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Characterization of neonates born to mothers with SARS-CoV-2 infection: review and meta-analysis
(2020)
Characterization of neonates born to mothers with SARS-CoV-2 infection has been partially carried out. There has been no systematic review providing a holistic neonatal presentation including possible vertical transmission. A systematic literature search was performed using PubMed, Google Scholar and Web of Science up to June, 6 2020. Studies on neonates born to mothers with SARS-CoV-2 infection were included. A binary random effect model was used for prevalence and 95% confidence interval. 32 studies involving 261 neonates were included in meta-analysis. Most neonates born to infected mothers did not show any clinical abnormalities (80.4%). Clinical features were dyspnea in 11 (42.3%) and fever in 9 newborns (19.1%). Of 261 neonates, 120 neonates were tested for infection, of whom 12 (10.0%) tested positive. Swabs from placenta, cord blood and vaginal secretion were negative. Neonates are mostly non affected by the mother's SARS-CoV-2 infection. The risk of vertical transmission is low.
Background: Due to the coronavirus disease 2019 (COVID-19) pandemic, interventions in the upper airways are considered high-risk procedures for otolaryngologists and their colleagues. The purpose of this study was to evaluate limitations in hearing and communication when using a powered air-purifying respirator (PAPR) system to protect against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) transmission and to assess the benefit of a headset. Methods: Acoustic properties of the PAPR system were measured using a head and torso simulator. Audiological tests (tone audiometry, Freiburg speech test, Oldenburg sentence test (OLSA)) were performed in normal-hearing subjects (n = 10) to assess hearing with PAPR. The audiological test setup also included simulation of conditions in which the target speaker used either a PAPR, a filtering face piece (FFP) 3 respirator, or a surgical face mask. Results: Audiological measurements revealed that sound insulation by the PAPR headtop and noise, generated by the blower-assisted respiratory protection system, resulted in significantly deteriorated hearing thresholds (4.0 ± 7.2 dB hearing level (HL) vs. 49.2 ± 11.0
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.