History and Current State of Neurosurgery in the Gambia.

BACKGROUND: Despite global efforts to improve surgical care access, many low- and middle-income countries, especially in neurosurgery, face significant shortages. The Gambia exemplifies this, with only 1 fully qualified neurosurgeon serving its population of 2.5 million people. This scarcity results in higher morbidity and mortality. OBJECTIVE: We aim to document the history and current state of neurosurgery in the Gambia to raise awareness and promote neurosurgery development. METHODS: The study reviews the Gambia's health care system, infrastructure, neurosurgical history, workforce, disease burden, and progress, with information derived from reference sources as well as author experience and interviews with key partners in Gambian health care. RESULTS: Neurosurgery in the Gambia began in the 1970s, facing constraints due to competing health care demands. Significant progress occurred much later in the early 2010s, marked by the initiation of Banjul Neuro Missions and the establishment of a dedicated neurosurgery unit. We report significant progress with neurosurgical interventions in the past few years showcasing the unit's dedication to advancing neurosurgical care in the Gambia. However, challenges persist, including a lack of trained neurosurgeons, equipment shortages such as ventilators and diagnostic imaging. Financial barriers for patients, particularly related to the costs of computer tomography scans, pose significant hurdles, impacting the timely diagnosis and intervention for neurological conditions. CONCLUSIONS: Neurosurgery in the Gambia is progressing, but challenges like equipment scarcity hinder further progress. We emphasize the need for addressing cost barriers, improving infrastructure, and fostering research. Engaging the government and international collaborations are vital for sustained development in Gambian neurosurgery.

hglobal-Index: A Novel Author-Level Measure of the Diffusion of Scientific Ideas Among High-, Low-, and Middle-Income Countries.

OBJECTIVE: To describe an intuitive and useful method for measuring the global impact of a medical scholar's research ideas by examining cross-border citations (CBCs) of peer-reviewed neurosurgical publications. METHODS: Publication and citation data for a random sample of the top 50 most academically productive neurosurgeons were obtained from Scopus Application Programming Interface. We characterized an author-level global impact index analogous to the widely used h-index, the hglobal-index, defined as the number of published peer-reviewed manuscripts with at least the same number of CBCs. To uncover socioeconomic insights, we explored the hglobal-index for high-, middle-, and low-income countries. RESULTS: The median (interquartile range) number of publications and CBCs were 144 (62-255) and 2704 (959-5325), respectively. The median (interquartile range) h-index and hglobal-index were 42 (23-61) and 32 (17-38), respectively. Compared with neurosurgeons in the random sample, the 3 global neurosurgeons had the highest hglobal-indices in low-income countries at 17, 13, and 9, despite below-average h-index scores of 33, 38, and 19, respectively. CONCLUSION: This intuitive update to the h-index uses CBCs to measure the global impact of scientific research. The hglobal-index may provide insight into global diffusion of medical ideas, which can be used for social science research, author self-assessment, and academic promotion.

Global Neurosurgery: A Retrospective Cohort Study to Compare the Effectiveness of Two Training Methods in Resource-Poor Settings.

BACKGROUND AND OBJECTIVES: Many low- and middle-income countries are experiencing profound health care workforce shortages. Surgical subspecialists generally practice in large urban centers but are in high demand in rural areas. These subspecialists must be trained through sustainable programs to address this disparity. We quantitatively compared the relative effectiveness of 2 unique training models to advance neurosurgical skills in resource-poor settings where formally trained neurosurgeons are unavailable. METHODS: Neurosurgical procedure data were collected from 2 hospitals in Tanzania (Haydom Lutheran Hospital [HLH] and Bugando Medical Centre [BMC]), where 2 distinct training models ("Train Forward" and "Back-to-Back," respectively) were incorporated between 2005 and 2012. RESULTS: The most common procedures performed were ventriculoperitoneal shunt (BMC: 559, HLH: 72), spina bifida repair (BMC: 187, HLH: 54), craniotomy (BMC: 61, HLH: 19), bone elevation (BMC: 42, HLH: 32), and craniotomy and evacuation (BMC: 18, HLH: 34). The number of annual procedures at BMC increased from 148 in 2008 to 357 in 2012; at HLH, they increased from 18 in 2005 to 80 in 2010. Postoperative complications over time decreased or did not significantly change at both sites as the diversity of procedures increased. CONCLUSION: The Train Forward and Back-to-Back training models were associated with increased surgical volume and complexity without increased complications. However, only the Train Forward model resulted in local, autonomous training of surgical subspecialists after completion of the initial training period. Incorporating the Train Forward method into existing training programs in low- and middle-income countries may provide unique benefits over historic training practices.

