Unhoused and Injured: Injury Characteristics and Outcomes in Unhoused Trauma Patients.

INTRODUCTION: The unhoused population is known to be at high risk for traumatic injury. However, there are scarce data regarding injury patterns and outcomes for this patient group. This study aims to investigate any differences in injury characteristics and hospital outcomes between unhoused and housed patients presenting with traumatic injuries. METHODS: We conducted a 3-y retrospective cohort study at a level 1 trauma center in a metropolitan area with a large unhoused population. All adult trauma patients who were identified as unhoused or housed underinsured (HUI) were included in the study. Injury characteristics, comorbidities, and hospital outcomes were compared between the two groups. RESULTS: A total of 8450 patients were identified, of which 7.5% were unhoused. Compared to HUI patients, unhoused patients were more likely to sustain minor injuries (65.2% versus 59.1%, P = 0.003) and more likely to be injured by assault (17.9% versus 12.4%, P < 0.001), stab wound (17.7% versus 10.8%, P < 0.001), and automobile versus pedestrian or bike (21.0% versus 15.8% P < 0.001). We found that unhoused patients had higher odds of mortality (adjusted odds ratio [AOR]: 1.93, 95% confidence interval [CI]: 1.10-3.36, P = 0.021), brain death (AOR: 5.40, 95% CI: 2.11-13.83, P < 0.001), bacteremia/sepsis (AOR: 4.36, 95% CI: 1.20-15.81, P = 0.025), and increased hospital length of stay (regression coefficient: 0.08, 95% CI: 0.03-0.12, P = 0.003). CONCLUSIONS: This study observed significant disparities in injury characteristics and hospital outcomes between the unhoused and HUI groups. Our results suggest that these disparities are impacted by social determinants of health unique to the unhoused population.

Broken time reversal symmetry in visual motion detection.

Our intuition suggests that when a movie is played in reverse, our perception of motion in the reversed movie will be perfectly inverted compared to the original. This intuition is also reflected in many classical theoretical and practical models of motion detection. However, here we demonstrate that this symmetry of motion perception upon time reversal is often broken in real visual systems. In this work, we designed a set of visual stimuli to investigate how stimulus symmetries affect time reversal symmetry breaking in the fruit fly Drosophila's well-studied optomotor rotation behavior. We discovered a suite of new stimuli with a wide variety of different properties that can lead to broken time reversal symmetries in fly behavioral responses. We then trained neural network models to predict the velocity of scenes with both natural and artificial contrast distributions. Training with naturalistic contrast distributions yielded models that break time reversal symmetry, even when the training data was time reversal symmetric. We show analytically and numerically that the breaking of time reversal symmetry in the model responses can arise from contrast asymmetry in the training data, but can also arise from other features of the contrast distribution. Furthermore, shallower neural network models can exhibit stronger symmetry breaking than deeper ones, suggesting that less flexible neural networks promote some forms of time reversal symmetry breaking. Overall, these results reveal a surprising feature of biological motion detectors and suggest that it could arise from constrained optimization in natural environments.

Back on the Streets: Examining Emergency Department Return Rates for Unhoused Patients Discharged After Trauma.

BACKGROUND: The unhoused population is at high risk for traumatic injuries and faces unique challenges in accessing follow-up care. However, there is scarce data regarding differences in Emergency Department (ED) return rates and reasons for return between unhoused and housed patients. METHODS: We conducted a 3-year retrospective cohort study at a level-1 trauma center in a large metropolitan area. All patients who presented to the ED with traumatic injuries and were discharged without hospital admission were included in the study. The primary outcome was ED returns for trauma-related complications or new traumatic events <6 months after discharge. Patient characteristics and study outcomes were compared between housed and unhoused groups. RESULTS: A total of 4184 patients were identified, of which 20.3% were unhoused. Compared to housed, unhoused patients were more likely to return to the ED (18.8% vs 13.9%, P < .001), more likely to return for trauma-related complications (4.6% vs 3.1%, P = .045), more likely to return with new trauma (7.1% vs 2.8%, P < .001), and less likely to return for scheduled wound checks (2.5% vs 4.3%, P = .012). Of the patients who returned with trauma-related complications, unhoused patients had a higher proportion of wound infection (20.5% vs 5.7%, P = .008). In the regression analysis, unhoused status was associated with increased odds of ED return with new trauma and decreased odds of return for scheduled wound checks. CONCLUSIONS: This study observed significant disparities between unhoused and housed patients after trauma. Our results suggest that inadequate follow-up in unhoused patients may contribute to further ED return.

