Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

PURPOSE: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction. METHODS: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models. RESULTS: Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873). CONCLUSION: Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

The role of lung ultrasound in procalcitonin-guided antibiotic discontinuation in ventilator-associated pneumonia.

Background and Aims: Starting antibiotic therapy at the proper time is the cornerstone of the management of ventilator-associated pneumonia (VAP). However, using antibiotics for a long duration of therapy in intensive care leads to increased bacterial resistance, financial burden and adverse drug reactions. We hypothesised that lung ultrasound may have a role in guiding antibiotic discontinuation in patients with VAP that will help to reduce the antibiotic duration and decrease the resistance. Methods: This was a prospective blinded cohort study from October 2020 to September 2021 in which 62 VAP patients were recruited. Antibiotics were started, procalcitonin (PCT) level and clinical pulmonary infection score were estimated and lung ultrasound (US) was performed on day 1 and repeated on day 7. On day 7, discontinuation of antibiotics was recommended if the PCT level was <0.25 µg/L. A lung reaeration score was recorded. Results: Based on the PCT levels, antibiotics were discontinued in 40 patients. The computed tomography findings of VAP had improved in all. The ultrasound reaeration score showed a highly significant negative correlation with the PCT on day 7 (-0.718, P < 0.001). A cut-off of 5 for the US score showed a sensitivity of 92.5%, specificity of 95.5%, positive predictive value of 97.4% and negative predictive value of 87.5% in detecting a low PCT score on day 7. Conclusion: Lung US is a non-invasive and safe method that can be used to guide antibiotic therapy in VAP.

Kinetic and Thermodynamics studies for Castor Oil Extraction Using Subcritical Water Technology.

In this work both kinetic and thermodynamics of castor oil extraction from its seeds using subcritical water technique were studied. It was found that the extraction process followed two consecutive steps. In these steps, the oil was firstly extracted from inside the powder by diffusion mechanism. Then the extracted oil, due to extending the extraction time under high temperature and pressure, was subjected to a decomposition reaction following first order mechanism. The experimental data correlated well with the irreversible consecutive unimolecular-type first order mechanism. The values of both oil extraction rate constants and decomposition rate constants were calculated through non-linear fitting using DataFit software. The extraction rate constants were found to be 0.0019, 0.024, 0.098, 0.1 and 0.117 min(-1), while the decomposition rate constants were 0.057, 0.059, 0.014, 0.019 and 0.17 min(-1) at extraction temperatures of 240, 250, 260, 270 and 280°C, respectively. The thermodynamic properties of the oil extraction process were investigated using Arrhenius equation. The values of the activation energy, Ea, and the frequency factor, A, were 73 kJ mol(-1) and 946, 002 min(-1), respectively. The physicochemical properties of the extracted castor oil including the specific gravity, viscosity, acid value, pH value and calorific value were found to be 0.947, 7.487, 1.094 mg KOH/g, 6.1, and 41.5 MJ/Kg, respectively. Gas chromatography analysis showed that ricinoleic acid (83.6%) appears as the predominant fatty acid in the extracted oil followed by oleic acid (5.5%) and linoleic acid (2.3%).