.93 (0.4–196.6) U/L vs. 101.28±158.59 U/L; p=0.409], but there was a significant difference in cellular immune response (CD8+) between the vaccine group and COVID-19 patient group [878.52±47368 cells/µL vs. 270.16±213.64 cells/µL; p=0.001]. CD8 assay can be used as a parameter to differentiate the cellular immune response between COVID-19 patients and COVID-19 vaccine recipients.

N-Acetylcysteine as A Potential Adjunctive Theraphy in Covid-19 Patients Associated Coagulopathy by Lowering Il-1????, D-dimer and Padua Scores Levels

Introduction: Many unique pathologies, such as hyperinflammation and hypercoagulopathy, have been associated with Covid-19, resulting in critical illness and poor outcomes. N-Acetylcysteine (NAC), a precursor of the antioxidant glutathione, has a number of biological effects, including increasing antioxidant capacity, inhibiting viral replication, and suppressing the release of proinflammatory cytokines, such as IL-1????, which further reduces coagulopathy as measured by D-dimer and PADUA scores. The aim of this research is to analyze the effect of NAC as an adjunctive therapy on the level of IL-1????, D-dimer and PADUA scores among Covid-19 patients. Method: A quasi-experimental, non-equivalent group, single-center study included 91 hospitalized confirmed Covid-19 patients by RT-PCR, 75 patients received standard of care plus 5000 mg/72 hours of NAC and 16 patients received standard of care without NAC. On the first and eighth days of treatment, IL-1????, D-dimer levels, and PADUA scores were measured by ELISA and quantitative-assay methods. Data analysis used were Mann-Whitney test, Wilcoxon test, and Spearman-rho correlation test. Result: After the eighth day of therapy, the NAC group had a statistically significantly reduction in IL-1????(pg/mL) (1,48[-4,23-6,68] vs -0,43[-3,40-2,0], p=0.03) and D-dimer(µg/mL) (0,75[-5,98-6,45] vs -0,03[-8,07-1,00], p&lt;0,001) levels than the control group, but not in PADUA scores (1,05[0-3] vs 0,94[0-1], p=0,458). In a subanalysis of NAC's group, IL-1???? was found to be quite strongly correlated with D-dimer (r=0,508, p&lt;0,001). Conclusion: Our findings show that supplementary NAC reduces hyperinflammation and hypercoagulopathy in Covid-19 patients by suppressing IL-1???? and lowering D-dimer, and thus could be used as a supplement to Covid-19 therapy.

Response to prone positioning in COVID-19 patients with acute respiratory distress syndrome: a retrospective observational study

BackgroundCOVID-19 pneumonia and respiratory failure are the leading causes of death in COVID-19 patients. Prone positioning was hypothesized to improve oxygenation in ARDS patients and is being studied in COVID-19, but the current evidence is still unclear regarding survival and hospitalization. We aimed to investigate the effect of prone positioning on oxygenation in patients with COVID-19 pneumonia and ARDS and to examine the factors associated with better/worse outcomes.MethodsA retrospective record-based cohort study included all confirmed COVID-19 patients with pneumonia and ARDS who underwent prone positioning admitted to King Fahad Hospital, Medina, Saudi Arabia, during 2020–2021.ResultsThis study included 75 cases (mean age 60.3 ± 15.7 year, 50 (66.7%) males), and all fulfilled the definition of ARDS. There was a significant improvement in oxygenation (PaO2 and PaO2/FIO2) following prone positioning (53.5 ± 6.8 vs. 60.4 ± 8.2 mmHg, p < 0.001 for PaO2 supine and prone and 120.3 ± 35 vs. 138 ± 40.2, p < 0.001 for PaO2/FIO2 supine and prone respectively). There was no significant difference in age, gender, smoking, or number of comorbidities between survivors and non-survivors. Survivors had significantly higher baseline PaO2 (p 0.018) and PF ratio (p 0.001) compared to non-survivors. They had also less severe inflammation and organ damage observed as significantly lower ferritin (p 0.001), D-dimer (p 0.026), aspartate aminotransferase (p 0.02), urea (p 0.032), creatinine (p 0.001), and higher platelet counts (p 0.001). Intubation and high-moderate comorbidity risk categories were associated with non-survival (p 0.001 and p 0.014, respectively).ConclusionProne positioning is useful in the improvement of oxygenation in intubated and awake patients with COVID-19 pneumonia and ARDS. Intubation and high comorbidity risk categories were associated with non-survival.

Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI—processed radiological parameter upon admission: a multicentre study

Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095–0.826), dyspnoea (OR: 1.684; 95%CI 1.049–2.705), loss of consciousness (OR: 4.593; 95%CI 1.702–12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900–0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967–0.996), neutrophil % (OR: 1.034; 95%CI 1.013–1.055), serum urea (OR: 1.018; 95%CI 1.010–1.026), affected lung area score (OR: 1.026; 95%CI 1.014–1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774–0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661–0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Rate and Risk Factors of Reinfection, Recurrence, and Hospital Readmission Among SARS-Cov-2 Hospitalized Patients; a National Cohort Study.

Reinfection and hospital readmission due to COVID-19 were significant and costly during the pandemic. This study aimed to assess the rate and risk factors of SARS-Cov-2 reinfection, recurrence, and hospital readmission, by analyzing the national data registry in Iran. This study was a retrospective cohort conducted from March 2020 to May 2021. A census method was used to consider all of the possible information in the national Medical Care Monitoring Center (MCMC) database obtained from the Ministry of Health and Medical Education; the data included information from all confirmed COVID-19 patients who were hospitalized and diagnosed using at least one positive Polymerase Chain Reaction (PCR) test by nasopharyngeal swab specimens. Univariate and multivariable Cox regression analyses were performed to assess the factors related to each studied outcome. After analyzing data from 1,445,441 patients who had been hospitalized due to COVID-19 in Iran, the rates of overall reinfection, reinfection occurring at least 90 days after the initial infection, recurrence, and hospital readmission among hospitalized patients were 67.79, 26.8, 41.61, and 30.53 per 1000 person-years, respectively. Among all cases of hospitalized reinfection (48292 cases), 38.61% occurred more than 90 days from the initial SARS-Cov-2 infection. Getting infected with COVID-19 in the fifth wave of the disease compared to getting infected in the first wave (P<0.001), having cancer (P<0.001), chronic kidney disease (P<0.001), and age over 80 years (P<0.001) were respectively the most important risk factors for overall reinfection. In contrast, age 19-44 years (P<0.001), intubation (P<0.001), fever (P<0.001), and cough (P<0.001) in the initial admission were the most important protective factors of overall reinfection, respectively. Reinfection and recurrence of COVID-19 after recovery and the rate of hospital readmission after discharge were remarkable. Advanced or young age, as well as having underlying conditions like cancer and chronic kidney disease, increase the risk of infection and readmission.

Determining the sweet detection threshold of COVID-19 patients during infection and recovery periods.

The loss of taste and smell is a common symptom of COVID-19, affecting individuals' quality of life and nutritional status. Detecting sweet thresholds during infection and recovery periods can assist in implementing dietary modifications and nutritional strategies for these patients. To investigate the changes and differences in sweet detection thresholds of confirmed COVID-19 patients on Day 1, Day 10, and Day 14 of the infection and recovery periods. The demographic factors such as gender, smoking status, BMI, and age group were abstracted on Excel sheet from the medical health records for confirmed COVID-19 patients, who were admitted to King Fahad General Hospital in Jeddah, Saudi Arabia, a COVID-19 care facility, from September 2021 to July 2022. Sweet detection thresholds were determined using a pair-wise comparison procedure and sugar solutions with varying concentrations, arranged in ascending order and presented to participants until the lowest detected concentration was noted after three consecutive positive detections, with the median just noticeable difference (JND) value calculated as the population average threshold. Sensory tests were conducted on COVID-19 patients during their infection and recovery periods to evaluate their taste sensation thresholds. The demographic factors of gender, smoking status, BMI, and age group were considered in the analysis. A total of 37 patients who met the inclusion criteria of the study were enrolled as participants. Significant variances in sweet detection thresholds were observed among the COVID-19 patients, with consistent decreases over the three testing days, indicating increasing sucrose sensitivity. Infected men showed significant returns to sweet detection thresholds on Day 14 compared to women, while infected smokers exhibited greater recoveries than non-smokers. Overweight patients had consistently elevated thresholds and recovery rates that were comparable to those of normal-weight patients by Day 14, while younger patients had lower thresholds than their older counterparts. On Day 14, the thresholds had significantly recovered to a level comparable to that of healthy individuals (approximately 0.23%). These findings suggest that sweet detection thresholds can be used as a marker for assessing the progression and recovery of COVID-19 patients. These findings highlight the importance of recognizing and managing alterations in sweet detection thresholds promptly in COVID-19 patients, as this could positively impact dietary management, nutritional recommendations, and interventions during infection and recovery periods.

