Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study

Virus and viral components transmitted through surgical smoke; a silent danger in operating room: a systematic review

BACKGROUND

During surgical procedures, heat-generating devices are widely used producing surgical smoke (SS). Since the SS can transmit infectious viruses, this systematic review was designed to investigate the potential viruses transmitted through SS.

METHODS

PubMed, Scopus, Web of Science, ProQuest, and Embase databases, along with Cochran Library, and Google Scholar search engine were searched systematically (by April 21, 2024). No language, place, and time restrictions were considered. All studies evaluating the SS and virus transmission, and whole investigations regarding the viral infections transmitted through SS were totally considered inclusion criteria. Besides, non-original, qualitative, case reports, case series, letters to the editor, editorial, and review studies were excluded from the analysis. This study was conducted in accordance with the PRISMA 2020 statement.

RESULTS

Twenty-six eligible studies were selected and reviewed for data extraction. The results showed that the SS contains virus and associated components. Six types of viruses or viral components were identified in SS including papillomavirus (HPV, BPV), Human Immunodeficiency Virus (HIV), varicella zoster, Hepatitis B (HBV), SARS-CoV-2, and Oral poliovirus (OPV), which are spread to surgical team through smoke-producing devices.

CONCLUSIONS

Since the studies confirm the presence of viruses, and viral components in SS, the potential risk to the healthcare workers, especially in operating room (OR), seems possible. Thus, the adoption of protective strategies against SS is critical. Despite the use of personal protective equipment (PPE), these viruses could affect OR personnel in surgical procedures.

Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection

In this paper, we evaluate the performance and analyze the explainability of machine learning models boosted by feature selection in predicting COVID-19-positive cases from self-reported information. In essence, this work describes a methodology to identify COVID-19 infections that considers the large amount of information collected by the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS). More precisely, this methodology performs a feature selection stage based on the recursive feature elimination (RFE) method to reduce the number of input variables without compromising detection accuracy. A tree-based supervised machine learning model is then optimized with the selected features to detect COVID-19-active cases. In contrast to previous approaches that use a limited set of selected symptoms, the proposed approach builds the detection engine considering a broad range of features including self-reported symptoms, local community information, vaccination acceptance, and isolation measures, among others. To implement the methodology, three different supervised classifiers were used: random forests (RF), light gradient boosting (LGB), and extreme gradient boosting (XGB). Based on data collected from the UMD-CTIS, we evaluated the detection performance of the methodology for four countries (Brazil, Canada, Japan, and South Africa) and two periods (2020 and 2021). The proposed approach was assessed in terms of various quality metrics: F1-score, sensitivity, specificity, precision, receiver operating characteristic (ROC), and area under the ROC curve (AUC). This work also shows the normalized daily incidence curves obtained by the proposed approach for the four countries. Finally, we perform an explainability analysis using Shapley values and feature importance to determine the relevance of each feature and the corresponding contribution for each country and each country/year.

Epidemiological, clinical and laboratory profile of patients presenting with severe acute respiratory syndrome (SARS-CoV-2) in Ethiopia

INTRODUCTION

Data regarding patients presenting with severe acute respiratory syndrome (SARS-CoV-2) illness have not adequately been documented which provides distinct insights into low-resource settings like Ethiopia. Thus, the study aimed to compare epidemiological, clinical and laboratory profiles of patients presenting with acute respiratory syndrome illness in Addis Ababa Ethiopia.

METHODS

We used a comparative cross-sectional study design among patients with SARS-CoV-2 illness at St. Paul's Hospital Millennium Medical College (SPHMMC), Addis Ababa, Ethiopia from October 2020 to September 2021. Using a structured questionnaire a consecutive sampling technique was applied to collect socio-demographic data. Additionally, nasal swabs were collected to confirm SARS-CoV-2 infection using a Real-Time Polymerase Chain Reaction. Blood samples were also collected from the participants for laboratory profiles (hematological tests like; white blood cell count, hematocrit, and platelet count; and biochemical and enzymatic tests like; aspartate transaminase (AST), creatinine, etc) analysis. Data were entered and analyzed using SPSS version 23.0 and p-values ≤0.05 were considered as statistically significant.

RESULTS

Of the total 413 participants presenting with SARS-CoV-2 illness, 250 (60.5%) participants tested positive for COVID-19 disease. COVID-19 patients were less likely to use an alcohol-based method of hand washing (12.5% vs 87.5%; p = 0.048). The COVID-19 patients had a higher proportion of headache (67.3% vs 32.7%, p = 0.001), sore throat (72.5% vs 27.5%, p = 0.001), and loss of sense of taste (74.4% vs 25.6%, p = 0.002). Patients with COVID-19 have significantly higher neutrophil than their counterparts (68.2% vs 31.8%; p = 0.001). Similarly, creatinine (64.9% vs 35.1%, p = 0.001) from renal function and alkaline phosphatase (66.8% vs 33.2%, p = 0.046) in the liver function tests were significantly higher in the COVID-19 patients.

CONCLUSION

Our findings suggest the need to substantially consider headache, sore throat, and loss of taste as potential clinical diagnostic symptoms for early screening and testing. Elevation of neutrophil, creatinine, alkaline phosphatase profiles are also used for potential diagnostic biomarkers in screening and testing suspected patients.

Consistent comparison of symptom-based methods for COVID-19 infection detection

BACKGROUND

During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.

PURPOSE

This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.

METHODS

Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.

RESULTS

Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% - 71.11%), logistic regression techniques (F1-score: 39.91% - 71.13%), and tree-based machine learning models (F1-score: 45.07% - 73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.

CONCLUSIONS

Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.

Prospective Assessment of Symptoms to Evaluate Asymptomatic SARS-CoV-2 Infections in a Cohort of Health Care Workers

BACKGROUND

The frequency of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is unclear and may be influenced by how symptoms are evaluated. In this study, we sought to determine the frequency of asymptomatic SARS-CoV-2 infections in a prospective cohort of health care workers (HCWs).

METHODS

A prospective cohort of HCWs, confirmed negative for SARS-CoV-2 exposure upon enrollment, were evaluated for SARS-CoV-2 infection by monthly analysis of SARS-CoV-2 antibodies as well as referral for polymerase chain reaction testing whenever they exhibited symptoms of coronavirus disease 2019 (COVID-19). Participants completed the standardized and validated FLU-PRO Plus symptom questionnaire scoring viral respiratory disease symptom intensity and frequency at least twice monthly during baseline periods of health and each day they had any symptoms that were different from their baseline.

RESULTS

Two hundred sixty-three participants were enrolled between August 25 and December 31, 2020. Through February 28, 2021, 12 participants were diagnosed with SARS-CoV-2 infection. Symptom analysis demonstrated that all 12 had at least mild symptoms of COVID-19, compared with baseline health, near or at time of infection.

CONCLUSIONS

These results suggest that asymptomatic SARS-CoV-2 infection in unvaccinated, immunocompetent adults is less common than previously reported. While infectious inoculum doses and patient factors may have played a role in the clinical manifestations of SARS-CoV-2 infections in this cohort, we suspect that the high rate of symptomatic disease was due primarily to participant attentiveness to symptoms and collection of symptoms in a standardized, prospective fashion. These results have implications for studies that estimate SARS-CoV-2 infection prevalence and for public health measures to control the spread of this virus.