ABSTRACT
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.
Subject(s)
COVID-19 , Male , Humans , Middle Aged , Aged , Female , Retrospective Studies , SARS-CoV-2 , Hospitals, University , Egypt , Hospital MortalityABSTRACT
The global concern of increasing number of children presenting with multisystem inflammatory syndrome in children (MIS-C) related to the coronavirus disease (COVID-19) has escalated the need for a case-oriented clinical approach that provides timely diagnosis and management. The aim of this study is to share our experience in managing 64 MIS-C patients of North African ethnicity guided by a risk-based algorithm. Sixty-four patients met the inclusion criteria, 19 (30%) patients were categorized as mild and moderate risk groups and cared for in an isolation ward and 45 patients who belonged to the high-risk group (70%) were admitted to the pediatric intensive care unit (PICU). Positive laboratory evidence of COVID-19 was found in 62 patients. Fever and dysfunction in 2 or more organs were confirmed in all cases (100%). Fifty patients (78%) presented with gastrointestinal symptoms, meanwhile only 10 patients (16%) had respiratory manifestations. Cardiac involvement was reported in 55 (86%) cases; hypotension and shock were found in 45 patients (70%) therein circulatory support and mechanical ventilations were needed for 45 and 13 patients respectively. Intravenous immunoglobulins (IVIG) were used for all cases and methylprednisolone was used in 60 patients (94%). Fifty-eight (91%) patients were discharged home after an average of 9 days of hospitalization. The mortality rate was 9% (6 patients). Conclusion. A single Egyptian center experience in the management of MIS-C patients guided by a proposed bed side algorithm is described. The algorithm proved to be a helpful tool for first-line responders, and helped initiate early treatment with IVIG.
ABSTRACT
BACKGROUND: Coronavirus disease-2019 (COVID-19) could be associated with morbidity and mortality in immunocompromised children. OBJECTIVE: The objective of this study was to measure the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among hospitalized children with cancer and to detect the associated clinical manifestations and outcomes. METHODOLOGY: A prospective noninterventional study including all hospitalized children with cancer conducted between mid-April and mid-June 2020 in Ain Shams University Hospital, Egypt. Clinical, laboratory, and radiologic data were collected. SARS-CoV-2 infection was diagnosed by reverse transcription polymerase chain reaction tests in nasopharyngeal swabs. RESULTS: Fifteen of 61 hospitalized children with cancer were diagnosed with SARS-CoV-2. Their mean age was 8.3±3.5 years. Initially, 10 (66.7%) were asymptomatic and 5 (33.3%) were symptomatic with fever and/or cough. Baseline laboratory tests other than SARS-CoV-2 reverse transcription polymerase chain reaction were not diagnostic; the mean absolute lymphocyte count was 8.7±2.4×109/L. C-reactive protein was mildly elevated in most of the patients. Imaging was performed in 10 (66.7%) patients with significant radiologic findings detected in 4 (40%) patients. Treatment was mainly supportive with antibiotics as per the febrile neutropenia protocol and local Children Hospital guidance for management of COVID-19 in children. CONCLUSIONS: Pediatric cancer patients with COVID-19 were mainly asymptomatic or with mild symptoms. A high index of suspicion and regular screening with nasopharyngeal swab in asymptomatic hospitalized cancer patients is recommended.