Validation of oxygen saturations measured in the community by emergency medical services as a marker of clinical deterioration in patients with confirmed COVID-19: a retrospective cohort study

ObjectivesTo evaluate oxygen saturation and vital signs measured in the community by emergency medical services (EMS) as clinical markers of COVID-19-positive patient deterioration.DesignA retrospective data analysis.SettingPatients were conveyed by EMS...

Neutrophil extracellular traps, demographic, clinical, and laboratory parameters in COVID-19 patients: Impact on the severity and outcome during Omicron waves in Indonesia

IntroductionIndonesia is significantly affected by the emergence of the Omicron variant during the coronavirus disease 2019 COVID-19 pandemic, with one of the most prominent case increases on February 2022 (third wave of the pandemic). Several factors, ranging from demographics to laboratory tests, have been linked to COVID-19 patients clinical manifestations and outcomes. One of the possible factors that can affect COVID-19 patients' status is the neutrophil extracellular traps (NETs). We aimed to determine NETs level along with demographic, clinical, and laboratory characteristics in COVID-19 patients and analyze its relationship with disease severity and survival. MethodsOne hundred confirmed COVID-19 patients at our hospital were recruited during February to September 2022. They were then divided into groups based on severity (mild-moderate and severe-critical) and outcome (survive and non-survive). The analysis included univariate and bivariate testing, including hazard ratio examination. ResultsClinical symptoms (headache, shortness of breath), pneumonia on chest X-ray, comorbidities (immune disorders, cerebrovascular disease), physical examination (unconsciousness, increased respiratory rate), and laboratory test (elevated C-reactive protein/CRP) all had an impact on patient survival. Meanwhile, similar patterns also observed on the severity-affecting factors, in addition to some variables such as abdominal pain, onset, systolic blood pressure, urea, creatinine, and procalcitonin. The NETs itself did not have any statistically significant role in the severity and survival of COVID-19 patients. ConclusionsClinical and laboratory variables played a crucial role in the prognostic and severity determination of COVID-19 patients, while NETs did not contribute to it.

Derivation of a multi-biomarker model for predicting mortality in hospitalised COVID-19 patients

Introduction: This study aimed to derive and assess the performance of a multi-biomarker model from a combination of basic laboratory biomarkers in predicting mortality of hospitalized COVID-19 patients. Methods: This was a cross-sectional study conducted in a university-affiliated hospital in Malaysia. Data of confirmed COVID-19 patients who were admitted from January 2020 to August 2021 were retrieved including their admission C-reactive protein (CRP), lactate dehydrogenase (LDH), and neutrophil-lymphocyte ratio (NLR). Patients were classified as non-survivors or survivors according to their hospital mortality status. Multi-variable logistic regression analysis was used to derive the multi-biomarker model. Results: A total of 188 confirmed COVID-19 patients were analysed, of which 46 (23%) died in the hospital. Their mean age was 52 (SD 17) years, 104 (52%) were males, 114 (57%) had severe COVID-19 pneumonia, with mean APACHE II score of 14 (SD 10). On admission, those who died had higher median levels of CRP 96.0 (IQR 39.8–182.0) vs 23.0 (IQR 0–67.0 mg/L, p &lt; 0.001), of LDH 973.0 (IQR 706.5–1520.0) vs 515.1 (408.8–738.8 IU/L, p &lt; 0.001), and of NLR 10.1 (IQR 5.5–23.6) vs 2.8 (IQR 1.5–5.9, p &lt; 0.001). The multi-biomarker model had a higher area under the curve (0.866, 95% CI 807-0.925) compared to its constituent individual biomarkers. At its optimal cutoff, this model had 78.9% sensitivity and 76.5% specificity for mortality prediction. Conclusion: A multi-biomarker model of CRP, LDH, and NLR predicted in-hospital mortality with a very good performance in our hospitalised COVID-19 patients.

