Around the world today, low- and middle-income countries (LMICs) have not benefited from advancements in neurosurgery; most have minimal or even no neurosurgical capacity in their entire country. In ...this paper, the authors examine in broad strokes the different ways in which individuals, organizations, and universities engage in global neurosurgery to address the global challenges faced in many LMICs. Key strategies include surgical camps, educational programs, training programs, health system strengthening projects, health policy changes/development, and advocacy. Global neurosurgery has begun coalescing with large strides taken to develop a coherent voice for this work. This large-scale collaboration via multilateral, multinational engagement is the only true solution to the issues we face in global neurosurgery. Key players have begun to come together toward this ultimate solution, and the future of global neurosurgery is bright.
According to recent estimates, at least 11% of the total global burden of disease is attributable to surgically-treatable diseases. In children, the burden is even more striking with up to 85% of ...children in low-income and middle-income countries (LMIC) having a surgically-treatable condition by age 15. Using population data from four countries, we estimated pediatric surgical needs amongst children residing in LMICs.
A cluster randomized cross-sectional countrywide household survey (Surgeons OverSeas Assessment of Surgical Need) was done in four countries (Rwanda, Sierra Leone, Nepal and Uganda) and included demographics, a verbal head to toe examination, and questions on access to care. Global estimates regarding surgical need among children were derived from combined data, accounting for country-level clustering.
A total of 13,806 participants were surveyed and 6,361 (46.1%) were children (0-18 years of age) with median age of 8 (Interquartile range IQR: 4-13) years. Overall, 19% (1,181/6,361) of children had a surgical need and 62% (738/1,181) of these children had at least one unmet need. Based on these estimates, the number of children living with a surgical need in these four LMICs is estimated at 3.7 million (95% CI: 3.4, 4.0 million). The highest percentage of unmet surgical conditions included head, face, and neck conditions, followed by conditions in the extremities. Over a third of the untreated conditions were masses while the overwhelming majority of treated conditions in all countries were wounds or burns.
Surgery has been elevated as an "indivisible, indispensable part of health care" in LMICs and the newly formed 2015 Sustainable Development Goals are noted as unachievable without the provision of surgical care. Given the large burden of pediatric surgical conditions in LMICs, scale-up of services for children is an essential component to improve pediatric health in LMICs.
Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the ...international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches.
To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings.
We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database.
ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038).
We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.
Traumatic Brain Injury (TBI) is disproportionally concentrated in low- and middle-income countries (LMICs), with the odds of dying from TBI in Uganda more than 4 times higher than in high income ...countries (HICs). The objectives of this study are to describe the processes of care and determine risk factors predictive of poor outcomes for TBI patients presenting to Mulago National Referral Hospital (MNRH), Kampala, Uganda.
We used a prospective neurosurgical registry based on Research Electronic Data Capture (REDCap) to systematically collect variables spanning 8 categories. Univariate and multivariate analysis were conducted to determine significant predictors of mortality.
563 TBI patients were enrolled from 1 June- 30 November 2016. 102 patients (18%) received surgery, 29 patients (5.1%) intended for surgery failed to receive it, and 251 patients (45%) received non-operative management. Overall mortality was 9.6%, which ranged from 4.7% for mild and moderate TBI to 55% for severe TBI patients with GCS 3-5. Within each TBI severity category, mortality differed by management pathway. Variables predictive of mortality were TBI severity, more than one intracranial bleed, failure to receive surgery, high dependency unit admission, ventilator support outside of surgery, and hospital arrival delayed by more than 4 hours.
The overall mortality rate of 9.6% in Uganda for TBI is high, and likely underestimates the true TBI mortality. Furthermore, the wide-ranging mortality (3-82%), high ICU fatality, and negative impact of care delays suggest shortcomings with the current triaging practices. Lack of surgical intervention when needed was highly predictive of mortality in TBI patients. Further research into the determinants of surgical interventions, quality of step-up care, and prolonged care delays are needed to better understand the complex interplay of variables that affect patient outcome. These insights guide the development of future interventions and resource allocation to improve patient outcomes.
Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and ...moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints.
To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models. The outcome can serve as a proxy for patients' functional status, even in mild and moderate patients with TBI.
Using a large data set (n = 5292) of patients with TBI from a quaternary care center and 84 predictors, including vitals, demographics, mechanism of injury, initial Glasgow Coma Scale, and comorbidities, we trained 6 different ML algorithms using a nested-stratified-cross-validation protocol. After optimizing hyperparameters and performing model selection, isotonic regression was applied to calibrate models.