Machine learning models to predict traumatic brain injury outcomes in Tanzania: Using delays to emergency care as predictors.

Constraints to emergency department resources may prevent the timely provision of care following a patient's arrival to the hospital. In-hospital delays may adversely affect health outcomes, particularly among trauma patients who require prompt management. Prognostic models can help optimize resource allocation thereby reducing in-hospital delays and improving trauma outcomes. The objective of this study was to investigate the predictive value of delays to emergency care in machine learning based traumatic brain injury (TBI) prognostic models. Our data source was a TBI registry from Kilimanjaro Christian Medical Centre Emergency Department in Moshi, Tanzania. We created twelve unique variables representing delays to emergency care and included them in eight different machine learning based TBI prognostic models that predict in-hospital outcome. Model performance was compared using the area under the receiver operating characteristic curve (AUC). Inclusion of our twelve time to care variables improved predictability in each of our eight prognostic models. Our Bayesian generalized linear model produced the largest AUC, with a value of 89.5 (95% CI: 88.8, 90.3). Time to care variables were among the most important predictors of in-hospital outcome in our best three performing models. In low-resource settings where delays to care are highly prevalent and contribute to high mortality rates, incorporation of care delays into prediction models that support clinical decision making may benefit both emergency medicine physicians and trauma patients by improving prognostication performance.

Deploying machine learning with messy, real world data in low- and middle-income countries: Developing a global health use case.

The rapid emergence of machine learning in the form of large-scale computational statistics and accumulation of data offers global health implementing partners an opportunity to adopt, adapt, and apply these techniques and technologies to low- and middle-income country (LMIC) contexts where we work. These benefits reside just out of the reach of many implementing partners because they lack the experience and specific skills to use them. Yet the growth of available analytical systems and exponential growth of data require the global digital health community to become conversant in this technology to continue to make contributions to help fulfill our missions. In this community case study, we describe the approach we took at IntraHealth International to inform the use case for machine learning in global health and development. We found that the data needed to take advantage of machine learning were plentiful and that an international, interdisciplinary team can be formed to collect, clean, and analyze the data at hand using cloud-based (e.g., Dropbox, Google Drive) and open source tools (e.g., R). We organized our work as a "sprint" lasting roughly 10 weeks in length so that we could rapidly prototype these approaches in order to achieve institutional buy in. Our initial sprint resulted in two requests in subsequent workplans for analytics using the data we compiled and directly impacted program implementation.

Neurosurgery Subspecialty Practice During a Pandemic: A Multicenter Analysis of Operative Practice in 7 U.S. Neurosurgery Departments During Coronavirus Disease 2019.

