Risk factors of developing critical conditions in Iranian patients with COVID-19

Clinical Characteristics and Outcomes in Hospitalized Patients with COVID-19 and Cancer History: A Multicenter Cross-Sectional Study in Southwestern Iran

Background: Cancer patients are more exposed to opportunistic infections, such as COVID-19, due to their poor health status. This study aimed to identify the clinical characteristics of cancer and non-cancer patients with COVID-19 that may lead to death, intubation, and ICU admission. Materials and Methods: A Multicenter Cross-Sectional study was conducted on confirmed COVID-19 adult patients with and without a history of cancer from March 2019 to March 2021. Demographic and clinical features, ICU admission, intubation, and discharge status have been extracted from patients' medical records. Chi-square, odds ratio, Mann-Whitney test, and logistic regression were used to analyze the data. Results: The death rate in 1332 cancer patients was 28% compared to the 91464 noncancer patients which was 9% with an odds ratio of 3.94 and p<0.001. ICU admission rates among the cancer group were 43%, while in the noncancer group, it was 17.9% (p<0.001). Moreover, intubation was done for 20.9% of cancer patients and 7.4% of non-cancer patients (p<0.001). However, no significant difference was observed between the two groups in terms of length of stay in the hospital. Multivariable logistic regression analysis showed that age, level of consciousness, SPO2, and autoimmune disorders were associated with mortality in cancer patients with COVID-19. Conclusion: This study showed that older age, loss of consciousness, low oxygen saturation, and suffering from autoimmune disorders were the predictors of death in cancer patients with COVID-19. These results can have important implications for the management and care of cancer patients with COVID-19.

Adjustment for collider bias in the hospitalized Covid-19 setting

Background

Causal directed acyclic graphs (cDAGs) are frequently used to identify confounding and collider bias. We demonstrate how to use causal directed acyclic graphs to adjust for collider bias in the hospitalized Covid-19 setting.

Materials and methods

According to the cDAGs, three types of modeling have been performed. In model 1, only vaccination is entered as an independent variable. In model 2, in addition to vaccination, age is entered the model to adjust for collider bias due to the conditioning of hospitalization. In model 3, comorbidities are also included for adjustment of collider bias due to the conditioning of hospitalization in different biasing paths intercepting age and comorbidities.

Results

There was no evidence of the effect of vaccination on preventing death due to Covid-19 in model 1. In the second model, where age was included as a covariate, a protective role for vaccination became evident. In model 3, after including chronic diseases as other covariates, the protective effect was slightly strengthened.

Conclusion

Studying hospitalized patients is subject to collider-stratification bias. Like confounding, this type of selection bias can be adjusted for by inclusion of the risk factors of the outcome which also affect hospitalization in the regression model.

Relationship between clinical-epidemiological parameters and outcomes of patients with COVID-19 admitted to the intensive care unit: a report from a Brazilian hospital

Background

People in low-income countries, especially those with low socio-economic conditions, are likelier to test positive for SARS-CoV-2. The unequal conditions of public health systems also increase the infection rate and make early identification and treatment of at-risk patients difficult. Here, we aimed to characterize the epidemiological profile of COVID-19 patients in intensive care and identify laboratory and clinical markers associated with death.

Materials and methods

We conducted an observational, descriptive, and cross-sectional study in a reference hospital for COVID-19 treatment in the Southern Region of Bahia State, in Brazil, to evaluate the epidemiological, clinical, and laboratory characteristics of COVID-19 patients admitted to the intensive care unit (ICU). Additionally, we used the area under the curve (AUC) to classify survivors and non-survivors and a multivariate logistic regression analysis to assess factors associated with death. Data was collected from the hospital databases between April 2020 and July 2021.

Results

The use of bladder catheters (OR 79.30; p < 0.0001) and central venous catheters (OR, 45.12; p < 0.0001) were the main factors associated with death in ICU COVID-19 patients. Additionally, the number of non-survivors increased with age (p < 0.0001) and prolonged ICU stay (p < 0.0001). Besides, SAPS3 presents a higher sensibility (77.9%) and specificity (63.1%) to discriminate between survivors and non-survivor with an AUC of 0.79 (p < 0.0001).

Conclusion

We suggest that multi-laboratory parameters can predict patient prognosis and guide healthcare teams toward more assertive clinical management, better resource allocation, and improved survival of COVID-19 patients admitted to the ICU.

Risk Factors for In-Hospital Mortality among Patients with Coronavirus-19 in Isfahan City, Iran

Background

The aim of the study is to explore the risk factors of mortality for hospitalized patients in three designated hospitals in Isfahan province.

Materials and Methods

This retrospective cohort study was conducted on all positive coronavirus disease (COVID)-19 patients admitted to Khorshid, Isabn Maryam, and Amin hospitals in Isfahan province. The demographic, clinical, laboratory, and outcome data of patients who were died or discharged from February 24, 2020, to April 18, 2020, were extracted from patient's medical records.

Results

Overall 1044 COVID-19 patients were included in this analysis. Based on the findings of this study, older age (≥65 years) (adjusted hazard ratio [aHR]: 2.06; 95% confidence interval [CI]: 1.13-3.76), chronic obstructive pulmonary disease (COPD) history (aHR: 2.52; 95% CI: 1.09-5.83), white blood cell (WBC) counts more than 10 × 10^3/L (aHR: 3.05; 95% CI: 1.42-6.55), Hb level <13 gr/L (aHR: 2.82; 95% CI: 1.34-5.93), bilateral pulmonary infiltrates (aHR: 2.02; 95% CI: 1.12-3.64) at admission, development of acute respiratory distress syndrome (ARDS) (aHR: 1.87; 95% CI: 1.01-3.47), and intensive care unit (ICU) admission (aHR: 2.09; 95% CI: 1.04-4.18) during hospitalization were risk factors for in-hospital mortality in patients with COVID-19.