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients.

During the early stages of the pandemic, computed tomography (CT) of the chest, along with serological and clinical data, was frequently utilized in diagnosing COVID-19, particularly in regions facing challenges such as shortages of PCR kits. In these circumstances, CT scans played a crucial role in diagnosing COVID-19 and guiding patient management. The COVID-19 Reporting and Data System (CO-RADS) was established as a standardized reporting system for cases of COVID-19 pneumonia. Its implementation necessitates a high level of agreement among observers to prevent any potential confusion. This study aimed to assess the inter-observer agreement between physicians from different specialties with variable levels of experience in their CO-RADS scoring of CT chests for confirmed COVID-19 patients, and to assess the feasibility of applying this reporting system to those having little experience with it. All chest CT images of patients with positive RT-PCR tests for COVID-19 were retrospectively reviewed by seven observers. The observers were divided into three groups according to their type of specialty (three radiologists, three house officers, and one pulmonologist). The observers assessed each image and categorized the patients into five CO-RADS groups. A total of 630 participants were included in this study. The inter-observer agreement was almost perfect among the radiologists, substantial among a pulmonologist and the house officers, and moderate-to-substantial among the radiologists, the pulmonologist, and the house officers. There was substantial to almost perfect inter-observer agreement when reporting using the CO-RADS among observers with different experience levels. Although the inter-observer variability among the radiologists was high, it decreased compared to the pulmonologist and house officers. Radiologists, house officers, and pulmonologists applying the CO-RADS can accurately and promptly identify typical CT imaging features of lung involvement in COVID-19.

Design Of A Patient Information System For Covid – 19 Using PHP At Mukomuko Hospital

The design of a COVID-19 patient information system using PHP Muko-muko Hospital has been successfully created which includes patient identity data confirmed for COVID-19, clinical information for COVID-19 patients, monitoring information for confirmed COVID-19 patients, and reports on COVID-19 patient data. -19. This system has been neatly designed to make it easier for officers to find data on COVID-19 patients who have been monitored or have not been monitored when applied to the real system.

Postpandemic Evaluation of the Eco-Efficiency of Personal Protective Equipment Against COVID-19 in Emergency Departments: Proposal for a Mixed Methods Study.

The COVID-19 pandemic has had a profound impact on emergency department (ED) care in Canada and around the world. To prevent transmission of COVID-19, personal protective equipment (PPE) was required for all ED care providers in contact with suspected cases. With mass vaccination and improvements in several infection prevention components, our hypothesis is that the risks of transmission of COVID-19 will be significantly reduced and that current PPE use will have economic and ecological consequences that exceed its anticipated benefits. Evidence is needed to evaluate PPE use so that recommendations can ensure the clinical, economic, and environmental efficiency (ie, eco-efficiency) of its use. To support the development of recommendations for the eco-efficient use of PPE, our research objectives are to (1) estimate the clinical effectiveness (reduced transmission, hospitalizations, mortality, and work absenteeism) of PPE against COVID-19 for health care workers; (2) estimate the financial cost of using PPE in the ED for the management of suspected or confirmed COVID-19 patients; and (3) estimate the ecological footprint of PPE use against COVID-19 in the ED. We will conduct a mixed method study to evaluate the eco-efficiency of PPE use in the 5 EDs of the CHU de Québec-Université Laval (Québec, Canada). To achieve our goals, the project will include four phases: systematic review of the literature to assess the clinical effectiveness of PPE (objective1; phase 1); cost estimation of PPE use in the ED using a time-driven activity-based costing method (objective2; phase 2); ecological footprint estimation of PPE use using a life cycle assessment approach (objective3; phase 3); and cost-consequence analysis and focus groups (integration of objectives1to3; phase 4). The first 3 phases have started. The results of these phases will be available in 2023. Phase 4 will begin in 2023 and results will be available in 2024. While the benefits of PPE use are likely to diminish as health care workers' immunity increases, it is important to assess its economic and ecological impacts to develop recommendations to guide its eco-efficient use. PROSPERO CRD42022302598; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=302598. DERR1-10.2196/50682.

