Developing a Personal Protective Equipment Calculator during the COVID-19 Pandemic.

These days, because of the Covid-19 pandemic, we have faced a number of challenges and scarcities in Iran. Lack of Personal Protective Equipment (PPE) is one the most remarkable problems which can have damaging consequences on the health system. In this letter we introduce software which can help hospitals to manage their PPEs in terms of purchasing, distributing and predicting the future needs in different time intervals. The software has several distinctive features such as superior speed, cost management, managerial dashboard, a wide range of applicability, comprehensiveness, supply chain management, and quality appraisal. We hope that our findings can assist health authorities in planning and optimizing the use of PPEs for the response to coronavirus disease, where the shortage of resources may occur due to supply chain issues.

Healthcare workers exposure risk assessment in the context of the COVID-19: a survey among frontline workers in Qazvin, Iran.

BACKGROUND: Healthcare workers perform various clinical procedures for COVID-19 patients facing an elevated risk of exposure to SARS-COV-2.This study aimed to assess the healthcare workers' exposure to COVID-19 in Qazvin, Iran in 2020. METHODS: We conducted this descriptive-analytical study among all healthcare workers on the frontline of exposure to COVID-19 in Qazvin province. We entered the participants into the study using a multi-stage stratified random sampling method. We utilized a questionnaire, "Health workers exposure risk assessment and management in the context of COVID-19 disease", designed by the World Health Organization (WHO) to collect data. We analyzed data using descriptive and analytical methods with SPSS software version 24. RESULTS: The results showed that all participants in the study had occupational exposure to the COVID-19 virus. So of 243 healthcare workers, 186 (76.5%) were at low risk and 57 (23.5%) at high risk of COVID-19 virus infection. Also, from the six domains mentioned in the questionnaire, health workers exposure risk assessment and management in the context of COVID-19 disease, the mean score of the domain of the type of healthcare worker interaction with a confirmed COVID-19 patient, the domain of health worker activities performed on a confirmed COVID-19 patient, the domain of the adherence to infection prevention and control (IPC) during health care interactions, and the domain of the adherence to IPC when performing aerosol-generating procedures in the high-risk group were more than the low-risk group. CONCLUSION: Despite strict WHO guidelines, many healthcare workers are exposed at contracting COVID-19. Therefore, healthcare managers, planners, and policymakers can revise the policies, provide appropriate and timely personal protective equipment, and plan for ongoing training for staff on the principles of infection prevention and control.

Ivermectin-Induced Clinical Improvement and Alleviation of Significant Symptoms of COVID-19 Outpatients: A Cross-Sectional Study.

Although several drugs have been proposed and used to treat the COVID-19 virus, but recent clinical trials have concentrated on ivermectin. It appears that ivermectin can potentially act against COVID-19 and stop the development in its infancy. The purpose of this study was to determine the effect of ivermectin on the recovery of outpatients with COVID-19. In this cross-sectional study, we compared the symptoms reduction in COVID-19 disease in two groups of patients by administering ivermectin. A total of 347 mild outpatients in the Iranian provinces of Qazvin and Khuzestan with a confirmed PCR were enrolled. The symptoms of outpatients with COVID-19 were analyzed using SPSS (V23). In this cross-sectional study, the sex ratio was 0.64 (female/male: 37.9/59.8) and most patients were under 50 years old (72.8%). The results of this study demonstrated a significant decrease in several COVID-19 disease symptoms, including fever, chills, dyspnea, headache, cough, fatigue, and myalgia in the group administered ivermectin compared to the control group. In addition, the odds ratio of the above symptoms was significantly lower in patients who received ivermectin than in patients who did not receive the drug (OR = 0.16, 95% CI = 0.09, 0.27).

Trends of in-hospital and ICU mortality in COVID-19 patients over the fourth and fifth COVID-19 surges in Iran: a retrospective cohort study from Iran.