Optimization in Visual Motion Estimation.

Sighted animals use visual signals to discern directional motion in their environment. Motion is not directly detected by visual neurons, and it must instead be computed from light signals that vary over space and time. This makes visual motion estimation a near universal neural computation, and decades of research have revealed much about the algorithms and mechanisms that generate directional signals. The idea that sensory systems are optimized for performance in natural environments has deeply impacted this research. In this article, we review the many ways that optimization has been used to quantitatively model visual motion estimation and reveal its underlying principles. We emphasize that no single optimization theory has dominated the literature. Instead, researchers have adeptly incorporated different computational demands and biological constraints that are pertinent to the specific brain system and animal model under study. The successes and failures of the resulting optimization models have thereby provided insights into how computational demands and biological constraints together shape neural computation.

Neural mechanisms to incorporate visual counterevidence in self-movement estimation.

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection.

Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.

Long-timescale anti-directional rotation in Drosophila optomotor behavior.

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

The impact of rounds with a psychiatry team in the intensive care unit: A prospective observational pilot study evaluating the effects on delirium incidence and outcomes.

BACKGROUND: Delirium in the intensive care unit (ICU) is a common but serious condition that has been associated with in-hospital mortality and post-discharge psychological dysfunction. The aim of this before and after study is to determine the effect of a multidisciplinary care model entailing daily ICU rounds with a psychiatrist on the incidence of delirium and clinical outcomes. OBJECTIVE: To assess the impact of a proactive psychiatry consultation model in the surgical ICU on the incidence and duration of delirium. METHODS: This was a prospective, single institution, observational controlled cohort pilot study of adult patients admitted to a surgical ICU. A control group that received standard of care (SOC) with daily delirium prevention care bundles in the pre-intervention period was compared to an intervention group, which had a psychiatrist participate in daily ICU rounds (post-intervention period). The primary outcome was delirium incidence. The secondary outcomes were: delirium duration, ventilator days, hospital and ICU length of stay, and in-hospital mortality. RESULTS: A total of 104 patients were enrolled and equally split between SOC and intervention groups; 95 contributed to analysis. The overall incidence of ICU delirium was 19%. SOC and intervention groups had similar rates of delirium (21% vs 18%, p = 0.72). None of the secondary outcomes statistically significantly differed between the two groups. CONCLUSION: Delirium in ICU patients is a potentially preventable condition with serious sequelae. There was no difference in delirium incidence or duration between patients receiving SOC or patients who had multidisciplinary rounds with a psychiatrist.

Long timescale anti-directional rotation in Drosophila optomotor behavior.

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied D. melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such "anti-directional turning" is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

Neural mechanisms to incorporate visual counterevidence in self motion estimation.

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.

Disparities in Care Among Gunshot Victims: A Nationwide Analysis.

