COVID-19 and seasonal influenza

Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics

Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission.

MATERIALS AND METHODS

In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm.

RESULTS

The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients.

CONCLUSIONS

These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.

A fatal case of COVID-19-associated meningoencephalitis in a patient coinfected with influenza A

We report a case of COVID-19-associated meningoencephalitis with a fatal outcome in a male patient with concomitant influenza A, who had been hospitalized at the beginning of 2022, in the Northeastern region of Brazil. He died due to cardiopulmonary arrest after developing status epilepticus on the third day of hospitalization. The SARS-CoV-2 RNA was detected in cerebrospinal fluid and Influenza A was detected in the nasopharyngeal swab. Meningoencephalitis due to COVID-19 is a rare manifestation and physicians must be aware of this complication, mainly during the pandemic. In viral co-circulation situations, the possibility of respiratory coinfections should be remembered.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions

Background

Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA).

Results

The 2D-QSAR modeling results showed GFA-MLR ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9192, Q2 = 0.8767, R2adj = 0.8991, RMSE = 0.0959, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8943, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7745) and GFA-ANN ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9227, Q2 = 0.9212, RMSE = 0.0940, $$R_{{{\text{test}}}}^{2}$$ R test 2  = 0.8831, $$R_{{{\text{pred}}}}^{2}$$ R pred 2  = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.9620, Q2 = 0.643) and CoMSIA_SED ($$R_{{\text{train }}}^{2}$$ R train 2  = 0.8770, Q2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> − 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents.

Conclusion

The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski’s rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

Cardiac auscultation predicts mortality in elderly patients admitted for COVID-19

INTRODUCTION

COVID-19 has had a great impact on the elderly population. All admitted patients underwent cardiac auscultation at the Emergency Department. However, to our knowledge, there is no literature that explains the implications of cardiac auscultation at the Emergency Department.

MATERIAL AND METHODS

Data collection from our hospital records. Our cohort consists of 300 admissions with a mean age of 81.6 years and 50.7% men.

RESULTS

Pathological cardiac auscultation at the Emergency Department was a risk factor for in-hospital mortality (RR = 1.9; 95% CI 1.3-2.8), heart failure (RR = 3.2; 95% CI = 1.8-5.6), respiratory failure (RR = 1.8; 95% CI = 1.3-2.5), acute kidney injury (RR = 2.6; 95% CI = 2-3.2), and ICU admission (RR = 3.3; 95% CI = 1.3-8.2). The findings in patients with pathological cardiac auscultation were that oxygen saturation in the Emergency Department, arterial pH, and HCO3^- were significantly lower, and the ALT/GPT, LDH, and lactate determinations were significantly higher, which is compatible and correlates with the fact that the main variable is indeed a risk factor for a more severe clinical course. Among the findings from pathological auscultation, arrhythmic tone/arrhythmia was the most frequent (50%) and a risk factor for in-hospital mortality (RR = 2.3; 95% CI = 1.6-3.4). Logistic regression was performed from a multivariate analysis that showed that the initial ex novo arrhythmia correlated with pathological cardiac auscultation is an independent risk factor for in-hospital mortality.

CONCLUSION

Continuous rhythm monitoring makes it possible to detect ex novo arrhythmias and act proactively, and to offer greater care and attention to these patients who have a higher risk of in-hospital mortality and a worse prognosis. Cardiac auscultation can alert us in order to perform more electrocardiograms in these patients and thus have better monitoring.