Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Chest X-ray lung imaging features in pediatric COVID-19 and comparison with viral lower respiratory infections in young children.

RATIONALE: Chest radiography (CXR) is a noninvasive imaging approach commonly used to evaluate lower respiratory tract infections (LRTIs) in children. However, the specific imaging patterns of pediatric coronavirus disease 2019 (COVID-19) on CXR, their relationship to clinical outcomes, and the possible differences from LRTIs caused by other viruses in children remain to be defined. METHODS: This is a cross-sectional study of patients seen at a pediatric hospital with polymerase chain reaction (PCR)-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n = 95). Patients were subdivided in infants (0-2 years, n = 27), children (3-10 years, n = 27), and adolescents (11-19 years, n = 41). A sample of young children (0-2 years, n = 68) with other viral lower respiratory infections (LRTI) was included to compare their CXR features with the subset of infants (0-2 years) with COVID-19. RESULTS: Forty-five percent of pediatric patients with COVID-19 were hospitalized and 20% required admission to intensive care unit (ICU). The most common abnormalities identified were ground-glass opacifications (GGO)/consolidations (35%) and increased peribronchial markings/cuffing (33%). GGO/consolidations were more common in older individuals and perihilar markings were more common in younger subjects. Subjects requiring hospitalization or ICU admission had significantly more GGO/consolidations in CXR (p < .05). Typical CXR features of pediatric viral LRTI (e.g., hyperinflation) were more common in non-COVID-19 viral LRTI cases than in COVID-19 cases (p < .05). CONCLUSIONS: CXR may be a complemental exam in the evaluation of moderate or severe pediatric COVID-19 cases. The severity of GGO/consolidations seen in CXR is predictive of clinically relevant outcomes. Hyperinflation could potentially aid clinical assessment in distinguishing COVID-19 from other types of viral LRTI in young children.

Effects of COVID-19 pandemic on adherence to obstructive sleep apnea therapy: A case report.

Telemedicine and remote monitoring are valuable tools to address inadequate obstructive sleep apnea compliance during the current pandemic.

Pediatric lung imaging features of COVID-19: A systematic review and meta-analysis.

RATIONALE: Pediatric COVID-19 studies have been mostly restricted to case reports and small case series, which have prevented the identification of specific pediatric lung disease patterns in COVID-19. The overarching goal of this systematic review and meta-analysis is to provide the first comprehensive summary of the findings of published studies thus far describing COVID-19 lung imaging data in the pediatric population. METHODS: A systematic literature search of PubMed was performed to identify studies assessing lung-imaging features of COVID-19 pediatric patients (0-18 years). A single-arm meta-analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI). RESULTS: A total of 29 articles (n = 1026 children) based on chest computerized tomography (CT) images were included. The main results of this comprehensive analysis are as follows: (1) Over a third of pediatric patients with COVID-19 (35.7%, 95% CI: 27.5%-44%) had normal chest CT scans and only 27.7% (95% CI: 19.9%-35.6%) had bilateral lesions. (2) The most typical pediatric chest CT findings of COVID-19 were ground-glass opacities (GGO) (37.2%, 95% CI: 29.3%-45%) and the presence of consolidations or pneumonic infiltrates (22.3%, 95% CI: 17.8%-26.9%). (3) The lung imaging findings in children with COVID-19 were overall less frequent and less severe than in adult patients. (4) Typical lung imaging features of viral respiratory infections in the pediatric population such as increased perihilar markings and hyperinflation were not reported in children with COVID-19. CONCLUSION: Chest CT manifestations in children with COVID-19 could potentially be used for early identification and prompt intervention in the pediatric population.