OBJECTIVE: Changes to neurosurgical practices during the coronavirus disease 2019 (COVID-19) pandemic have not been thoroughly analyzed. We report the effects of operative restrictions imposed under variable local COVID-19 infection rates and health care policies using a retrospective multicenter cohort study and highlight shifts in operative volumes and subspecialty practice. METHODS: Seven academic neurosurgery departments' neurosurgical case logs were collected; procedures in April 2020 (COVID-19 surge) and April 2019 (historical control) were analyzed overall and by 6 subspecialties. Patient acuity, surgical scheduling policies, and local surge levels were assessed. RESULTS: Operative volume during the COVID-19 surge decreased 58.5% from the previous year (602 vs. 1449, P = 0.001). COVID-19 infection rates within departments' counties correlated with decreased operative volume (r = 0.695, P = 0.04) and increased patient categorical acuity (P = 0.001). Spine procedure volume decreased by 63.9% (220 vs. 609, P = 0.002), for a significantly smaller proportion of overall practice during the COVID-19 surge (36.5%) versus the control period (42.0%) (P = 0.02). Vascular volume decreased by 39.5% (72 vs. 119, P = 0.01) but increased as a percentage of caseload (8.2% in 2019 vs. 12.0% in 2020, P = 0.04). Neuro-oncology procedure volume decreased by 45.5% (174 vs. 318, P = 0.04) but maintained a consistent proportion of all neurosurgeries (28.9% in 2020 vs. 21.9% in 2019, P = 0.09). Functional neurosurgery volume, which declined by 81.4% (41 vs. 220, P = 0.008), represented only 6.8% of cases during the pandemic versus 15.2% in 2019 (P = 0.02). CONCLUSIONS: Operative restrictions during the COVID-19 surge led to distinct shifts in neurosurgical practice, and local infective burden played a significant role in operative volume and patient acuity.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms. METHODS: TBI patients' data were prospectively collected in Kampala, Uganda, from 2016 to 2020. To predict good versus poor outcome at hospital discharge, we created deep neural network, shallow neural network, and elastic-net regularized logistic regression models. Predictors included 13 easily acquirable clinical variables. We assessed model performance with 5-fold cross-validation to calculate areas under both the receiver operating characteristic curve and precision-recall curve (AUPRC), in addition to standardized partial AUPRC to focus on comparisons at clinically relevant operating points. RESULTS: We included 2164 patients for model training, of which 12% had poor outcomes. The deep neural network performed best as measured by the area under the receiver operating characteristic curve (0.941) and standardized partial AUPRC in region maximizing recall (0.291), whereas the shallow neural network was best by the area under the precision-recall curve (0.770). In several other comparisons, the elastic-net regularized logistic regression was noninferior to the neural networks. CONCLUSIONS: We present the first use of deep learning for TBI prognostication, with an emphasis on LMICs, where there is great need for decision support to allocate limited resources. Optimal algorithm selection depends on the specific clinical setting; deep learning is not a panacea, though it may have a role in these efforts.

Corticosteroid Randomization after Significant Head Injury and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury Models Compared with a Machine Learning-Based Predictive Model from Tanzania.

Hospitals in low- and middle-income countries (LMICs) could benefit from decision support technologies to reduce time to triage, diagnosis, and surgery for patients with traumatic brain injury (TBI). Corticosteroid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) are robust examples of TBI prognostic models, although they have yet to be validated in Sub-Saharan Africa (SSA). Moreover, machine learning and improved data quality in LMICs provide an opportunity to develop context-specific, and potentially more accurate, prognostic models. We aim to externally validate CRASH and IMPACT on our TBI registry and compare their performances to that of the locally derived model (from the Kilimanjaro Christian Medical Center [KCMC]). We developed a machine learning-based prognostic model from a TBI registry collected at a regional referral hospital in Moshi, Tanzania. We also used the core CRASH and IMPACT online risk calculators to generate risk scores for each patient. We compared the discrimination (area under the curve [AUC]) and calibration before and after Platt scaling (Brier, Hosmer-Lemeshow Test, and calibration plots) for CRASH, IMPACT, and the KCMC model. The outcome of interest was unfavorable in-hospital outcome defined as a Glasgow Outcome Scale score of 1-3. There were 2972 patients included in the TBI registry, of whom 11% had an unfavorable outcome. The AUCs for the KCMC model, CRASH, and IMPACT were 0.919, 0.876, and 0.821, respectively. Prior to Platt scaling, CRASH was the best calibrated model (χ2 = 68.1) followed by IMPACT (χ2 = 380.9) and KCMC (χ2 = 1025.6). We provide the first SSA validation of the core CRASH and IMPACT models. The KCMC model had better discrimination than either of these. CRASH had the best calibration, although all model predictions could be successfully calibrated. The top performing models, KCMC and CRASH, were both developed using LMIC data, suggesting that locally derived models may outperform imported ones from different contexts of care. Further work is needed to externally validate the KCMC model.