Prehospital COVID-19 patients discharged at the scene – an observational study

BackgroundEmergency medical services (EMS) were the first point of contact for many COVID-19 patients during the pandemic. The aim of this study was to investigate whether the non-conveyance decision of a COVID-19 patient was more frequently associated with a new EMS call than direct ambulance transport to the hospital.MethodsAll confirmed COVID-19 patients with an EMS call within 14 days of symptom onset were included in the study. Patients were compared based on their prehospital transport decision (transport vs. non-conveyance). The primary endpoint was a new EMS call within 10 days leading to ambulance transport.ResultsA total of 1 286 patients met the study criteria; of these, 605 (47.0%) were male with a mean (standard deviation [SD]) age of 50.5 (SD 19.3) years. The most common dispatch codes were dyspnea in 656 (51.0%) and malaise in 364 (28.3%) calls. High-priority dispatch was used in 220 (17.1%) cases. After prehospital evaluation, 586 (45.6%) patients were discharged at the scene. Oxygen was given to 159 (12.4%) patients, of whom all but one were transported.A new EMS call leading to ambulance transport was observed in 133 (10.3%) cases; of these, 40 (30.1%) were in the group primarily transported and 93 (69.9%) were among the patients who were primarily discharged at the scene (p<.001). There were no significant differences in past medical history, presence of abnormal vital signs, or total NEWS score. Supplemental oxygen was given to 33 (24.8%) patients; 3 (2.3%) patients received other medications.ConclusionNearly half of all prehospital COVID-19 patients could be discharged at the scene. Approximately every sixth of these had a new EMS call and ambulance transport within the following 10 days. No significant deterioration was seen among patients primarily discharged at the scene. EMS was able to safely adjust its performance during the first pandemic wave to avoid ED overcrowding.

428. Risk Stratification and Clinical Outcomes in a Mixed Vaccination Cohort of COVID-19 Patients Admitted in a Tertiary Hospital Using ISARIC 4C Scores

Abstract Background The risk of clinical deterioration and death with the use of prognostic tools remain necessary for strategic planning among COVID-19 patients. The International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) developed and validated Coronavirus Clinical Characterization Consortium (4C) scores to predict clinical deterioration and mortality. ISARIC 4C scores uses readily available clinical parameters. With the increasing uptake of COVID-19 vaccine, there is a need to evaluate these tools. The study examined a cohort of fully vaccinated (FV), partially vaccinated (PV) and unvaccinated (UV) COVID-19 patients and determined their clinical outcomes and stratified risks using ISARIC 4C scores. Methods We reviewed medical records of confirmed COVID-19 patients admitted to a single tertiary hospital between May 2021 and April 2022. Vaccination status were classified as FV, PV and UV. The primary outcome was in-hospital mortality. Baseline data were collected and predictors recorded upon admission were used to calculate the 4C scores. Multiple logistic regression analysis was used to determine the association of vaccination status and 4C Deterioration and Mortality scores with in-hospital mortality. STATA 14 was used for data analysis and p&amp;lt; 0.05 was considered statistically significant. Results A total of 301 confirmed COVID-19 patients were included and 46.5% were fully vaccinated. FV patients had significantly lower CRP (p&amp;lt; 0.0001), had higher mean oxygen saturation (p&amp;lt; 0.0001), had lower mean mortality score (p=0.004), had lower mean % risk for deterioration (p&amp;lt; 0.0001) and had significantly lower 30-day mortality (&amp;lt; 0.0001) compared to those UV and PV. Patient who died had significantly higher mean age (p=0.001), lower mean lymphocyte ratio (p=0.0004), had significantly higher mean deterioration and mortality score (p&amp;lt; 0.0001). Being fully vaccinated was associated with lower risk for mortality (OR=0.4, 95%CI=0.2 to 0.7). Conclusion Fully vaccinated patients were at lower risk for deterioration and mortality. High ISARIC 4C scores were also significantly associated with higher risk for mortality. Risk stratification tools should be utilized in COVID-19 management as they provide a helpful means to identify patients at risk for worse outcomes. Disclosures All Authors: No reported disclosures