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In the US, disparities in surgical care impede the delivery of uniformly high-quality care to all patients. There is a lack of disparity-sensitive measures related to surgical care. The American College of Surgeons Metrics for Equitable Access and Care in Surgery group, through research and expert consensus, aimed to identify disparity-sensitive measures in surgical care.An environmental scan, systematic literature review, and subspecialty society surveys were conducted to identify potential disparity-sensitive surgical measures. A modified Delphi process was conducted where panelists rated measures on both importance and validity. In addition, a novel literature-based disparity-sensitive scoring process was used.We identified 841 potential disparity-sensitive surgical measures. From these, our Delphi and literature-based approaches yielded a consensus list of 125 candidate disparity-sensitive measures. These measures were rated as both valid and important and were supported by the existing literature.There are profound disparities in surgical care within the US healthcare system. A multidisciplinary Delphi panel identified 125 potential disparity-sensitive surgical measures that could be used to track health disparities, evaluate the impact of focused interventions, and reduce healthcare inequity.

Evaluating the relationship between health care costs and quality is paramount in the current health care economic climate, as an understanding of value is needed to drive policy decisions. While many policy analyses are focused on the larger health care system, there is a pressing need for surgically focused economic analyses. Surgical care is costly, and innovative technology is constantly introduced into the operating room, and surgical care impacts patients' short- and long-term physical and economic well-being. Unfortunately, significant knowledge gaps exist regarding the relationship between cost, value, and economic impact of surgical interventions. Despite the plethora of health care data available in the forms of claims databases, discharge databases, and national surveys, no single source of data contains all the information needed for every policy-relevant analysis of surgical care. For this reason, it is important to understand which data are available and what can be accomplished with each of the data sets. In this article, we provide an overview of databases commonly used in surgical health services research. We focus our review on the following five categories of data: governmental claims databases, commercial claims databases, hospital-based clinical databases, state and national discharge databases, and national surveys. For each, we present a summary of the database sampling frame, clinically relevant variables, variables relevant to economic analyses, strengths, weaknesses, and examples of surgically relevant analyses. This review is intended to improve understanding of the current landscape of data available, as well as stimulate novel analyses among surgical populations. Ongoing debates over national health policy reforms may shape the delivery of surgical care for decades to come. Appropriate use of available data resources can improve our understanding of the economic impact of surgical care on our health care system and our patients.Regular Review, Level V.

The abstract provides an overview of lymphedema, a chronic and progressive lymphatic disorder that affects millions of people globally. This condition is associated with high costs and comorbidity, with limited treatment options available. Phlebolymphedema, a common form of secondary lymphedema, is linked to the combined effects of lymphatic and chronic venous insufficiencies. Early intervention is crucial to managing the condition; however, early detection remains challenging due to the lack of reliable diagnostic tools. This case study evaluates the clinical utility of a portable tissue analytical tool in a subject with bilateral lower extremity lymphedema undergoing a 60-day self-management program. The study includes details of the subject's profile, the intervention, and the measurements taken with the novel tool.

Deep brain stimulation (DBS) has shown remarkable success in treating neurological and psychiatric disorders such as Parkinson’s disease, essential tremor, dystonia, epilepsy, and obsessive-compulsive disorder. Despite this success, the underlying mechanism of action remains unknown. DBS is now being explored as a means to improve cognition and functional outcomes in other psychiatric conditions, such as those characterized by reduced N-methyl-D-aspartate (NMDA) function (i.e. schizophrenia). While most DBS treatments for movement disorders require continuous stimulation, there is evidence that intermittent stimulation in cognitive and psychiatric conditions may have persisting effects beyond the period of stimulation. This suggests that the effects of DBS on brain activity last beyond the time of its acute electrical effects, necessitating a broader exploration of how neuromodulation alters brain networks. In this study, we utilize a novel technology, functional ultrasound imaging (fUSI), to characterize the cerebrovascular impact of medial septal nucleus (MSN) DBS under conditions of NMDA antagonism (pharmacologically using Dizocilpine [MK-801]) in anesthetized male mice. Imaging from a sagittal plane across a variety of brain regions, we find that MSN theta-frequency (7.7Hz) DBS had a larger effect on hippocampal cerebral blood volume (CBV) after stimulation offset. This was observed following an intraperitoneal (i.p.) injection of either saline vehicle or MK-801 (1 mg/kg). This effect was not present using standard high frequency DBS stimulation parameters (i.e. gamma [100Hz]). These results indicate the MSN DBS increases circuit-specific hippocampal neurovascular activity in a frequency-dependent manner and does so in manner that continues beyond the period of electrical stimulation.Significance StatementDeep brain stimulation (DBS) is a well-established treatment for neurological and neuropsychiatric diseases, yet it’s mechanism of action remains unknown. We recently demonstrated that medial septal nucleus (MSN) DBS improves spatial memory in rodents treated with the NMDA antagonist MK-801, a drug known to mimic cognitive impairment in schizophrenia. Here we utilize functional ultrasound imaging (fUSI) in mice to visualize the effects of MSN DBS on cerebral blood volume (CBV) following saline or MK-801 administration. We find that theta frequency-specific (7.7Hz) MSN DBS restored MK-801-induced CBV reductions within the septohippocampal circuit (i.e., hippocampus) after stimulation offset. These results demonstrate that MSN stimulation drives circuit-specific neurovascular activity and may ultimately improve cognitive function in diseases with NMDA hypofunction, such as schizophrenia.

