Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age $\geq 18$ years;inpatient admission;PCR positive for SARS-CoV-2;lung CT available at PACS. We categorized cases into 4 classes: mild < 5% of pulmonary parenchymal involvement, moderate - 5-24%, severe - 25-49%, and critical $\geq50$ %. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory and cardiovascular functioning from a set of demographics and radiomics data regardless of the settings of reconstructionkernels. The regression models can be used for scoring lung impairment and comparing disease severity in followup studies. The most accurate prediction we achieved was 6.454 +/- 3.715% of mean absolute error/range for all thefeatures and 7.069 +/- 4.17% for radiomics.Conclusion:The models may contribute to the proper risk evaluation anddisease management especially when the oxygen therapy impacts the actual values of the functional findings. Still,the structural assessment of an acute lung injury reflects the severity of the disease.

Reliability of machine learning in reflecting severity and pathophysiology in COVID-19 pneumonia: impact of CT reconstruction kernels on accuracy

Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age≥18 years;inpatient admission;PCR positive for SARS-CoV-2;lung CT available at PACS. We categorized cases into 4 classes: mild ≤25% of pulmonary parenchymal involvement, moderate - 25-50%, severe - 50-75%, and critical –over 75%. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory and cardiovascular functioning from a set of demographics and radiomics data regardless of the settings of reconstruction kernels. The regression models can be used for scoring lung impairment and comparing disease severity in follow up studies. The most accurate prediction we achieved was 6.454±3.715% of mean absolute error/range for all the features and 7.069±4.17% for radiomics. Conclusion: The models may contribute to the proper risk evaluation and disease management especially when the oxygen therapy impacts the actual values of the functional findings. Still, the structural assessment of an acute lung injury reflects the severity of the disease. Author

Incidence of occupational contact dermatitis in healthcare workers: a systematic review.

Healthcare workers (HCWs) can be considered at an increased risk of developing occupational contact dermatitis (OCD) due to repetitive hand washing with soaps and disinfectants and extended use of gloves for many hours during the day. The aim of this study was to summarize the incidence of OCD in HCWs. We searched the databases PubMed/MEDLINE (1980-present), EMBASE (1980-present) and Cochrane Library (1992-present) through May 2020 using the search term 'incidence of contact dermatitis in HCWs' according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Overall, 16 studies (six cohorts; 10 register-based) with follow-up periods between 1987 and 2013 fulfilled the inclusion criteria. The incidence of OCD reported in studies using registers of occupational diseases ranged from 0.6 to 6.7 per 10 000 person-years. The cohort studies reported incidence from 15.9 to 780.0 per 10 000 person-years; the incidence was higher in studies which included apprentice nurses. A higher incidence was also observed amongst dental practitioners, particularly dental technicians and nurses, compared to other HCWs. Studies reporting incidence data are very scarce and results differed by study design, type of contact dermatitis and investigated HCWs. Our study highlighted the dearth of high-quality data on the incidence of OCD and the possible underestimation of disease burden. Prospective cohort studies with harmonized designs, especially exposure assessment and outcome ascertainment, are required to provide more accurate, valid and recent estimates of the incidence of OCD. A high incidence amongst specific occupational groups suggests the need to undertake intervention studies with a focus on prevention, particularly during pandemics such as COVID-19.