INTRODUCTION: Given the well-known healthcare disparities most pronounced in racial and ethnic minorities, trauma healthcare in underrepresented patients should be examined, as in-hospital bias may influence the care rendered to patients. This study seeks to examine racial differences in outcomes and resource utilization among victims of gunshot wounds in the United States. METHODS: This is a retrospective review of the National Trauma Data Bank (NTDB) conducted from 2007 to 2017. The NTDB was queried for patients who suffered a gunshot wound not related to accidental injury or suicide. Patients were stratified according to race. The primary outcome for this study was mortality. Secondary outcomes included racial differences in resource utilization including air transport and discharge to rehabilitation centers. Univariate and multivariate analyses were used to compare differences in outcomes between the groups. RESULTS: A total of 250,675 patients were included in the analysis. After regression analysis, Black patients were noted to have greater odds of death compared to White patients (odds ratio [OR] 1.14, confidence interval [CI] 1.037-1.244; P = 0.006) and decreased odds of admission to the intensive care unit (ICU) (OR 0.76, CI 0.732-0.794; P < 0.001). Hispanic patients were significantly less likely to be discharged to rehabilitation centers (Hispanic: 0.78, CI 0.715-0.856; P < 0.001). Black patients had the shortest time to death (median time in minutes: White 49 interquartile range [IQR] [9-437] versus Black 24 IQR [7-205] versus Hispanic 39 IQR [8-379] versus Asian 60 [9-753], P < 0.001). CONCLUSIONS: As society carefully examines major institutions for implicit bias, healthcare should not be exempt. Greater mortality among Black patients, along with differences in other important outcome measures, demonstrate disparities that encourage further analysis of causes and solutions to these issues.

Odour motion sensing enhances navigation of complex plumes.

Odour plumes in the wild are spatially complex and rapidly fluctuating structures carried by turbulent airflows1-4. To successfully navigate plumes in search of food and mates, insects must extract and integrate multiple features of the odour signal, including odour identity5, intensity6 and timing6-12. Effective navigation requires balancing these multiple streams of olfactory information and integrating them with other sensory inputs, including mechanosensory and visual cues9,12,13. Studies dating back a century have indicated that, of these many sensory inputs, the wind provides the main directional cue in turbulent plumes, leading to the longstanding model of insect odour navigation as odour-elicited upwind motion6,8-12,14,15. Here we show that Drosophila melanogaster shape their navigational decisions using an additional directional cue-the direction of motion of odours-which they detect using temporal correlations in the odour signal between their two antennae. Using a high-resolution virtual-reality paradigm to deliver spatiotemporally complex fictive odours to freely walking flies, we demonstrate that such odour-direction sensing involves algorithms analogous to those in visual-direction sensing16. Combining simulations, theory and experiments, we show that odour motion contains valuable directional information that is absent from the airflow alone, and that both Drosophila and virtual agents are aided by that information in navigating naturalistic plumes. The generality of our findings suggests that odour-direction sensing may exist throughout the animal kingdom and could improve olfactory robot navigation in uncertain environments.

Feature maps: How the insect visual system organizes information.

A new study explores how a population of neurons in the insect brain responds to different features of visual scenes and discovers an unusual topographic map that organizes the information they encode.

Excitatory and inhibitory neural dynamics jointly tune motion detection.

Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.

Predicting Unplanned Intensive Care Unit Admission for Trauma Patients: The CRASH Score.

INTRODUCTION: Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission. METHODS: The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve. RESULTS: A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78. CONCLUSIONS: The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation.

Neural mechanisms to exploit positional geometry for collision avoidance.

Visual motion provides rich geometrical cues about the three-dimensional configuration of the world. However, how brains decode the spatial information carried by motion signals remains poorly understood. Here, we study a collision-avoidance behavior in Drosophila as a simple model of motion-based spatial vision. With simulations and psychophysics, we demonstrate that walking Drosophila exhibit a pattern of slowing to avoid collisions by exploiting the geometry of positional changes of objects on near-collision courses. This behavior requires the visual neuron LPLC1, whose tuning mirrors the behavior and whose activity drives slowing. LPLC1 pools inputs from object and motion detectors, and spatially biased inhibition tunes it to the geometry of collisions. Connectomic analyses identified circuitry downstream of LPLC1 that faithfully inherits its response properties. Overall, our results reveal how a small neural circuit solves a specific spatial vision task by combining distinct visual features to exploit universal geometrical constraints of the visual world.

Identifying Inputs to Visual Projection Neurons in Drosophila Lobula by Analyzing Connectomic Data.

