Prognosis of COVID-19 patients using lab tests: A data mining approach.

Background: The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques. Methods: In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results: Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion: Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.

The impact of HIV on the risk of COVID-19 death among hospitalized patients.

BACKGROUND: Little is known about the association between Human Immunodeficiency Virus (HIV) infection and risk of death among hospitalized COVID-19 patients. We aimed to investigate this association using a multicenter study. MATERIAL AND METHODS: This multicenter study was conducted using the registry database of Coronavirus Control Operations Headquarter from March 21, 2021 to January 18, 2020 in the province of Tehran, Iran. The interest outcome was COVID-19 death among hospitalized patients living with and without HIV. The Cox regression models with robust standard error were used to estimate the association between HIV infection and risk of COVID-19 death. The subgroup and interaction analysis were also performed in this study. RESULTS: 326052 patients with COVID-19 were included in the study, of whom 127 (0.04%) were living with HIV. COVID-19 patients with HIV were more likely to be female, older, and to have symptoms such as fever, muscular pain, dyspnea and cough. The death proportion due to COVID-19 was 18 (14.17%) and 21595 (6.63%) among HIV and non-HIV patients, respectively. Patients living with HIV had lower mean survival time compared to those without HIV (26.49 vs. 15.31 days, P-value = 0.047). Crude risk of COVID-19 death was higher among HIV patients than in non-HIV group (hazard ratio[HR]: 1.60, 1.08-2.37). Compared to those without HIV, higher risk of COVID-19 death was observed among patients with HIV after adjusting for sex (1.60, 1.08-2.36), comorbidities (1.49, 1.01-2.19), cancer (1.59, 1.08-2.33), and PO2 (1.68, 1.12-2.50). However, the risk of COVID-19 death was similar in patients with and without HIV after adjusting for age (1.46, 0.98-2.16) and ward (1.30, 0.89-1.89). CONCLUSION: We found no strong evidence of association between HIV infection and higher risk of COVID-19 death among hospitalized patients. To determine the true impact of HIV on the risk of COVID-19 death, factors such as age, comorbidities, hospital ward, viral load, CD4 count, and antiretroviral treatment should be considered.

Mortality among hospitalized COVID-19 patients during surges of SARS-CoV-2 alpha (B.1.1.7) and delta (B.1.617.2) variants.

The aim of this study was to evaluate the death proportion and death risk of COVID-19 hospitalized patients over time and in different surges of COVID-19. This multi-center observational study was conducted from March 21, 2021 to October 3, 2021 which included the alpha and delta SARS-CoV-2 surges occurred in April and August in Tehran, respectively. The risk of COVID-19 death was compared in different months of admission. A total of 270,624 patients with COVID-19, of whom 6.9% died, were admitted to hospitals in Tehran province. Compared to patients admitted in March, a higher risk of COVID-19 death was observed among patients admitted to the hospital in July (HR 1.28; 95% CI 1.17, 1.40), August (HR 1.40; 95% CI 1.28, 1.52), September (HR 1.37; 95% CI 1.25, 1.50) and October (HR 4.63; 95% CI 2.77, 7.74). The ICU death proportion was 36.8% (95% CI: 35.5, 38.1) in alpha surge and increased significantly to 39.8 (95% CI 38.6, 41.1) in delta surge. The risk of COVID-19 death was significantly higher in delta surge compared to alpha surge (HR 1.22; 95% CI 1.17, 1.27). Delta surge was associated with a higher risk of death compared to alpha surge. High number of hospitalizations, a shortage of hospital beds, ICU spaces and medical supplies, poor nutritional status of hospitalized patients, and lack of the intensivist physicians or specialized nurses in the ICU were factors that contributed to the high mortality rate in the delta surge in Iran.

Efficacy of the barley-based remedy, a Persian medicine formula, in coronavirus disease 2019 (COVID-19) hospitalized patients: An open-labeled randomized controlled trial.

Background: With the pandemic of coronavirus disease 2019 (COVID-19), and the growing attention of people around the world to the use of traditional and complementary medicines to control of the disease, evaluating the effectiveness of these treatments has received special attention. Aim: This study aimed to assess the clinical efficacy of a barley-based (Hordeum vulgare) remedy combined with conventional medicine in comparison to the conventional therapy in confirmed COVID-19 patients. Materials and methods: Seventy COVID-19 patients were randomly divided into barley-based remedy plus conventional medicine (barley-based remedy group) and conventional therapy (control group). Both groups were treated for 5 days. The outcomes were oxygen saturation, main symptoms (fever, respiratory rate, cough, and fatigue), and laboratory data (lymphocytic count, and CRP); they were measured for 6 days. Results: In comparison to the control group, the oxygen saturation level in the barley-based remedy group significantly increased, from the second day of the intervention (P < 0.05). The herbal remedy significantly improved fatigue from the third day (P < 0.05). Meanwhile, the severity and frequency of cough between the groups were not significantly different. The herbal remedy had no significant effect on the CRP and the lymphocytic count of every time points of measurement. The average of respiratory rate and temperature of patients were in the normal range in both groups during the intervention. Conclusion: Barley-based remedy could significantly enhance the blood oxygen saturation and reduce fatigue. However, it needs to be confirmed by large sample size trials.

Baseline Characteristics and Associated Factors of Mortality in COVID-19 Patients;an Analysis of 16000 Cases in Tehran, Iran

Introduction: Given the importance of evidence-based decision-making, this study aimed to evaluate epidemiological and clinical characteristics as well as associate factors of mortality among admitted COVID-19 cases. Methods: This multicenter, cross-sectional study was conducted on confirmed and suspected COVID-19 cases who were hospitalized in 19 public hospitals affiliated to Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran, between February 19 and May 12, 2020. Epidemiological and clinical characteristics of the infected cases were compared between the deceased and survivors after discharge. Case fatality rates (CFRs) were calculated across all study variables. Single and multiple logistic regressions were used to explore the risk factors associated with COIVD-19 mortality. Results: Out of the 16035 cases that referred to the hospitals affiliated to SBMU, 16016 patients (99.93% of Confirmed and 99.83% of suspected cases) were hospitalized. 1612 patients died with median hospitalization days of 5 (interquartile range (IQR): 2-9) and 3 (1-7) for confirmed and suspected COVID-19 cases, respectively. The highest death rate was observed among ages>65 (63.4% of confirmed cases, 62.3% of suspected cases) and intensive care unit (ICU)/critical care unit (CCU) patients (62.7% of confirmed cases, 52.2% of suspected cases). Total case fatality rate (CFR) was 10.05% (13.52% and 6.37% among confirmed and suspected cases, respectively). The highest total CFR was observed in patients with age>65 years (25.32%), underlying comorbidities (25.55%), and ICU/CCU patients (41.7%). The highest CFR was reported for patients who had diabetes and cardiovascular diseases (38.46%) as underlying non-communicable diseases (NCDs), and patients with cancer (35.79%). Conclusion: This study showed a high CFR among suspected and confirmed COVID-19 cases, and highlighted the main associated risk factors including age, sex, underlying NCDs, and ICU/CCU admission affecting survival of COVID-19 patients.