Background Previous studies have shown that non-critically ill COVID-19 patients co-infected with other respiratory viruses have poor clinical outcomes. However, limited studies focused on this co-infections in critically ill patients. This study aims to evaluate the clinical outcomes of critically ill patients infected with COVID-19 and co-infected by other respiratory viruses. Methods A multicenter retrospective cohort study was conducted for all adult patients with COVID-19 who were hospitalized in the ICUs between March, 2020 and July, 2021. Eligible patients were sub-categorized into two groups based on simultaneous co-infection with other respiratory viruses throughout their ICU stay. Influenza A or B, Human Adenovirus (AdV), Human Coronavirus (i.e., 229E, HKU1, NL63, or OC43), Human Metapneumovirus, Human Rhinovirus/Enterovirus, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), Parainfluenza virus, and Respiratory Syncytial Virus (RSV) were among the respiratory viral infections screened. Patients were followed until discharge from the hospital or in-hospital death. Results A total of 836 patients were included in the final analysis. Eleven patients (1.3%) were infected concomitantly with other respiratory viruses. Rhinovirus/Enterovirus (38.5%) was the most commonly reported co-infection. No difference was observed between the two groups regarding the 30-day mortality (HR 0.39, 95% CI 0.13, 1.20; p = 0.10). The in-hospital mortality was significantly lower among co-infected patients with other respiratory viruses compared with patients who were infected with COVID-19 alone (HR 0.32 95% CI 0.10, 0.97; p = 0.04). Patients concomitantly infected with other respiratory viruses had longer median mechanical ventilation (MV) duration and hospital length of stay (LOS). Conclusion Critically ill patients with COVID-19 who were concomitantly infected with other respiratory viruses had comparable 30-day mortality to those not concomitantly infected. Further proactive testing and care may be required in the case of co-infection with respiratory viruses and COVID-19. The results of our study need to be confirmed by larger studies.

Neural networks have several useful applications in machine learning. However, benefiting from the neural-network architecture can be tricky in some instances due to the large number of parameters that can influence performance. In general, given a particular dataset, a data scientist cannot do much to improve the efficiency of the model. However, by tuning certain hyperparameters, the model’s accuracy and time of execution can be improved. Hence, it is of utmost importance to select the optimal values of hyperparameters. Choosing the optimal values of hyperparameters requires experience and mastery of the machine learning paradigm. In this paper, neural network-based architectures are tested based on altering the values of hyperparameters for handwritten-based digit recognition. Various neural network-based models are used to analyze different aspects of the same, primarily accuracy based on hyperparameter values. The extensive experimentation setup in this article should, therefore, provide the most accurate and time-efficient solution models. Such an evaluation will help in selecting the optimized values of hyperparameters for similar tasks.

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