Decision making has been intensively studied in the posterior parietal cortex in non-human primates on a single neuron level. In humans decision making has mainly been studied with psychophysical tools or with fMRI. Here, we investigated how single neurons from human posterior parietal cortex represent numeric values informing future decisions during a complex two-player game. The tetraplegic study participant was implanted with a Utah electrode array in the anterior intraparietal area (AIP). We played a simplified variant of Black Jack with the participant while neuronal data was recorded. During the game two players are presented with numbers which are added up. Each time a number is presented the player has to decide to proceed or to stop. Once the first player stops or the score reaches a limit the turn passes on to the second player who tries to beat the score of the first player. Whoever is closer to the limit (without overshooting) wins the game. We found that many AIP neurons selectively responded to the face value of the presented number. Other neurons tracked the cumulative score or were selectively active for the upcoming decision of the study participant. Interestingly, some cells also kept track of the opponent’s score. Our findings show that parietal regions engaged in hand action control also represent numbers and their complex transformations. This is also the first demonstration of complex economic decisions being possible to track in single neuron activity in human AIP. Our findings show how tight are the links between parietal neural circuits underlying hand control, numerical cognition and complex decision-making.

There are many electrode types for recording and stimulating neural tissue, most of which necessitate direct contact with the target tissue. These electrodes range from large, scalp electrodes which are used to non-invasively record averaged, low frequency electrical signals from large areas/volumes of the brain, to penetrating microelectrodes which are implanted directly into neural tissue and interface with one or a few neurons. With the exception of scalp electrodes (which provide very low-resolution recordings), each of these electrodes requires a highly invasive, open brain surgical procedure for implantation, which is accompanied by significant risk to the patient. To mitigate this risk, a minimally invasive endovascular approach can be used. Several types of endovascular electrodes have been developed to be delivered into the blood vessels in the brain via a standard catheterization procedure. In this review, the existing body of research on the development and application of endovascular electrodes is presented. The capabilities of each of these endovascular electrodes is compared to commonly used direct-contact electrodes to demonstrate the relative efficacy of the devices. Potential clinical applications of endovascular recording and stimulation and the advantages of endovascular versus direct-contact approaches are presented.

Electronic health records represent a large data source for outcomes research, but the majority of EHR data is unstructured (e.g. free text of clinical notes) and not conducive to computational methods. While there are currently approaches to handle unstructured data, such as manual abstraction, structured proxy variables, and model-assisted abstraction, these methods are time-consuming, not scalable, and require clinical domain expertise. This paper aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. We trained selective prediction models to identify the presence of four distinct clinical variables in free-text pathology reports: primary cancer diagnosis of glioblastoma (GBM, n = 659), resection of rectal adenocarcinoma (RRA, n = 601), and two procedures for resection of rectal adenocarcinoma: abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601). Data were manually abstracted from pathology reports and used to train L1-regularized logistic regression models using term-frequency-inverse-document-frequency features. Data points that the model was unable to predict with high certainty were manually abstracted. All four selective prediction models achieved a test-set sensitivity, specificity, positive predictive value, and negative predictive value above 0.91. The use of selective prediction led to sizable gains in automation (anywhere from 57% to 95% reduction in manual abstraction of charts across the four outcomes). For our GBM classifier, the selective prediction model saw improvements to sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier. Selective prediction using utility-based probability thresholds can facilitate unstructured data extraction by giving “easy” charts to a model and “hard” charts to human abstractors, thus increasing efficiency while maintaining or improving accuracy.

Brain-machine interfaces (BMIs) can be transformative for people living with chronic paralysis. BMIs translate brain signals into computer commands, bypassing neurological impairments and enabling people with neurological injury or disease to control computers, robots, and more with nothing but thought. State-of-the-art BMIs have already made this future a reality in limited clinical trials. However, high performance BMIs currently require highly invasive electrodes in the brain. Device degradation limits longevity to about 5 years. Their field of view is small, restricting the number, and type, of applications possible. The next generation of BMI technology should include being longer lasting, less invasive, and scalable to sense activity from large regions of the brain. Functional ultrasound neuroimaging is a recently developed technique that meets these criteria. In this present study, we demonstrate the first online, closed-loop ultrasonic brain-machine interface. We used 2 Hz real-time functional ultrasound to measure the neurovascular activity of the posterior parietal cortex in two nonhuman primates (NHPs) as they performed memory-guided movements. We streamed neural signals into a classifier to predict the intended movement direction. These predictions controlled a behavioral task in real-time while the NHP did not produce overt movements. Both NHPs quickly succeeded in controlling up to eight independent directions using the BMI. Furthermore, we present a simple method to “pretrain” the BMI using data from previous sessions. This enables the BMI to work immediately from the start of a session without acquiring extensive additional training data. This work establishes, for the first time, the feasibility of an ultrasonic BMI and prepares for future work on a next generation of minimally invasive BMIs that can restore function to patients with neurological, physical, or even psychiatric impairments.