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in optimizing patient outcomes and healthcare resource utilization. Machine learning may allow for effective predictions of patient outcomes both with and without surgery. Data from patients with TBI was collected prospectively at Mulago National Referral Hospital in Kampala, Uganda, from 2016 to 2019. One linear and six non-linear machine learning models were designed to predict good versus poor outcome near hospital discharge and internally validated using nested five-fold cross-validation. The 13 predictors included clinical variables easily acquired on admission and whether or not the patient received surgery. Using an elastic-net regularized logistic regression model (GLMnet), with predictions calibrated using Platt scaling, the probability of poor outcome was calculated for each patient both with and without surgery (with the difference quantifying the "individual treatment effect," ITE). Relative ITE represents the percent reduction in chance of poor outcome, equaling this ITE divided by the probability of poor outcome with no surgery. Ultimately, 1766 patients were included. Areas under the receiver operating characteristic curve (AUROCs) ranged from 83.1% (single C5.0 ruleset) to 88.5% (random forest), with the GLMnet at 87.5%. The two variables promoting good outcomes in the GLMnet model were high Glasgow Coma Scale score and receiving surgery. For the subgroup not receiving surgery, the median relative ITE was 42.9% (interquartile range [IQR], 32.7% to 53.5%); similarly, in those receiving surgery, it was 43.2% (IQR, 32.9% to 54.3%). We provide the first machine learning-based model to predict TBI outcomes with and without surgery in LMICs, thus enabling more effective surgical decision making in the resource-limited setting. Predicted ITE similarity between surgical and non-surgical groups suggests that, currently, patients are not being chosen optimally for neurosurgical intervention. Our clinical decision aid has the potential to improve outcomes.

Comprehending the lack of access to maternal and neonatal emergency care: Designing solutions based on a space-time approach.

OBJECTIVE: The objective of this study was to better understand how the lack of emergency child and obstetric care can be related to maternal and neonatal mortality levels. METHODS: We performed spatiotemporal geospatial analyses using data from Brazilian municipalities. An emergency service accessibility index was derived using the two-step floating catchment area (2SFCA) for 951 hospitals. Mortality data from 2000 to 2015 was used to characterize space-time trends. The data was overlapped using a spatial clusters analysis to identify regions with lack of emergency access and high mortality trends. RESULTS: From 2000 to 2015 Brazil the overall neonatal mortality rate varied from 11,42 to 11,71 by 1000 live births. The maternal mortality presented a slightly decrease from 2,98 to 2,88 by 100 thousand inhabitants. For neonatal mortality the Northeast and North regions presented the highest percentage of up trending. For maternal mortality the North region exhibited the higher volume of up trending. The accessibility index obtained highlighted large portions of the rural areas of the country without any coverage of obstetric or neonatal beds. CONCLUSIONS: The analyses highlighted regions with problems of mortality and access to maternal and newborn emergency services. This sequence of steps can be applied to other low and medium income countries as health situation analysis tool. SIGNIFICANCE STATEMENT: Low and middle income countries have greater disparities in access to emergency child and obstetric care. There is a lack of approaches capable to support analysis considering a spatiotemporal perspective for emergency care. Studies using Geographic Information System analysis for maternal and child care, are increasing in frequency. This approach can identify emergency child and obstetric care saturated or deprived regions. The sequence of steps designed here can help researchers, and policy makers to better design strategies aiming to improve emergency child and obstetric care.

An Attitude Survey and Assessment of the Feasibility, Acceptability, and Usability of a Traumatic Brain Injury Decision Support Tool in Uganda.

BACKGROUND: Traumatic brain injury (TBI) prognostic models are potential solutions to severe human and technical shortages. Although numerous TBI prognostic models have been developed, none are widely used in clinical practice, largely because of a lack of feasibility research to inform implementation. We previously developed a prognostic model and Web-based application for in-hospital TBI care in low-resource settings. In this study, we tested the feasibility, acceptability, and usability of the application with potential end-users. METHODS: We performed our feasibility assessment with providers involved in TBI care at both a regional and national referral hospital in Uganda. We collected qualitative and quantitative data on decision support needs, application ease of use, and implementation design. RESULTS: We completed 25 questionnaires on potential uses of the app and 11 semistructured feasibility interviews. Top-cited uses were informing the decision to operate, informing the decision to send the patient to intensive care, and counseling patients and relatives. Participants affirmed the potential of the application to support difficult triage situations, particularly in the setting of limited access to diagnostics and interventions, but were hesitant to use this technology with end-of-life decisions. Although all participants were satisfied with the application and agreed that it was easy to use, several expressed a need for this technology to be accessible by smartphone and offline. CONCLUSIONS: We elucidated several potential uses for our app and important contextual factors that will support future implementation. This investigation helps address an unmet need to determine the feasibility of TBI clinical decision support systems in low-resource settings.