OBJECTIVE: The current study aimed to investigate the temporal trend of in-hospital and intensive care unit (ICU) mortality of coronavirus disease 2019 (COVID-19) patients over 6 months in the spring and summer of 2021 in Iran. DESIGN: We performed an observational retrospective cohort study. SETTING: Qazvin Province- Iran during 6 month from April to September 2021. PARTICIPANTS: All 14355 patients who were hospitalized with confirmed COVID-19 in hospitals of Qazvin Province. INTERVENTION: No intervention. MAIN OUTCOME MEASURES: The trends of overall in-hospital mortality and ICU mortality were the main outcome of interest. We obtained crude and adjusted in-hospital and ICU mortality rates for each month of admission and over surge and lull periods of the disease. RESULTS: The overall in-hospital mortality, early mortality and ICU mortality were 8.8%, 3.2% and 67.6%, respectively. The trend for overall mortality was almost plateau ranging from 6.5% in July to 10.7% in April. The lowest ICU mortality was 60.0% observed in April, whereas it reached a peak in August (ICU mortality = 75.7%). Admission on surge days of COVID-19 was associated with an increased risk of overall mortality (Odds ratio = 1.3, 95% confidence interval = 1.1, 1.5). The comparison of surge and lull status showed that the odds of ICU mortality in the surge of COVID-19 was 1.7 higher than in the lull period (P-value < 0.001). CONCLUSIONS: We found that the risk of both overall in-hospital and ICU mortality increased over the surge period and fourth and fifth waves of severe acute respiratory syndrome coronavirus 2 infection in Iran. The lack of hospital resources and particularly ICU capacities to respond to the crisis during the surge period is assumed to be the main culprit.

Predicting COVID-19 Cases Among Nurses Using Artificial Neural Network Approach.

We designed a forecasting model to determine which frontline health workers are most likely to be infected by COVID-19 among 220 nurses. We used multivariate regression analysis and different classification algorithms to assess the effect of several covariates, including exposure to COVID-19 patients, access to personal protective equipment, proper use of personal protective equipment, adherence to hand hygiene principles, stressfulness, and training on the risk of a nurse being infected. Access to personal protective equipment and training were associated with a 0.19- and 1.66-point lower score in being infected by COVID-19. Exposure to COVID-19 cases and being stressed of COVID-19 infection were associated with a 0.016- and 9.3-point higher probability of being infected by COVID-19. Furthermore, an artificial neural network with 75.8% (95% confidence interval, 72.1-78.9) validation accuracy and 76.6% (95% confidence interval, 73.1-78.6) overall accuracy could classify normal and infected nurses. The neural network can help managers and policymakers determine which frontline health workers are most likely to be infected by COVID-19.

A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit.

Background: Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods: We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results: All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R 2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion: Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

Psychological Impact of the COVID-19 on Hospitalized Patients: A Qualitative Study.

Background: As the 2019 coronavirus spreads rapidly around the world, it has caused widespread fear and anxiety in various populations. This study aimed to explore the psychological effects of COVID-19 on patients with this disease. Materials and Methods: A qualitative study was conducted with a phenomenological approach. A purposive sample of 11 patients with COVID-19 was recruited. Data were collected from the beginning of March to the beginning of June 2020 using semi-structured interviews and they were analyzed according to Van Manen's method. Interviews were audiotaped, transcribed verbatim, and analyzed using thematic analysis. Results: Initially, 315 codes were extracted. During data analysis and comparisons, the codes were reduced to 108. Ultimately, 10 categories, 38 subcategories, and 3 themes emerged. The theme of "behavioral responses" including 5 categories (Remorse, Fear and despair, Death anxiety, Growth, Support), "disease-caused helplessness" including two categories (Failure, Denial), and "decline of social networks" including three categories (Rejection, Stigma, Feeling guilty). Conclusions: After understanding the findings of this research, nurses working in the wards of patients with COVID-19 can better consider the importance of assessing and analyzing the psychological challenges and experiences of these patients during the course of illness and quarantine. Findings also enhance the identification and organization of training needs during such a pandemic and the design of nursing programs to meet them.

Challenges in the clinical education environment during the outbreak of COVID-19: Development and psychometric testing

