Objective The debilitating and persistent effects of intensive care unit (ICU)-acquired delirium and weakness warrant testing of prevention strategies. The purpose of this study was to evaluate the effectiveness and safety of implementing the Awakening and Breathing Coordination, Delirium monitoring/management, and Early exercise/mobility (ABCDE) bundle into everyday practice. Design Eighteen-month, prospective, cohort, before-after study conducted between November 2010 and May 2012. Setting Five adult ICUs, one step-down unit, and one oncology/hematology special care unit located in a 624-bed tertiary medical center. Patients Two hundred ninety-six patients (146 pre- and 150 post-bundle implementation), age ≥ 19 years, managed by the institutions’ medical or surgical critical care service. Interventions ABCDE bundle. Measurements For mechanically ventilated patients (n = 187), we examined the association between bundle implementation and ventilator-free days. For all patients, we used regression models to quantify the relationship between ABCDE bundle implementation and the prevalence/duration of delirium and coma, early mobilization, mortality, time to discharge, and change in residence. Safety outcomes and bundle adherence were monitored. Main Results Patients in the post-implementation period spent three more days breathing without mechanical assistance than did those in the pre-implementation period (median [IQR], 24 [7 to 26] vs. 21 [0 to 25]; p = 0.04). After adjusting for age, sex, severity of illness, comorbidity, and mechanical ventilation status, patients managed with the ABCDE bundle experienced a near halving of the odds of delirium (odds ratio [OR], 0.55; 95% confidence interval [CI], 0.33–0.93; p = 0.03) and increased odds of mobilizing out of bed at least once during an ICU stay (OR, 2.11; 95% CI, 1.29–3.45; p = 0.003). No significant differences were noted in self-extubation or reintubation rates. Conclusions Critically ill patients managed with the ABCDE bundle spent three more days breathing without assistance, experienced less delirium, and were more likely to be mobilized during their ICU stay than patients treated with usual care.

There has been a plethora of literature regarding nonoperative management of blunt splenic injuries published since the original EAST practice management guideline was written. Nonoperative management of blunt splenic injuries is now the treatment modality of choice in hemodynamically stable patients, irrespective of the grade of injury, patient age, or the presence of associated injuries. Its use is associated with a low overall morbidity and mortality when applied to an appropriate patient population. Nonoperative management of blunt splenic injuries should only be considered in an environment that provides capabilities for monitoring, serial clinical evaluations, and has an operating room available for urgent laparotomy. Patients presenting with hemodynamic instability and peritonitis still warrant emergent operative intervention. Intravenous contrast enhanced computed tomographic scan is the diagnostic modality of choice for evaluating blunt splenic injuries. Repeat imaging should be guided by a patient's clinical status. Adjunctive therapies like angiography with embolization are increasingly important adjuncts to nonoperative management of splenic injuries. Despite the explosion of literature on this topic, many questions regarding nonoperative management of blunt splenic injuries remain without conclusive answers in the literature.

IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Similar to developmental programs in eukaryotes, the death of a subpopulation of cells is thought to benefit bacterial biofilm development. However mechanisms that mediate a tight control over cell death are not clearly understood at the population level. Here we reveal that CidR dependent pyruvate oxidase (CidC) and α-acetolactate synthase/decarboxylase (AlsSD) overflow metabolic pathways, which are active during staphylococcal biofilm development, modulate cell death to achieve optimal biofilm biomass. Whereas acetate derived from CidC activity potentiates cell death in cells by a mechanism dependent on intracellular acidification and respiratory inhibition, AlsSD activity effectively counters CidC action by diverting carbon flux towards neutral rather than acidic byproducts and consuming intracellular protons in the process. Furthermore, the physiological features that accompany metabolic activation of cell death bears remarkable similarities to hallmarks of eukaryotic programmed cell death, including the generation of reactive oxygen species and DNA damage. Finally, we demonstrate that the metabolic modulation of cell death not only affects biofilm development but also biofilm-dependent disease outcomes. Given the ubiquity of such carbon overflow pathways in diverse bacterial species, we propose that the metabolic control of cell death may be a fundamental feature of prokaryotic development.