Antibiotic use during the first 6 months of COVID-19 pandemic in Iran: A large-scale multi-centre study.

WHAT IS KNOWN AND OBJECTIVE: Although antibiotics are ineffective against viral infections, epidemiological studies have revealed that the COVID-19 pandemic resulted in the overuse of antibiotics and disruption of antimicrobial stewardship programmes. We investigated the pattern of antibiotic use during the first 6 months of the COVID-19 pandemic in Iran. METHODS: A multi-centre retrospective study was designed to investigate the use of 16 broad-spectrum antibiotics in 12 medical centres. The rate of antibiotic use was calculated and reported based on the Defined Daily Dose (DDD) per 100 hospital bed-days. The bacterial co-infection rate was also reported. RESULTS AND DISCUSSION: Totally, 43,791 hospitalized COVID-19 patients were recruited in this study. It was found that 121.6 DDD of antibiotics were used per 100 hospital bed-days, which estimated that each patient received approximately 1.21 DDDs of antibiotics every day. However, the bacterial co-infections were detected only in 14.4% of the cases. A direct correlation was observed between the rate of antibiotic use and mortality (r[142] = 0.237, p = 0.004). The rate of antibiotic consumption was not significantly different between the ICU and non-ICU settings (p = 0.15). WHAT IS NEW AND CONCLUSION: In this study, widespread antibiotic use was detected in the absence of the confirmed bacterial coinfection in COVID-19 patients. This over-consumption of broad-spectrum antibiotics may be associated with increased mortality in hospitalized COVID-19 patients, which can be an alarming finding.

Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning.

Statins can help COVID-19 patients' treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients' severity based on clinical features. Results indicated that the decision tree method could predict patients' illness severity with 87.9% accuracy. Other methods, including the K-nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.