Incidence of multisystem inflammatory syndrome in children and the comorbidity scores in pediatric coronavirus disease 2019 cases.

BACKGROUND: We aimed to determine the incidence of multisystem inflammatory syndrome in children (MIS-C) in pediatric coronavirus disease 2019 (COVID-19) cases and to define the relationships between the need for hospitalization, the development of MIS-C, and the Charlson Comorbidity Index (CCI) and Pediatric Comorbidity Index (PCI) scores. METHODS: All pediatric COVID-19 cases between March 25, 2020, and December 28, 2020, in the Marmara University Pendik Training and Research Hospital were enrolled. Patients who needed hospitalization were determined. Hospital records were re-examined to identify those diagnosed as having MIS-C. The CCI and PCI were used to validate the comorbidity status. RESULTS: Among 2,055 pediatric COVID-19 cases, 1,340 were included in the study, and 213 patients (15.9%) had at least one comorbidity. All the patients or their parents were interviewed about the need for hospitalization, except for the acute period. Six patients had MIS-C, which corresponds to a MIS-C incidence of 0.4%. The need for hospitalization increased in the patients with comorbidities (P < 0.05). No correlation was found between the comorbidity scores and the development of MIS-C. The need for hospitalization increased in the patients with CCI scores of ≥2 and PCI scores of ≥4 (P < 0.05). CONCLUSIONS: Our study is the first to examine the incidence of MIS-C, which was 0.4%, by long-term follow up of pediatric COVID-19 cases and to demonstrate that the CCI and PCI can be used to predict the need for hospitalization and prognosis of pediatric patients with COVID-19.

Point-of-care Lung Ultrasound, Lung CT and NEWS to Predict Adverse Outcomes and Mortality in COVID-19 Associated Pneumonia.

Introduction: The appraisal of disease severity and prediction of adverse outcomes using risk stratification tools at early disease stages is crucial to diminish mortality from coronavirus disease 2019 (COVID-19). While lung ultrasound (LUS) as an imaging technique for the diagnosis of lung diseases has recently gained a leading position, data demonstrating that it can predict adverse outcomes related to COVID-19 is scarce. The main aim of this study is therefore to assess the clinical significance of bedside LUS in COVID-19 patients who presented to the emergency department (ED). Methods: Patients with a confirmed diagnosis of SARS-CoV-2 pneumonia admitted to the ED of our hospital between March 2021 and May 2021 and who underwent a 12-zone LUS and a lung computed tomography scan were included prospectively. Logistic regression and Cox proportional hazard models were used to predict adverse events, which was our primary outcome. The secondary outcome was to discover the association of LUS score and computed tomography severity score (CT-SS) with the composite endpoints. Results: We assessed 234 patients [median age 59.0 (46.8-68.0) years; 59.4% M), including 38 (16.2%) in-hospital deaths for any cause related to COVID-19. Higher LUS score and CT-SS was found to be associated with ICU admission, intubation, and mortality. The LUS score predicted mortality risk within each stratum of NEWS. Pairwise analysis demonstrated that after adjusting a base prediction model with LUS score, significantly higher accuracy was observed in predicting both ICU admission (DBA -0.067, P = .011) and in-hospital mortality (DBA -0.086, P = .017). Conclusion: Lung ultrasound can be a practical prediction tool during the course of COVID-19 and can quantify pulmonary involvement in ED settings. It is a powerful predictor of ICU admission, intubation, and mortality and can be used as an alternative for chest computed tomography while monitoring COVID-19-related adverse outcomes.