Global neurosurgery: a scoping review detailing the current state of international neurosurgical outreach.

OBJECTIVE: Global neurosurgery is a rapidly emerging field that aims to address the worldwide shortages in neurosurgical care. Many published outreach efforts and initiatives exist to address the global disparity in neurosurgical care; however, there is no centralized report detailing these efforts. This scoping review aims to characterize the field of global neurosurgery by identifying partnerships between high-income countries (HICs) and low- and/or middle-income countries (LMICs) that seek to increase neurosurgical capacity. METHODS: A scoping review was conducted using the Arksey and O'Malley framework. A search was conducted in five electronic databases and the gray literature, defined as literature not published through traditional commercial or academic means, to identify studies describing global neurosurgery partnerships. Study selection and data extraction were performed by four independent reviewers, and any disagreements were settled by the team and ultimately the team lead. RESULTS: The original database search produced 2221 articles, which was reduced to 183 final articles after applying inclusion and exclusion criteria. These final articles, along with 9 additional gray literature references, captured 169 unique global neurosurgery collaborations between HICs and LMICs. Of this total, 103 (61%) collaborations involved surgical intervention, while local training of medical personnel, research, and education were done in 48%, 38%, and 30% of efforts, respectively. Many of the collaborations (100 [59%]) are ongoing, and 93 (55%) of them resulted in an increase in capacity within the LMIC involved. The largest proportion of efforts began between 2005-2009 (28%) and 2010-2014 (17%). The most frequently involved HICs were the United States, Canada, and France, whereas the most frequently involved LMICs were Uganda, Tanzania, and Kenya. CONCLUSIONS: This review provides a detailed overview of current global neurosurgery efforts, elucidates gaps in the existing literature, and identifies the LMICs that may benefit from further efforts to improve accessibility to essential neurosurgical care worldwide.

Estimating prognosis for traumatic brain injury patients in a low-resource setting: how do providers compare to the CRASH risk calculator?

OBJECTIVE: Traumatic brain injury (TBI), a burgeoning global health concern, is one condition that could benefit from prognostic modeling. Risk stratification of TBI patients on presentation to a health facility can support the prudent use of limited resources. The CRASH (Corticosteroid Randomisation After Significant Head Injury) model is a well-established prognostic model developed to augment complex decision-making. The authors' current study objective was to better understand in-hospital decision-making for TBI patients and determine whether data from the CRASH risk calculator influenced provider assessment of prognosis. METHODS: The authors performed a choice experiment using a simulated TBI case. All participant doctors received the same case, which included a patient history, vitals, and physical examination findings. Half the participants also received the CRASH risk score. Participants were asked to estimate the patient prognosis and decide the best next treatment step. The authors recruited a convenience sample of 28 doctors involved in TBI care at both a regional and a national referral hospital in Uganda. RESULTS: For the simulated case, the CRASH risk scores for 14-day mortality and an unfavorable outcome at 6 months were 51.4% (95% CI 42.8%, 59.8%) and 89.8% (95% CI 86.0%, 92.6%), respectively. Overall, participants were overoptimistic when estimating the patient prognosis. Risk estimates by doctors provided with the CRASH risk score were closer to that score than estimates made by doctors in the control group; this effect was more pronounced for inexperienced doctors. Surgery was selected as the best next step by 86% of respondents. CONCLUSIONS: This study was a novel assessment of a TBI prognostic model's influence on provider estimation of risk in a low-resource setting. Exposure to CRASH risk score data reduced overoptimistic prognostication by doctors, particularly among inexperienced providers.

An evaluation of outcomes in patients with traumatic brain injury at a referral hospital in Tanzania: evidence from a survival analysis.