BACKGROUND: COVID-19 pandemic poses unique physical and emotional challenges in providing clinical education. Failure to identify the challenges and problems that students face in the clinical learning environment hinders their effective learning and growth. Consequently, the progress of their skills is affected. The aim of this study was to develop a challenge in the clinical education environment of medical students during the outbreak of COVID-19 questionnaire and to test its psychometric properties. MATERIALS AND METHODS: This study is part of a larger study that was conducted using a combined consecutive method in Qazvin. In the first stage, a phenomenological study was performed with van Manen's method by interviewing 12 students at Qazvin University. To extract the items of the tool in the second stage, the concept was defined. Ultimately, the psychometric properties of the questionnaire were evaluated with face validity, content validity (quantitative and qualitative), construct validity (exploratory factor analysis), internal consistency (Cronbach's alpha), and test–retest reliability (intraclass correlation coefficient). RESULTS: The initial tool had 70 questions. After validation, 53 items remained in the final questionnaire. Four extracted dimensions were as follows: “Inadequate professional competency,” “Inefficient clinical planning” and “outcomes of learning-teaching activities,” and “the challenges related to the stigma of medical staff.” Cronbach's alpha for the whole questionnaire was 0.98 (range: 0.87–0.98). The test–retest (intraclass correlation coefficient) reliability was 0.98 (P < 0.001). CONCLUSIONS: According to the obtained results, if the items of “Inadequate professional competency,” “Inefficient clinical planning” and “outcomes of learning-teaching activities,” and “the challenges related to the stigma of medical staff,” the challenges of students’ clinical education can be reduced during the COVID-19 outbreak.

Machine learning-based COVID-19 diagnosis by demographic characteristics and clinical data.

INTRODUCTION: To facilitate rapid and effective diagnosis of COVID-19, effective screening can alleviate the challenges facing healthcare systems. We aimed to develop a machine learning-based prediction of COVID-19 diagnosis and design a graphical user interface (GUI) to diagnose COVID-19 cases by recording their symptoms and demographic features. METHODS: We implemented different classification models including support vector machine (SVM), Decision tree (DT), Naïve Bayes (NB) and K-nearest neighbor (KNN) to predict the result of COVID-19 test for individuals. We trained these models by data of 16973 individuals (90% of all individuals included in data gathering) and tested by 1885 individuals (10% of all individuals). Maximum relevance minimum redundancy (MRMR) algorithms used to score features for prediction of result of COVID-19 test. A user-friendly GUI was designed to predict COVID-19 test results in individuals. RESULTS: Study results revealed that coughing had the highest positive correlation with the positive results of COVID-19 test followed by the duration of having COVID-19 signs and symptoms, exposure to infected individuals, age, muscle pain, recent infection by COVID-19 virus, fever, respiratory distress, loss of smell or taste, nausea, anorexia, headache, vertigo, CT symptoms in lung scans, diabetes and hypertension. The values of accuracy, precision, recall, F1-score, specificity and area under receiver operating curve (AUROC) of different classification models computed in different setting of features scored by MRMR algorithm. Finally, our designed GUI by receiving each of the 42 features and symptoms from the users and through selecting one of the SVM, KNN, Naïve Bayes and decision tree models, predict the result of COVID-19 test. The accuracy, AUROC and F1-score of SVM model as the best model for diagnosis of COVID-19 test were 0.7048 (95% CI: 0.6998, 0.7094), 0.7045 (95% CI: 0.7003, 0.7104) and 0.7157 (95% CI: 0.7043, 0.7194), respectively. CONCLUSION: In this study we implemented a machine learning approach to facilitate early clinical decision making during COVID-19 outbreak and provide a predictive model of COVID-19 diagnosis capable of categorizing populations in to infected and non-infected individuals the same as an efficient screening tool.

Challenges in the clinical education environment during the outbreak of COVID-19: Development and psychometric testing.

BACKGROUND: COVID-19 pandemic poses unique physical and emotional challenges in providing clinical education. Failure to identify the challenges and problems that students face in the clinical learning environment hinders their effective learning and growth. Consequently, the progress of their skills is affected. The aim of this study was to develop a challenge in the clinical education environment of medical students during the outbreak of COVID-19 questionnaire and to test its psychometric properties. MATERIALS AND METHODS: This study is part of a larger study that was conducted using a combined consecutive method in Qazvin. In the first stage, a phenomenological study was performed with van Manen's method by interviewing 12 students at Qazvin University. To extract the items of the tool in the second stage, the concept was defined. Ultimately, the psychometric properties of the questionnaire were evaluated with face validity, content validity (quantitative and qualitative), construct validity (exploratory factor analysis), internal consistency (Cronbach's alpha), and test-retest reliability (intraclass correlation coefficient). RESULTS: The initial tool had 70 questions. After validation, 53 items remained in the final questionnaire. Four extracted dimensions were as follows: "Inadequate professional competency," "Inefficient clinical planning" and "outcomes of learning-teaching activities," and "the challenges related to the stigma of medical staff." Cronbach's alpha for the whole questionnaire was 0.98 (range: 0.87-0.98). The test-retest (intraclass correlation coefficient) reliability was 0.98 (P < 0.001). CONCLUSIONS: According to the obtained results, if the items of "Inadequate professional competency," "Inefficient clinical planning" and "outcomes of learning-teaching activities," and "the challenges related to the stigma of medical staff," the challenges of students' clinical education can be reduced during the COVID-19 outbreak.

