Utility and safety of draining pleural effusions in mechanically ventilated patients: a systematic review and meta-analysis

Abstract Introduction Pleural effusions are frequently drained in mechanically ventilated patients but the benefits and risks of this procedure are not well established. Methods We performed a literature search of multiple databases (MEDLINE, EMBASE, HEALTHSTAR, CINAHL) up to April 2010 to identify studies reporting clinical or physiological outcomes of mechanically ventilated critically ill patients who underwent drainage of pleural effusions. Studies were adjudicated for inclusion independently and in duplicate. Data on duration of ventilation and other clinical outcomes, oxygenation and lung mechanics, and adverse events were abstracted in duplicate independently. Results Nineteen observational studies (N = 1,124) met selection criteria. The mean PaO2:FiO2 ratio improved by 18% (95% confidence interval (CI) 5% to 33%, I 2 = 53.7%, five studies including 118 patients) after effusion drainage. Reported complication rates were low for pneumothorax (20 events in 14 studies including 965 patients; pooled mean 3.4%, 95% CI 1.7 to 6.5%, I 2 = 52.5%) and hemothorax (4 events in 10 studies including 721 patients; pooled mean 1.6%, 95% CI 0.8 to 3.3%, I 2 = 0%). The use of ultrasound guidance (either real-time or for site marking) was not associated with a statistically significant reduction in the risk of pneumothorax (OR = 0.32; 95% CI 0.08 to 1.19). Studies did not report duration of ventilation, length of stay in the intensive care unit or hospital, or mortality. Conclusions Drainage of pleural effusions in mechanically ventilated patients appears to improve oxygenation and is safe. We found no data to either support or refute claims of beneficial effects on clinically important outcomes such as duration of ventilation or length of stay

Diagnosis and management of first case of COVID-19 in Canada: Lessons applied from SARS-CoV-1

We report diagnosis and management of the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) hospitalized in Toronto, Canada. No healthcare-associated transmission occurred. In the face of a potential pandemic of COVID-19, we suggest sustainable and scalable control measures developed based on lessons learned from severe acute respiratory syndrome

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

The impact of vancomycin trough concentrations on outcomes in non-deep seated infections: a retrospective cohort study

Abstract Background Guidelines recommending vancomycin trough concentrations > 10 mg/L in non-deep seated infections are based on expert opinion. The objective of this study was to evaluate patients with non-deep seated infections treated with short-course vancomycin to determine whether there were differences in outcomes with trough concentrations of ≤10 mg/L (low) versus > 10 mg/L (high). Methods A retrospective cohort study of patients hospitalized between March 10, 2010 and December 31, 2015 who received ≤14 days of vancomycin to treat a non-deep seated infection and had at least one steady state trough concentration was completed. Patient data for the low versus high trough cohorts were compared using appropriate statistical tests and binary logistic regression was used to identify factors associated with clinical outcome. Results Of 2098 patients screened, 103 (5%) met inclusion criteria. Baseline characteristics between cohorts were not different. Clinical cure was not different between the low (42/48 [88%]) and high trough (48/55 [87%]) cohorts (p > 0.99) and vancomycin trough concentration was not associated with clinical outcome (p = 0.973). More patients in the high trough group had dosing changes (7/48 [15%] vs. 22/55 [40%], p = 0.0046), with approximately three times more dose adjustments per patient (0.17 vs. 0.55, p = 0.0193). No signal for increased vancomycin resistance associated with vancomycin troughs was identified. Conclusions: No difference in clinical or microbiological outcomes based on vancomycin trough concentrations were observed in patients with non-deep seated infections treated with vancomycin for ≤14 days. Targeting higher vancomycin trough concentrations of > 10 mg/L may be associated with increased workload with no corresponding benefit in clinical or microbiological outcomes in these patients

Utility of routine post-admission testing for SARS-CoV-2 in a rehabilitation facility

Asymptomatic screening for SARS-CoV-2 is recommended in healthcare settings during periods of increased incidence, yet studies in rehabilitation settings are lacking. Routine weekly post-admission asymptomatic testing in a rehabilitation facility offered marginal gain beyond syndromic and targeted unit testing and was not associated with a reduced risk of healthcare-associated COVID-19