One year of COVID-19 pandemic: what we Radiologists have learned about imaging

Analysis of the Effectiveness of Nursing Interventions in Critically Ill Patients in Respiratory Medicine

OBJECTIVE

To enhance and analyze the clinical effectiveness of implementing quality nursing interventions in the clinical care of critically ill patients in respiratory medicine.

METHODS

Clinical data of respiratory medicine patients treated in our hospital over the years were collected and 96 patients who met the requirements of the purpose of this study and the sample inclusion criteria were selected as the study subjects from the patients treated between April 2019 and January 2020. According to the care methods received by the patients in our hospital, 48 of them who implemented conventional care were used as the control group, and another 48 patients who were given quality care interventions were used as the observation group. The data were statistically recorded and comparatively analyzed for the indicators such as nursing oxygen index, heart rate, and clinical treatment efficiency of patients in both groups.

RESULTS

Compared with the control group, patients in the observation group who received quality nursing intervention had more significant improvement in blood oxygen index and heart rate after nursing care; the clinical treatment efficiency of patients in the observation group was significantly higher than that of the control group (95.83% vs. 81.25%). The data comparison between the groups showed a significant difference, P < 0.05, which was statistically significant.

CONCLUSION

Adding quality nursing interventions to the implementation of conventional care for patients with respiratory diseases can better improve patients' clinical symptoms, accelerate their clinical recovery, improve and enhance prognosis, and further improve clinical outcomes.

A Deep Learning Approach to Identify Chest Computed Tomography Features for Prediction of SARS-CoV-2 Infection Outcomes

There is still an urgent need to develop effective treatments to help minimize the cases of severe COVID-19. A number of tools have now been developed and applied to address these issues, such as the use of non-contrast chest computed tomography (CT) for evaluation and grading of the associated lung damage. Here we used a deep learning approach for predicting the outcome of 1078 patients admitted into the Baqiyatallah Hospital in Tehran, Iran, suffering from COVID-19 infections in the first wave of the pandemic. These were classified into two groups of non-severe and severe cases according to features on their CT scans with accuracies of approximately 0.90. We suggest that incorporation of molecular and/or clinical features, such as multiplex immunoassay or laboratory findings, will increase accuracy and sensitivity of the model for COVID-19 -related predictions.