Impact of Aspergillus spp. isolation in the first 24 hours of admission in critically ill patients with severe influenza virus pneumonia

A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective

Background: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon ICU admission. Methods: We conducted a secondary analysis of two prospective multicenter cohort studies with confirmed influenza A (H1N1)pdm09 and COVID-19. Multiple logistic regression (MLR) and random forest (RF) were used to identify factors associated with BFC in the overall population and in each subgroup (influenza and COVID-19). The performance of these models was assessed by the area under the ROC curve (AUC) and out-of-bag (OOB) methods for MLR and RF, respectively. Results: Of the 8902 patients, 41.6% had influenza and 58.4% had SARS-CoV-2 infection. The median age was 60 years, 66% were male, and the crude ICU mortality was 25%. BFC was observed in 14.2% of patients. Overall, the predictive models showed modest performances, with an AUC of 0.68 (MLR) and OOB 36.9% (RF). Specific models did not show improved performance. However, age, procalcitonin, CRP, APACHE II, SOFA, and shock were factors associated with BFC in most models. Conclusions: Machine learning models do not adequately predict the presence of co-infection in critically ill patients with pandemic virus infection. However, the presence of factors such as advanced age, elevated procalcitonin or CPR, and high severity of illness should alert clinicians to the need to rule out this complication on admission to the ICU.

Viral Pneumonia: From Influenza to COVID-19

Respiratory viruses are increasingly recognized as a cause of community-acquired pneumonia (CAP). The implementation of new diagnostic technologies has facilitated their identification, especially in vulnerable population such as immunocompromised and elderly patients and those with severe cases of pneumonia. In terms of severity and outcomes, viral pneumonia caused by influenza viruses appears similar to that caused by non-influenza viruses. Although several respiratory viruses may cause CAP, antiviral therapy is available only in cases of CAP caused by influenza virus or respiratory syncytial virus. Currently, evidence-based supportive care is key to managing severe viral pneumonia. We discuss the evidence surrounding epidemiology, diagnosis, management, treatment, and prevention of viral pneumonia.