Speech brain-machine interfaces (BMI’s) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted, and mimed speech decoding have been achieved, results for internal speech decoding are sparse, and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. In this work, a tetraplegic participant with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. We found robust internal speech decoding from SMG single neuron activity, achieving up to 91% classification accuracy during an online task (chance level 12.5%). Evidence of shared neural representations between internal speech, word reading, and vocalized speech processes were found. SMG represented words in different languages (English/ Spanish) as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/ visual imagination). Activity in S1 was modulated by vocalized but not internal speech, suggesting no articulator movements of the vocal tract occurred during internal speech production. This works represents the first proof-of-concept for a high-performance internal speech BMI.

Cerebrovascular accidents (CVA) are a source of postoperative morbidity. Existing data on CVA after lung transplantation (LT) are limited. We aimed to evaluate the impact of CVA on LT survival.A retrospective analysis of LT recipients at the University of Texas Southwestern Medical Center was performed. Data was obtained from the institutional thoracic transplant database between January 2012 and December 2018, which consisted of 476 patients. Patients were stratified by the presence of a postoperative CVA. Univariate comparisons of baseline characteristics, operative variables, and postoperative outcomes between the cohorts were performed. Survival was analyzed by Kaplan-Meier method. Aalen's additive regression model was utilized to assess mortality hazard over time.The incidence of CVA was 4.2% (20/476). Lung allocation score was higher in the CVA cohort (46.2 [41.7, 57.3] vs. 41.5 [35.8, 52.2], p = 0.04). There were no significant differences in operative variables. CVA patients had longer initial intensive care unit (ICU) stays (316 h [251, 557] vs. 124 [85, 218], p 0.001) and longer length of stay (22 days [17, 53] vs. 15 [11, 26], p = 0.007). CVA patients required more ICU readmissions (35% vs. 15%, p = 0.02) and had a lower rates of home discharge (35% vs. 71%, p 0.001). Thirty-day mortality was higher in the CVA cohort (20% vs. 1.3%, p 0.001). Overall survival was lower in the CVA cohort (log rank p = 0.044).Postoperative CVA following LT was associated with longer ICU stays, more ICU readmissions, longer length of stay, and fewer home discharges. Thirty day and long-term mortality were significantly higher in the CVA group.

The responsiveness of primary somatosensory cortex (S1) to physical tactile stimuli is well documented but the extent to which it is modulated by vision is unresolved. Additionally, recent literature has suggested that tactile events are represented in S1 in a more complex, generalized manner than its long-established topographic organization. To better characterize S1 function, neural activity was recorded from a tetraplegic patient implanted with microelectrode arrays in S1 during 1s stroking touches to the forearm (evoking numb sensation) or finger (naturalistic sensation). Touch conditions included visually observed first person physical touches, physical touches without vision, and visual touches without physical contact which occurred either to a third person, an inanimate object, or the patient’s own body in virtual reality. Two major findings emerged from this dataset. The first was that vision strongly modulates S1 activity, but only if there is a physical element to the touch, suggesting that passive observation of touches is not sufficient to recruit S1 neurons. The second was that despite the location of the recording arrays in a putative arm area of S1, neural activity was able to represent both arm and finger touches in physical touch conditions. Arm touches were encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization, as well as in a more general manner encompassing larger areas of the body.

Background The health care systems of low-income countries have severely limited capacity to treat surgical diseases and conditions. There is limited information about which hospital mortality outcomes are suitable metrics in these settings. Methods We did a 1-year observational cohort study of patient admissions to the Surgery and the Obstetrics and Gynecology departments and of newborns delivered at a Ugandan secondary referral hospital. We examined the proportion of deaths captured by standardized metrics of mortality. Results There were 17,015 admissions and 9612 deliveries. A total of 847 deaths were documented: 385 (45.5%) admission deaths and 462 (54.5%) perinatal deaths. Less than one-third of admission deaths occurred during or after an operation (n = 126/385, 32.7%). Trauma and maternal mortality combined with perioperative mortality produced 79.2% (n = 305/385) of admission deaths. Of 462 perinatal deaths, 412 (90.1%) were stillborn, and 50 (10.9%) were early neonatal deaths. The combined metrics of the trauma mortality rate, maternal mortality ratio, thirty-day perioperative mortality rate, and perinatal mortality rate captured 89.8% (n = 761/847) of all deaths documented at the hospital. Conclusions The combination of perinatal, maternal, trauma, and perioperative mortality metrics captured most deaths documented at a Ugandan referral hospital.

The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.