OBJECTIVE: The purpose of this study was to determine if patients with traumatic brain injury (TBI) in low- and middle-income countries who receive surgery have better outcomes than patients with TBI who do not receive surgery, and whether this differs with severity of injury. METHODS: The authors generated a series of Kaplan-Meier plots and performed multiple Cox proportional hazard models to assess the relationship between TBI surgery and TBI severity. The TBI severity was categorized using admission Glasgow Coma Scale scores: mild (14, 15), moderate (9-13), or severe (3-8). The authors investigated outcomes from admission to hospital day 14. The outcome considered was the Glasgow Outcome Scale-Extended, categorized as poor outcome (1-4) and good outcome (5-8). The authors used TBI registry data collected from 2013 to 2017 at a regional referral hospital in Tanzania. RESULTS: Of the final 2502 patients, 609 (24%) received surgery and 1893 (76%) did not receive surgery. There were significantly fewer road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving surgery were also more likely to receive care in the ICU, to have a poor outcome, to have a moderate or severe TBI, and to stay in the hospital longer. The hazard ratio for patients with TBI who underwent operation versus those who did not was 0.17 (95% CI 0.06-0.49; p < 0.001) in patients with moderate TBI; 0.2 (95% CI 0.06-0.64; p = 0.01) for those with mild TBI, and 0.47 (95% CI 0.24-0.89; p = 0.02) for those with severe TBI. CONCLUSIONS: Those who received surgery for their TBI had a lower hazard for poor outcome than those who did not. Surgical intervention was associated with the greatest improvement in outcomes for moderate head injuries, followed by mild and severe injuries. The findings suggest a reprioritization of patients with moderate TBI-a drastic change to the traditional practice within low- and middle-income countries in which the most severely injured patients are prioritized for care.

Lifetime prevalence of intimate partner violence against women in an urban Brazilian city: A cross-sectional survey.

BACKGROUND: Intimate partner violence is a global health burden that disproportionately affects women and their health outcomes. Women in Brazil are also affected by interpersonal violence. We aimed to estimate the lifetime prevalence of three forms of interpersonal violence against women (IPVAW) and to identify sociodemographic factors associated with IPVAW in one urban Brazilian city. METHODS: Using a cross-sectional design, we interviewed women aged ≥18 years in the urban Brazilian city, Maringá, who currently have or have had an intimate partner. The 13-item WHO Violence Against Women instrument was used to ask participants about their experiences with intimate partner violence, categorized into psychological, physical and sexual violence. We estimated associations between IPVAW and sociodemographic characteristics using generalized linear models. RESULTS AND CONCLUSIONS: Of the 419 women who were enrolled and met inclusion criteria, lifetime prevalence of IPVAW was 56%. Psychological violence was more prevalent (52%) than physical (21%) or sexual violence (13%). Twenty-eight women (6.4%) experienced all three forms of IPVAW. Women were more likely to experience violence if they were employed, did not live with their partner or had 4 or more children. Educational level, household income, age and race were not significantly associated factors. Our findings highlight a high prevalence of IPVAW in a community in southern Brazil.

A traumatic brain injury prognostic model to support in-hospital triage in a low-income country: a machine learning-based approach.

OBJECTIVE: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide, with a disproportionate burden of this injury on low- and middle-income countries (LMICs). Limited access to diagnostic technologies and highly skilled providers combined with high patient volumes contributes to poor outcomes in LMICs. Prognostic modeling as a clinical decision support tool, in theory, could optimize the use of existing resources and support timely treatment decisions in LMICs. The objective of this study was to develop a machine learning-based prognostic model using data from Kilimanjaro Christian Medical Centre in Moshi, Tanzania. METHODS: This study is a secondary analysis of a TBI data registry including 3138 patients. The authors tested nine different machine learning techniques to identify the prognostic model with the greatest area under the receiver operating characteristic curve (AUC). Input data included demographics, vital signs, injury type, and treatment received. The outcome variable was the discharge score on the Glasgow Outcome Scale-Extended. RESULTS: The AUC for the prognostic models varied from 66.2% (k-nearest neighbors) to 86.5% (Bayesian generalized linear model). An increasing Glasgow Coma Scale score, increasing pulse oximetry values, and undergoing TBI surgery were predictive of a good recovery, while injuries suffered from a motor vehicle crash and increasing age were predictive of a poor recovery. CONCLUSIONS: The authors developed a TBI prognostic model with a substantial level of accuracy in a low-resource setting. Further research is needed to externally validate the model and test the algorithm as a clinical decision support tool.

Retrospective Analysis of Population Demographic Characteristics, Medical Interventions, and Resource Use at a Medical Student-Run Clinic on the Texas-Mexico Border.