Prognosis of Patients with Tracheal Intubation in the Emergency Department

Background Intubation of critically ill patients is one of the increasing emergency procedures. we designed this study to determine age and sex-related mortality rates after emergency intubation. Methods This retrospective study collected and analyzed non-trauma intubated patients in a referral hospital from the years 2017 to 2019 and before the appearance of COVID-19. Patients who were intubated outside of emergency by EMS technicians were excluded. We recorded data of intubated patients, like sex, age, length of being intubated and final diagnosis. P values of less than 0.05 were significant. Results Data of 520 non-trauma intubated patients were collected and analyzed. More than 64% of the patients were over 65 years old and had a higher mortality rate (86.7%;P<0.001) than younger patients. The overall in-hospital mortality rate was 80%. More than three quarters of the decedents died within a week of intubation (P<0.001). There was no significant relationship between sex and mortality rate (P=0.535). Conclusion Our data showed that with increased age there was a decrease in the chance of being extubated.

Factors related to students' satisfaction with holding e-learning during the Covid-19 pandemic based on the dimensions of e-learning.

BACKGROUND: E-learners' satisfaction has a significant impact on the success of the e-learning process and leads to improving the quality of the e-learning system. Many factors affect e-learning satisfaction. This study aimed to determine the factors related to students' satisfaction with e-learning during the Covid-19 pandemic based on the dimensions of e-learning. METHODS: The present study was a cross-sectional study, which was conducted in 2020 among students studying in different fields of Qazvin University of Medical Sciences using stratified random sampling. To collect data three parts of questionnaires were used included the demographic information, the measuring the effectiveness of e-learning, and measuring the level of satisfaction with holding e-learning during the Covid-19 period. Data were entered into spss23 and analyzed by descriptive method, chi-square, and t-test. RESULTS: The results showed that the mean (standard deviation) score of satisfaction with e-learning in the students was 20.75 (2.13) and 59 % of them had undesirable satisfaction. There was a significant relationship between satisfaction with e-learning and variables of gender and history of attending online classes before Covid-19. Regarding the four aspects of e-learning, there was a statistically significant difference between the two groups of students with desirable satisfaction and undesirable satisfaction. The results revealed that the mean scores of dimensions of teaching and learning; feedback and evaluation; flexibility and appropriateness; and workload among students with desirable satisfaction were higher than students with undesirable satisfaction. CONCLUSION: Considering the results, efforts should be made to improve the quality of e-learning and the factors affecting it, because due to the prevalence of Covid-19, distance education may be held for a long time. Lack of attention to these cases can reduce the quality of education and students' level of knowledge.

Lived Experiences of the Patients Hospitalized with COVID-19: A Phenomenological Study in a Province of Northwest Iran

COVID-19 is a serious infectious disease whose rapid and widespread spread urged the World Health Organization to declare it a global public health emergency. Understanding the experience of people infected and quarantined with COVID-19 is very important in maximizing disease control and minimizing the negative effects on patients, their families, and social networks. This study explored the experiences of patients with COVID-19 during care and quarantine in northwest Iran. A purposive sample of 11 patients with COVID-19 was recruited. Data were collected from the beginning of March to the beginning of June 2020, using semi-structured interviews and these were analyzed according to van Manen's phenomenological method. Interviews were audiotaped, transcribed verbatim and analyzed using thematic analysis. Ultimately, four themes, Characteristics of the experience, Response to traumatic experience, Deprivation, and Confusion, and containing 19 sub-themes, emerged. After understanding the findings of this research, nurses working in the wards of patients with COVID-19 can better consider the importance of assessing and analyzing the challenges and experiences of patients during periods of illness and quarantine. Findings also enhance the identification and organization of training needs during such a pandemic and the design of nursing programs to respond to them.

Corrigendum to "Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak" [Addict. Behav. Rep. 12 (2020) 100307].

[This corrects the article DOI: 10.1016/j.abrep.2020.100307.].