BackgroundRecent studies have demonstrated the vital influence of commensal microbial communities on human health. The central role of the gut in the response to injury is well described; however, no prior studies have used culture-independent profiling techniques to characterize the gut microbiome after severe trauma. We hypothesized that in critically injured patients, the gut microbiome would undergo significant compositional changes in the first 72 hours after injury.MethodsTrauma stool samples were prospectively collected via digital rectal examination at the time of presentation (0 hour). Patients admitted to the intensive care unit (n=12) had additional stool samples collected at 24 hours and/or 72 hours. Uninjured patients served as controls (n=10). DNA was extracted from stool samples and 16S rRNA-targeted PCR amplification was performed; amplicons were sequenced and binned into operational taxonomic units (OTUs; 97% sequence similarity). Diversity was analyzed using principle coordinates analyses, and negative binomial regression was used to determine significantly enriched OTUs.ResultsCritically injured patients had a median Injury Severity Score of 27 and suffered polytrauma. At baseline (0 hour), there were no detectable differences in gut microbial community diversity between injured and uninjured patients. Injured patients developed changes in gut microbiome composition within 72 hours, characterized by significant alterations in phylogenetic composition and taxon relative abundance. Members of the bacterial orders Bacteroidales, Fusobacteriales and Verrucomicrobiales were depleted during 72 hours, whereas Clostridiales and Enterococcus members enriched significantly.DiscussionIn this initial study of the gut microbiome after trauma, we demonstrate that significant changes in phylogenetic composition and relative abundance occur in the first 72 hours after injury. This rapid change in intestinal microbiota represents a critical phenomenon that may influence outcomes after severe trauma. A better understanding of the nature of these postinjury changes may lead to the ability to intervene in otherwise pathological clinical trajectories.Level of evidenceIIIStudy typePrognostic/epidemiological

Coagulopathy is common after injury and develops independently from iatrogenic, hypothermic, and dilutional causes. Despite considerable research on the topic over the past decade, trauma-induced coagulopathy (TIC) continues to portend poor outcomes, including decreased survival. We review the current evidence regarding the diagnosis and mechanisms underlying trauma induced coagulopathy and summarize the debates regarding optimal management strategy including product resuscitation, potential pharmacologic adjuncts, and targeted approaches to hemostasis. Throughout, we will identify areas of continued investigation and controversy in the understanding and management of TIC.

IMPORTANCE Despite the highest injury rates worldwide, formal medical care is not often sought after injuries in Sub-Saharan Africa. Unaffordable costs associated with trauma care might inhibit injured patients from seeking care. OBJECTIVES To (1) determine the injury epidemiology in Cameroon using populationrepresentative data, (2) identify the barriers to use of formal health care after injury, and (3) determine the association between use of care and economic outcomes after injury. DESIGN, SETTING, AND PARTICIPANTS This mixed-methods, cross-sectional study included a population-representative, community-based survey and nested qualitative semistructured interviews in the urban-rural Southwest Region of Cameroon. Three-stage cluster sampling was used to select target households.

Objective Machine learning techniques have demonstrated superior discrimination compared to conventional statistical approaches in predicting trauma death. The objective of this study is to evaluate whether machine learning algorithms can be used to assess risk and dynamically identify patient-specific modifiable factors critical to patient trajectory for multiple key outcomes after severe injury. Methods SuperLearner, an ensemble machine-learning algorithm, was applied to prospective observational cohort data from 1494 critically-injured patients. Over 1000 agnostic predictors were used to generate prediction models from multiple candidate learners for outcomes of interest at serial time points post-injury. Model accuracy was estimated using cross-validation and area under the curve was compared to select among predictors. Clinical variables responsible for driving outcomes were estimated at each time point. Results SuperLearner fits demonstrated excellent cross-validated prediction of death (overall AUC 0.94–0.97), multi-organ failure (overall AUC 0.84–0.90), and transfusion (overall AUC 0.87–0.9) across multiple post-injury time points, and good prediction of Acute Respiratory Distress Syndrome (overall AUC 0.84–0.89) and venous thromboembolism (overall AUC 0.73–0.83). Outcomes with inferior data quality included coagulopathic trajectory (AUC 0.48–0.88). Key clinical predictors evolved over the post-injury timecourse and included both anticipated and unexpected variables. Non-random missingness of data was identified as a predictor of multiple outcomes over time. Conclusions Machine learning algorithms can be used to generate dynamic prediction after injury while avoiding the risk of over- and under-fitting inherent in ad hoc statistical approaches. SuperLearner prediction after injury demonstrates promise as an adaptable means of helping clinicians integrate voluminous, evolving data on severely-injured patients into real-time, dynamic decision-making support.

