Objective The lung injury prediction score (LIPS) identifies patients at risk for ARDS in the emergency department (ED) but it has not been validated in non-ED hospitalized patients. We aimed to evaluate whether LIPS identifies non-ED hospitalized patients at risk of developing ARDS at the time of critical care contact. Design Retrospective study. Setting Five academic medical centers. Patients Nine hundred consecutive patients (≥18 y/o) with at least one ARDS risk factor at the time of critical care contact. Interventions None. Measurements and Main Results LIPS was calculated using the worst values within the 12 hours before initial critical care contact. Patients with ARDS at the time of initial contact were excluded. ARDS developed in 124 (13.7%) patients a median of 2 days (IQR 2–3) after critical care contact. Hospital mortality was 22% and was significantly higher in ARDS than non-ARDS patients (48% vs. 18%, p<0.001). Increasing LIPS was significantly associated with development of ARDS (OR 1.31, 95%CI 1.21–1.42) and the composite outcome of ARDS or death (OR 1.26, 95%CI 1.18–1.34). A LIPS ≥ 4 was associated with the development of ARDS (OR 4.17, 95%CI 2.26–7.72), composite outcome of ARDS or death (OR 2.43, 95%CI 1.68–3.49), and ARDS after accounting for the competing risk of death (HR 3.71, 95%CI 2.05–6.72). For ARDS development, the LIPS has an AUROC of 0.70 and a LIPS ≥ 4 has 90% sensitivity (misses only 10% of ARDS cases), 31% specificity, 17% positive predictive value, and 95% negative predictive value. Conclusions In a cohort of non-ED hospitalized patients, the LIPS and LIPS ≥ 4 can identify patients at increased risk of ARDS and/or death at the time of critical care contact but it does not perform as well as in the original ED cohort.

We can only speculate the reasons for many of these results at this time and further research into the sociological, psychological, and environmental factors is required. A high proportion of patients are assaulted at their home addresses. Further study is necessary to improve patient care with additional data provided by emergency medical services, police departments and surrounding hospitals.

Sepsis is a life-threatening disease state characterized by organ dysfunction and a dysregulated response to infection. The heart is one of the many organs affected by sepsis, in an entity termed sepsis-induced cardiomyopathy. This was initially used to describe a reversible depression in ejection fraction with ventricular dilation but advances in echocardiography and introduction of new techniques such as speckle tracking have led to descriptions of other common abnormalities in cardiac function associated with sepsis. This includes not only depression of systolic function, but also supranormal ejection fraction, diastolic dysfunction, and right ventricular dysfunction. These reports have led to inconsistent definitions of sepsis-induced cardiomyopathy. Just as there is heterogeneity among patients with sepsis, there is heterogeneity in the cardiac response; thus resuscitating these patients with a single approach is likely suboptimal. Many factors affect the heart in sepsis including inflammatory mediators, catecholamine responsiveness, and pathogen related toxins. This review will discuss different functional effects characterized by echocardiographic changes in sepsis and their prognostic and management implications.

Background: Acts of violence can be considered random when viewed singularly, but are appreciable as patterns and clusters of an epidemic. Violence begets violence: it has been shown that people exposed to violence are more likely to harm themselves, their families, and members of the community. Our previous manuscript on this subject demonstrated distinct clusters of violent trauma, where each subtype appeared to have its own domain. Methods: The location, date, time of day, and mechanism of injury of all non-accidental trauma patients from 1 January 2008 to 31 December 2013 were collected and analyzed. Kernel density analysis was used to identify areas of increased activity and these were compared by year. The areas identified were mapped by their latitude and longitude. The data for the year 2013 were used to determine the potential for the predictive value of the prior 5 years. Results: Definite trends can be observed in the temporal distribution of trauma, with a higher incidence of violent trauma occurring between 6 pm and 6 am during the 6-year period. Seasonal variation of higher amounts of violent trauma is also observed from April through August. Predictive modeling did not yield significant results for the following year or for the following month using Crimestat software. Conclusion: Discernable trends of trauma are able to be demonstrated based upon the identifiable clusters of assault but these clusters do not remain constant from year to year. Predictive modeling can assist with the identification of elevated incidents of activity, however has poor predictive value for the entirety of the year. The use of the Crimestat Time Series Forecasting Module is best served with the early indication of large changes in assault patterns. Further study and improved collaboration with the local police precincts and surrounding trauma centers is required to treat the epidemic of violence.