Electron microscopy (EM)-based connectomes provide important insights into how visual circuitry of fruit fly Drosophila computes various visual features, guiding and complementing behavioral and physiological studies. However, connectomic analyses of the lobula, a neuropil putatively dedicated to detecting object-like features, remains underdeveloped, largely because of incomplete data on the inputs to the brain region. Here, we attempted to map the columnar inputs into the Drosophila lobula neuropil by performing connectivity-based and morphology-based clustering on a densely reconstructed connectome dataset. While the dataset mostly lacked visual neuropils other than lobula, which would normally help identify inputs to lobula, our clustering analysis successfully extracted clusters of cells with homogeneous connectivity and morphology, likely representing genuine cell types. We were able to draw a correspondence between the resulting clusters and previously identified cell types, revealing previously undocumented connectivity between lobula input and output neurons. While future, more complete connectomic reconstructions are necessary to verify the results presented here, they can serve as a useful basis for formulating hypotheses on mechanisms of visual feature detection in lobula.

Parallel locomotor control strategies in mice and flies.

Our understanding of the neural basis of locomotor behavior can be informed by careful quantification of animal movement. Classical descriptions of legged locomotion have defined discrete locomotor gaits, characterized by distinct patterns of limb movement. Recent technical advances have enabled increasingly detailed characterization of limb kinematics across many species, imposing tighter constraints on neural control. Here, we highlight striking similarities between coordination patterns observed in two genetic model organisms: the laboratory mouse and Drosophila. Both species exhibit continuously-variable coordination patterns with similar low-dimensional structure, suggesting shared principles for limb coordination and descending neural control.

The impact of coronavirus 2019 on trauma.

PURPOSE OF REVIEW: The relationship between trauma and the ongoing global coronavirus 2019 (COVID-19) pandemic is still largely unclear. This comprehensive review of recent studies examining overall trauma volumes, mechanisms of injury, and outcomes after trauma during the COVID-19 pandemic was performed to better understand the impact of the pandemic on trauma patients. RECENT FINDINGS: In the early stages of the pandemic, the overall volumes of patients seen in many major trauma centers had decreased; however, these rates largely returned to historical baselines after the cessation of stay-at-home orders. An increasing proportion of trauma patients were injured by penetrating mechanisms during the pandemic. Being a victim of interpersonal violence was an independent risk factor for COVID-19 infection. In two studies utilizing propensity score-matched analysis among trauma patients, COVID-19 infection was associated with a five- to sixfold increase in mortality risk as compared to uninfected patients. SUMMARY: Consequences of the COVID-19 pandemic include increased financial stressors, job loss, mental illness, and illegal drug use, all of which are known risk factors for trauma. This is particularly true among vulnerable patient populations such as racial minority groups and low socioeconomic status patients. To lessen the impact of COVID-19 on trauma patients, increased awareness of the problem and heightened emphasis on injury prevention must be made.

Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons.

Animals have evolved sophisticated visual circuits to solve a vital inference problem: detecting whether or not a visual signal corresponds to an object on a collision course. Such events are detected by specific circuits sensitive to visual looming, or objects increasing in size. Various computational models have been developed for these circuits, but how the collision-detection inference problem itself shapes the computational structures of these circuits remains unknown. Here, inspired by the distinctive structures of LPLC2 neurons in the visual system of Drosophila, we build anatomically-constrained shallow neural network models and train them to identify visual signals that correspond to impending collisions. Surprisingly, the optimization arrives at two distinct, opposing solutions, only one of which matches the actual dendritic weighting of LPLC2 neurons. Both solutions can solve the inference problem with high accuracy when the population size is large enough. The LPLC2-like solutions reproduces experimentally observed LPLC2 neuron responses for many stimuli, and reproduces canonical tuning of loom sensitive neurons, even though the models are never trained on neural data. Thus, LPLC2 neuron properties and tuning are predicted by optimizing an anatomically-constrained neural network to detect impending collisions. More generally, these results illustrate how optimizing inference tasks that are important for an animal's perceptual goals can reveal and explain computational properties of specific sensory neurons.