Introduction Despite the rising trend in breast cancer incidence and mortality across Sub-Saharan Africa, there remains a critical knowledge gap about the burden and patterns of breast disease and breast cancer screening practices at the population level. This study aimed to identify socioeconomic factors associated with knowledge and practice of breast self-examination (BSE) as well as assess the prevalence of breast disease symptoms among a mixed urban-rural population of women in the Southwest region of Cameroon. Methods We conducted a household-level community-based study in Southwest Cameroon between January and March 2017, using a three-stage cluster sampling framework. We surveyed 1287 households and collected self-reported data on 4208 female subjects, 790 of whom were household representatives. Each household representative provided information on behalf of all female household members about any ongoing breast disease symptoms. Moreover, female household representatives were questioned about their own knowledge and practice of BSE. Results Women demonstrated low frequency of knowledge of BSE, as 25% (n=201) of household representatives reported any knowledge of BSE; and among these only 15% (n=30) practiced BSE on a monthly basis. Age (aOR: 1.04), usage of Liquid Petroleum Gas fuel, a marker of higher socioeconomic status (aOR: 1.86), and speaking English as a primary language in the household (aOR: 1.59) were significant predictors of knowledge of BSE. Eleven women reported ongoing breast disease symptoms resulting in an overall prevalence of 2.3 cases of breast disease symptoms per 1000 women. Conclusions Socioeconomic disparities in access to health education may be a determinant of knowledge of BSE. Community-based strategies are needed to improve dissemination of breast cancer screening methods, particularly for women who face barriers to accessing care.

BACKGROUND Acute traumatic coagulopathy (ATC) afflicts 20–30% of trauma patients, but the extensive collinearity of the coagulation cascade complicates attempts to clarify global clotting factor dysfunction. This study aims to characterize phenotypes of clotting factor dysfunction and their contributions to mortality after major trauma. METHODS This prospective cohort study examines all adult trauma patients of the highest activation level presenting to San Francisco General Hospital between 2/2005 and 2/2015. Factors II, V, VII, VIII, IX, X, and protein C activity on admission and mortality status at 28 days were assessed. Predictors of 28-day mortality in univariate analysis were included in multiple logistic regression controlling for traumatic brain injury (TBI), acidosis, age, and mechanism of injury. Principal component analysis (PCA) was utilized to identify phenotypic coagulation. RESULTS Complete coagulation factor data was available for 876/1,429 (61%). In multiple logistic regression, factors V (OR: 0.86, CI95%: 0.76–0.97), VIII (OR: 0.97, CI95%: 0.95–0.99), X (OR: 0.79, CI95%: 0.68–0.92), and protein C (OR: 1.17, CI95%: 1.05–1.30) significantly predicted 28-day mortality after controlling for age, base deficit, mechanism of injury, and TBI. PCA identified 2 significant principal components (Phenotypes 1 and 2) that accounted for 66.3% of the total variance. Phenotype 1 (factors II, VII, IX, X, and protein C abnormalities) explained 49.3% and was associated with increased injury, coagulopathy, TBI, and mortality. Phenotype 2 (factors V and VIII abnormalities) explained 17.0% and was associated with increased coagulopathy, blunt injury, and mortality. Only Phenotype 2 remained significantly associated with 28-day mortality in multiple logistic regression. CONCLUSIONS PCA identified 2 distinct phenotypes within the entirety of global clotting factor abnormalities, and these findings substantiate the crucial association of factors V and VIII on mortality following trauma. This may be the first step toward identifying unique phenotypes after injury and personalizing hemostatic resuscitation. LEVEL OF EVIDENCE Prognostic study, Level III

Background: Trauma-induced coagulopathy can present as abnormalities in a conventional or viscoelastic coagulation assay or both. We hypothesized that patients with discordant coagulopathies reflect different clinical phenotypes. Methods: Blood samples were collected prospectively from critically injured patients upon arrival at two urban Level I trauma centers. International normalized ratio (INR), partial thromboplastin time (PTT), thromboelastography (TEG), and coagulation factors were assayed. Results: 278 patients (median ISS 17, mortality 26%) were coagulopathic: 20% with isolated abnormal INR and/or PTT (CONVENTIONAL), 49% with isolated abnormal TEG (VISCOELASTIC), and 31% with abnormal INR/PTT and TEG (BOTH). Compared with VISCOELASTIC, CONVENTIONAL and BOTH had higher ISS, lower GCS, larger base deficit, and decreased factor activities (all p<0.017). They received more blood products and had more ICU/ventilation days (all p<0.017). Mortality was higher in CONVENTIONAL (40%) and BOTH (49%) than VISCOELASTIC (6%, p<0.017). Conclusions: Although TEG-guided resuscitation improves survival after injury, INR and PTT identify coagulopathic patients with highest mortality regardless of TEG and likely represent distinct mechanisms independent of biochemical clot strength.

Prognostic and therapeutic, level II and III.