Browsing by Subject "Neurosurgery"

Neurosurgical and neurological conditions account for a significant disease burden worldwide, with low- and middle-income countries bearing more than 90% of the burden. Uganda is a low-income nation with a high demand for neuro care services but limited access, especially in rural and remote areas. Mobile health clinics, which have proven to be effective in other specialties, could be adapted to provide neurological care in such regions. The objective of this research was to establish the feasibility, appropriateness, and usability of mobile neuro clinics for providing neurological care to people in Uganda's rural and remote communities. Participants who met the inclusion criteria were invited to participate in an education session. Following the education session, the participants participated in an interview session to evaluate the feasibility, appropriateness, and usability of mobile neuro clinics. The education and interview tools were developed using the Consolidated Framework for Implementation Research (CFIR). To weight provider views, a sentiment weighted scale was used, with total aggregate sentiment scores greater than 42 in each CFIR domain indicating high feasibility, acceptability, and usability. All the assessed CFIR domains scored above sentiment score of 49. The implementation process domain (167) received the best overall sentiment score, followed by the implementation climate structure (141), inner setting domain (102), innovation domain (59), and outer setting domain (55). According to the findings of the research, mobile neuro clinics are feasible, appropriate, and usable in Uganda. To achieve the best results, however, careful planning and integration involving stakeholders from conceptualization to execution are required.

Background: Traumatic brain injury (TBI) has a disproportionate burden on low- and middle-income countries (LMICs) and cost-effective and culturally relevant measures are necessary to improve TBI care. This study aims to characterize emergency healthcare providers’ decision making when treating TBI patients, develop a machine learning-based model to predict TBI patient outcome, and conduct a decision curve analysis (DCA) to evaluate model clinical applicability. Methods: This study is twofold: 1) a secondary analysis of a TBI data registry with 4142 patients and 2) a survey examining physicians decision-making in treating 50 TBI patients in real time. Results: Five machine learning models were developed with AUCs ranging from 70.86% (Single C5.0 Ruleset) to 85.67% (Ensemble Model). DCA showed that all models exhibited a greater net benefit over ranges of clinical thresholds. The survey collected information on 50 patients providing insight on tools used by physicians in real-time when treating TBI patients as well as the unmet need patients at KCMC faced. Conclusions: This study is the first to use machine learning modeling and DCA in the context of TBI prognosis in Sub-Saharan Africa. Prognostic models have great potential within the decision-making process for treating TBI patients in LMIC health systems and such utility can be expanded through determining different threshold probabilities for various interventions.

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Background/ObjectivesAfrica has only 1% of the global neurosurgery workforce, despite having 14% of the global population and 15% of the global neurosurgical disease burden. Also, neurosurgical training is hampered by paucity of training institutions, dearth of training faculty, and deficiency of optimal training resources. The study appraises the current specialist neurosurgical workforce in Africa, evaluates the major neurosurgery training programs, and projects the 2030 workforce capacity using current growth trends. Methods The study involved systematic and gray literature search, with quantitative analysis of retrospective data on the neurosurgery workforce, qualitative evaluation of the major neurosurgery training programs for their strength, weaknesses, opportunities, and threats, and projection modeling of the workforce capacity up to year 2030. Results 1,974 neurosurgeons serve 1.3 billion people (density 0.15/100,000; ratio 1:678,740), in Africa, with the majority (1,271; 64.39%) in North Africa. There are 106 specialist neurosurgery training institutions in 26 African countries, with North Africa having 52 (49.05%) of the training centers. Training is heterogenous, with the major programs being the West African College of Surgeons (WACS) - 24 centers across 7 countries, and the College of Surgeons of East, Central and Southern Africa (COSECSA) - 17 centers in 8 countries. At the current linear growth rate of 74.2 neurosurgeons/year or exponential growth rate of 6.81% per annum, Africa will have 2,716 - 3,813 neurosurgeons by 2030, with a deficit of 4,795 - 11,953 neurosurgeons. The continent requires a scale-up of its linear growth rate to 663.4 - 1269.5 neurosurgeons/year, or exponential growth rate to 15.87% - 22.21% per annum to meet its needs. While North African countries will likely meet their 2030 workforce requirements, sub-Saharan African countries will have significant workforce deficits. Conclusion Despite a recent surge in neurosurgery residency training, the current state of Africa’s neurosurgery workforce is dire, and many countries will be unable to meet their workforce requirements by 2030 at current growth trends. A significant scale-up of the neurosurgery workforce is required in order to meet these targets.

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As technology advances the state of medical imaging, the capabilities of surgical tooling has remained stagnant, contributing to a rift between a surgeon’s ability to perceive and their ability to act. At this point in the evolution of surgical tooling, some level of action must be yielded to robotic control. This thesis describes the development and provides an evaluation of a first-prototype device for automated tumor removal. Specifically, the device combines a unique implementation of a three-dimensional scanner with a steerable cutting laser, enabling both sensing and cutting in a platform that can generate 3D images of relatively smooth surfaces to a precision beyond the ability of a human surgeon to act. This device will be used as a research platform to answer the important questions currently standing in the way of bringing automation into the operating room. This work outlines the foundational development of a device that could provide a significant improvement to patient outcomes and reduce operating costs by a magnitude not yet demonstrated in the field of surgical robotics.

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