PURPOSE: The study's purpose was to assess population demographics and resource utilization of the Medical Student Run Clinic, which provides primary care services to patients in El Paso, Texas along the Texas-Mexico border. METHODS: A retrospective cross-sectional chart review was performed on 760 patients evaluated at the medical student-run clinic between 2013 and 2016. Data included demographic characteristics, chief complaints, diagnoses, and interventions, which were analyzed with calculations of means, standard deviations, and percentages. RESULTS: Most (79.7%) patients were female; average age was 38.43 years; 91% of patients were Hispanic, and 66.8% spoke Spanish. Average BMI was 30.9 kg/m2. Less than 1% of patients presented with a psychiatric complaint; however, 17.9% screened positive for anxiety, and 16.5% screened positive for depression. CONCLUSIONS: This study shows that diabetes, hypertension, obesity, anxiety, and depression represent avenues for future patient-centered interventions and provide insight into challenges patients face along the border.

Trends in neurosurgical care in Western Uganda: an interrupted time series analysis.

OBJECTIVE: In addition to the rising burden of surgical disease globally, infrastructure and human resources for health remain a great challenge for low- and middle-income countries, especially in Uganda. In this study, the authors aim to explore the trends of neurosurgical care at a regional referral hospital in Uganda and assess the long-term impact of the institutional collaboration between Mulago National Referral Hospital and Duke University. METHODS: An interrupted time series is a quasi-experimental design used to evaluate the effects of an intervention on longitudinal data. The authors applied this design to evaluate the trends in monthly mortality rates for neurosurgery patients at Mbarara Regional Referral Hospital (MRRH) from March 2013 to October 2015. They used segmented regression and autoregressive integrated moving average models for the analysis. RESULTS: Over the study timeframe, MRRH experienced significant increases in referrals received (from 117 in 2013 to 211 in 2015), neurosurgery patients treated (from 337 in 2013 to 625 in 2015), and operations performed (from 61 in 2013 to 173 in 2015). Despite increasing patient volumes, the hospital achieved a significant reduction in hospital mortality during 2015 compared to prior years (p value = 0.0039). CONCLUSIONS: This interrupted time series analysis study showed improving trends of neurosurgical care in Western Uganda. There is a steady increase in volume accompanied by a sharp decrease in mortality through the years. Multiple factors are implicated in the significant increase in volume and decrease in mortality, including the addition of a part-time neurosurgeon, improvement in infrastructure, and increased experience. Further in-depth prospective studies exploring seasonality and long-term outcomes are warranted.

Thoracic Paraspinal Castleman Disease in a Young Mexican-American Man with Postcoital Hematuria.

BACKGROUND: Castleman disease (CD) is an uncommon disorder of deregulated lymphoproliferation with unicentric (UCD) and multicentric forms based on extent of nodal involvement. Gross resection with histopathologic analysis remains the gold standard for diagnosis of UCD and is curative in most cases. Symptomatic paraspinal UCD is a rare presentation with potentially dangerous complications, and its tendency to mimic more common spinal tumors presents a significant diagnostic challenge. CASE PRESENTATION: A 25-year-old Hispanic man with no past medical history was evaluated for a known left-sided paraspinal mass that was incidentally discovered during an emergency department work-up for hematuria. Computed tomography on initial presentation revealed a 5.3 cm × 3.3 cm × 4.8 cm heterogeneously enhancing left paraspinal mass adjacent to the T11 vertebral body with tonguelike extension into the T11-T12 neural foramen. Although he remained neurologically intact throughout most of the diagnostic work-up, an inconclusive biopsy, worsening hematuria, and late-onset radiculopathy with severe back pain prompted surgical intervention. Microscopic histomorphology was consistent with CD. He continued to have intermittent hematuria and dysuria postoperatively, but repeat computed tomography at 7 months confirmed no recurrence of the mass. CONCLUSIONS: Compared with previous reports, our case of postcoital hematuria and radiculopathy accompanying a paraspinal thoracic mass in a young Mexican-American man is a unique presentation. Awareness and early consideration of UCD in the work-up of a paraspinal mass may spare affected patients adverse and dangerous sequelae, such as spinal cord compression and excessive intraoperative hemorrhage.