A High-Tech Solution for the Low Resource Setting: A Tool to Support Decision Making for Patients with Traumatic Brain Injury

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2019

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Abstract

Background. The confluence of a capacity-exceeding disease burden and persistent resource shortages have resulted in traumatic brain injury’s (TBI) devastating impact in low and middle income countries (LMIC). Lifesaving care for TBI depends on accurate and timely decision making within the hospital. As result of technology and highly skilled provider shortages, treatment delays are common in low resource settings. This reality demands a low cost, scalable and accurate alternative to support decision making. Decision support tools leveraging the accuracy of modern prognostic modeling techniques represents one possible solution. This thesis is a collation of research dedicated to the advancement of TBI decision support technology in low resource settings. Methods. The study location included three national and referral hospitals in Uganda and Tanzania. We performed a survival analysis, externally validated existing TBI prognostic models, developed our own prognostic model, and performed a feasibility study for TBI decision support tools in an LMIC. Results. The survival analysis revealed a greater surgical benefit for mild and moderate head injuries compared to severe injuries. However, severe injury patients experienced a higher surgery rate than mild and moderate injuries. We developed a prognostic model using machine learning with a good level of accuracy. This model outperformed existing TBI models in regards to discrimination but not calibration. Our feasibility study captured the need for improved prognostication of TBI patients in the hospital. Conclusions. This pioneering work has provided a foundation for further investigation and implementation of TBI decision support technologies in low resource settings.

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Elahi, Cyrus (2019). A High-Tech Solution for the Low Resource Setting: A Tool to Support Decision Making for Patients with Traumatic Brain Injury. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/18879.

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