Conclusions

Multiple factors were found related to the severity and death among COVID-19 patients. We were found that older age (≥65 years) with COPD history, high level of WBC, low level of Hb (<13 g/L), bilateral pulmonary infiltrates at admission, development of ARDS, and ICU admission during hospitalization were identified as risk factors of death among COVID-19 patients. More related studies are needed in the future.

Socio-demographical Profile of 7285 SARS-Cov-2 Positive Early Cases; Comparison with National Four Epidemic Waves

Background: Objectives were to investigate aspects of the COVID-19 epidemics via testing the individuals who were referred to Aramesh Medical Laboratory in Tehran and to integrate the molecular results with epidemiological data since the beginning of the epidemic. Methods: In this cross-sectional Study 77528 outpatients were referred to Aramesh Medical laboratory by physicians for the diagnosis of SARS-CoV-2 infection between March 2019 and May 2021. Viral acid nucleic extracted from nasal and throat specimens and subsequently amplified using Reverse Transcriptase Real-Time PCR. Laboratory data including Ct values compared with epidemic peaks of COVID-19 countrywide. Statistical Analysis was done by SPSS 21 Software. Results: 14312 (18.46%) tested positive.36.5% of the positive cases were in the 30 to 39 years old age group. The positive result rate was significantly different based on months, ranging from 6% to 28%, compatible with four recognized epidemic peaks encompassing the end of March through the first week of April (first epidemic peak), from June to July 2020 (second epidemic peak), October until mid of November 2020 (third epidemic wave) followed by the end of April to May 2021 (until the end period of study, in the middle of 4th peak). In 37.8% of cases, the Ct value was between 21 and 28. Two separate trends were seen for Ct ≤ 25 and Ct ≤ 20 for the first and fourth epidemic peaks, respectively. There was an association between the number of total monthly positive results and total deaths in the country, especially with the second to third peaks (in the course of summer 2020) and fourth epidemic peak. Conclusion: It might be useful to consider laboratory admission rates as an indicator for changes in the epidemic level in the country to continue the SARS-CoV-2 surveillance in accordance with public decision-makers.

Misconceptions and associated factors of COVID-19 infection among internally displaced persons in Sudan

Coronavirus disease 2019 (COVID-19) is a global public health threat that has spread rapidly and caused morbidity and mortality worldwide. Reducing the myths about infectious diseases is vital for controlling transmission. This study explored the level of misconceptions and associated factors of COVID-19 among internally displaced persons in Sudan. This study is a cross-sectional, descriptive design and community-based study. We collected the data using a self-administered questionnaire via the convenience sampling technique among internally displaced persons in the camps of Zalingei town in the central Darfur region of Sudan. The total mean score of the respondents’ misconception was 3.1725 (SD=0.59) with 63.2%, indicating moderate misunderstanding of COVID- 19. Multiple linear regression revealed the independent variables together had a significant impact on a misconception, F(14,116)=2.429, p<0.005. The regression model explains 22.7% of the variance in misunderstanding. Analysis of the influence of single factors on the dependent variable showed that people aged 31–40 years had significantly higher levels of misconception, 0.381 (t=2.116, p<0.037), than those aged over 60 years, and university graduates had considerably lower levels of misunderstanding, −0.061 (t=−2.091, p<0.03) than non-graduates. This study found a moderate level of misconception of COVID-19. Non-graduates had higher levels of misunderstanding than graduates. The results suggest that an education campaign should focus on people with low levels of education to correct their misconceptions regarding the prevention of COVID-19 infection.

Prevalence and outcome of COVID-19 among Iranian celiac patients

Aim

This study aimed to evaluate the prevalence and outcome of COVID-19 among Iranian celiac disease patients.

Background

Patients with celiac disease (CD) might be at greater risk for opportunistic viral infections. Coronavirus disease-2019 (COVID-19) is a new coronavirus (SARS-CoV-2) cause of respiratory disorder which spread around the world at the end of 2019. The question is does COVID-19 infection increase the risk of severe outcome and/or a higher mortality in treated celiac disease?.

Methods

Data regarding demographic details, clinical history, and COVID-19 infection symptoms among treated celiac disease patients was collected from July 2020 to January 2021 and analyzed using SPSS version 25.

Results

A total of 455 celiac disease patients were included in this study. The prevalence of Covid-19 infection among celiac disease patients was 2.4%. Infection among women (72.7%) was higher than the men, and only one overweight man who smoked was hospitalized. Among COVID-19 infected celiac disease patients, the most common symptoms were myalgia 90.9% (10/11), fever, body trembling, headache, shortness of breath, loss of smell and taste, and anorexia (72.7%). Treatments for COVID-19, included antibiotics (90.9%), pain analgesics (54.5%), antihistamines (27.3%), antivirals (9.1%) and hydroxychloroquine (9.1%).

Conclusion

This study shows that treated celiac disease is not a risk factor for severity or higher mortality in patients infected with COVID-19. Women, however, might need extra-protection to prevent COVID-19 infection.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.