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.

Evaluation of pharmacist-led educational interventions to reduce medication errors in emergency hospitals: a new insight into patient care.

BACKGROUND: Medication errors made by nurses are common in general practice and can lead to harm in patients. The aim of this study was to evaluate the impact of pharmacist-led educational implementations in reducing medication errors made by nurses in an emergency hospital in Cairo, Egypt. METHODS: A prospective pre-post-interventional study was conducted in an emergency hospital using direct observation for the detection of errors. The rate and severity of medication errors were determined before and after the implementation of educational tools. RESULTS: In total, 1025 and 1024 patients were examined pre- and post-intervention, respectively. Pharmacist interventions resulted in a significant reduction in the medication error rate from 351 (34.2%) in the pre-intervention phase to 157 (15.3%) in the post-intervention phase (P < 0.001). In both the pre- and post-intervention phases, none of the medication errors were associated with harm/death. Furthermore, all types of medication errors declined as a result of the interventions. CONCLUSION: Clinical pharmacists' interventions focusing on improving nurses' drug knowledge and awareness of errors were shown to be effective in reducing the rate and severity of medication administration errors among nurses in an emergency hospital environment.