When maximizing the microaveraged area under the receiver operating characteristic curve during hyperparameter optimization, a random forest model exhibited top performance. A random forest model was also selected when maximizing the microaveraged area under the precision-recall curve. For both models, the weighted average area under the receiver operating characteristic curves was 0.84 (95% CI 0.81-0.87) and the weighted average area under the precision-recall curves was 0.85 (95% CI 0.82-0.88).
Our group presents high-performing ML models to predict trichotomized discharge disposition. These models can assist in optimization of patient triage and treatment, especially in cases of mild and moderate TBI.
Introduction Uganda has a high demand for neurosurgical and neurological care. 78% of the over 50 million population reside in rural and remote communities where access to neurosurgical and ...neurological services is lacking. This study aimed to determine the feasibility, appropriateness, and usability of mobile neuro clinics (MNCs) in providing neurological care to rural and remote Ugandan populations. Methods Neurosurgery, neurology, and mobile health clinic providers participated in an education and interview session to assess the feasibility, appropriateness, and usability of the MNC intervention. A qualitative analysis of the interview responses using the constructs in the updated Consolidated Framework for Implementation Research was performed. Providers’ opinions were weighted using average sentiment scores on a novel sentiment-weighted scale adapted from the CFIR. A stakeholder analysis was also performed to assess the power and interest of the actors described by the participants. Results Twenty-one healthcare providers completed the study. Participants discussed the potential benefits and concerns of MNCs as well as potential barriers and critical incidents that could jeopardize the intervention. Of the five CFIR domains evaluated, variables in the implementation process domain showed the highest average sentiment scores, followed by the implementation climate constructs, inner setting, innovation, and outer setting domains. Furthermore, many interested stakeholders were identified with diverse roles and responsibilities for implementing MNCs. These findings demonstrate that MNC innovation is feasible, appropriate, and usable. Conclusion The findings of this study support the feasibility, appropriateness, and usability of MNCs in Uganda. However, integration of this innovation requires careful planning and stakeholder engagement at all levels to ensure the best possible outcomes.
Traumatic brain injury (TBI) accounts for the majority of Uganda's neurosurgical disease burden; however, invasive intracranial pressure (ICP) monitoring is infrequently used. Noninvasive monitoring ...could change the care of patients in such a setting through quick detection of elevated ICP.
Given the novelty of pupillometry in Uganda, this mixed methods study assessed the feasibility of pupillometry for noninvasive ICP monitoring for patients with TBI.
Twenty-two healthcare workers in Kampala, Uganda received education on pupillometry, practiced using the device on healthy volunteers, and completed interviews discussing pupillometry and its implementation. Interviews were assessed with qualitative analysis, while quantitative analysis evaluated learning time, measurement time, and accuracy of measurements by participants compared to a trainer's measurements.
Most participants (79%) reported a positive perception of pupillometry. Participants described the value of pupillometry in the care of patients during examination, monitoring, and intervention delivery. Commonly discussed concerns included pupillometry's cost, understanding, and maintenance needs. Perceived implementation challenges included device availability and contraindications for use. Participants suggested offering continued education and engaging hospital leadership as implementation strategies. During training, the average learning time was 13.5 minutes (IQR 3.5), and the measurement time was 50.6 seconds (IQR 11.8). Paired t-tests to evaluate accuracy showed no statistically significant difference in comparison measurements.
Pupillometry was considered acceptable for noninvasive ICP monitoring of patients with TBI, and pupillometer use was shown to be feasible during training. However, key concerns would need to be addressed during implementation to aid device utilization.
Objective
Effective surgical treatment of drug‐resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High‐frequency oscillations (HFOs) are potential biomarkers of the ...EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas.
Methods
We used novel liquid crystal polymer thin‐film μECoG arrays (.76–1.72‐mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80–250 Hz) and fast ripple (250–600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal‐to‐noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data.
Results
We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty‐seven percent of HFOs occurred on single 200‐μm‐diameter recording contacts, with minimal high‐frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95‐mm radius. Through spatial averaging, we estimated that macrocontacts with 2–3‐mm diameter would only capture 44% of the HFOs detected in our μECoG recordings.
Significance
These results demonstrate that thin‐film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug‐resistant epilepsy.
PDF
