Objective: To identify markers associated with in-hospital death in patients with coronavirus disease 2019 (COVID-19)eassociated pneumonia. Patients and Methods: A retrospective cohort study was conducted of 140 patients with moderate to critical COVID-19eassociated pneumonia requiring oxygen supplementation admitted to the hospital

BACKGROUND-Transfusion-associated circulatory overload (TACO) is a frequent complication of blood transfusion. Investigations identifying risk factors for TACO in critically ill patients are lacking.

Background Acute lung injury (ALI) is a serious postoperative complication with limited treatment options. A preoperative risk prediction model would assist both clinicians and scientists interested in ALI. The objective of this investigation was to develop a surgical lung injury prediction (SLIP) model to predict risk of postoperative ALI based on readily available preoperative risk factors. Methods This is secondary analysis of a prospective cohort investigation including adult patients undergoing high-risk surgery. Preoperative risk factors for postoperative ALI were identified and evaluated for inclusion in the SLIP model. Multivariate logistic regression was used to develop the model. Model performance was assessed with the area under the Receiver Operating Characteristics Curve and the Hosmer and Lemeshow Goodness-of-fit test. Results Out of 4,366 patients, 113 (2.6%) developed early postoperative ALI. Predictors of postoperative ALI in multivariate analysis which were maintained in the final SLIP model included high-risk cardiac, vascular, and thoracic surgery, diabetes mellitus, chronic obstructive pulmonary disease, gastroesophageal reflux disease, and alcohol abuse. The SLIP score discriminated patients who developed early postoperative ALI from those who did not with an area under the Receiver Operating Characteristic Curve (95% CI) of 0.82 (0.78 – 0.86) and was well calibrated (Hosmer Lemeshow p = 0.55). Internal validation using 10-fold cross-validation noted minimal loss of diagnostic accuracy with a mean +/− standard deviation area under the Receiver Operating Characteristic Curve of 0.79 +/− 0.08. Conclusions Using readily available preoperative risk factors, we developed the SLIP scoring system to predict risk of developing early postoperative ALI.

Rationale: Significant progress has been made in understanding the pathogenesis of acute respiratory distress syndrome (ARDS). Recent advances in hospital practice may have reduced the incidence of this lethal syndrome. Objectives: To observe incidence trends and associated outcomes of ARDS.Methods: This population-based cohort study was conducted in Olmsted County, Minnesota. Using a validated screening protocol, investigators identified intensive care patients with acute hypoxemia and bilateral pulmonary infiltrates. The presence of ARDS was independently confirmed according to American-European Consensus Conference criteria. The incidence of ARDS and associated outcomes were compared over the 8-year study period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008). Measurements and Main Results: Over the 8-year period, critically ill Olmsted County residents presented with increasing severity of acute illness, a greater number of comorbidities, and a higher prevalence of major predisposing conditions for ARDS. The ARDS incidence decreased significantly from 82.4 to 38.9 per 100,000 person-years during the study period (P , 0.001). A decline in hospital-acquired ARDS (P , 0.001) was responsible for the fall in the incidence density with no change on admission (P 5 0.877). Overall, mortality and hospital and intensive care unit lengths of stay decreased over time (P , 0.001), whereas the ARDS case-fatality did not change significantly. Conclusions: Despite an increase in patients' severity of illness, number of comorbidities, and prevalence of major ARDS risk factors, the incidence of ARDS in this suburban community decreased by more than half. Correlation of the observed findings with changes in health care delivery may have important implications for the planning of acute care services in other regions.

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in Videos and Image Captioning. To overcome this problem, we propose a compact and flexible structure, namely Block-Term tensor decomposition, which greatly reduces the parameters of RNNs and improves their training efficiency. Compared with alternative low-rank approximations, such as tensortrain RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only more concise (when using the same rank), but also able to attain a better approximation to the original RNNs with much fewer parameters. On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes 17,388 times fewer parameters than the standard LSTM to achieve an accuracy improvement over 15.6% in the Action Recognition task on the UCF11 dataset.

IMPORTANCE Fever is common in critically ill neurologic patients. Knowledge of the indicators of central fever may allow greater antibiotic stewardship in this era of rapidly developing super-resistant microorganisms. OBJECTIVE To develop a model to differentiate central from infectious fever in critically ill neurologic patients with fever of an undetermined cause.

Excessive oxygen supplementation is common in mechanically ventilated patients with ALI and may be associated with worsening lung function.

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