| dc.description.abstract |
<p>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.</p>
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