RESUMEN
Autonomic dysfunction is very common in patients with dementia, and its presence might also help in differential diagnosis among dementia subtypes. Various central nervous system structures affected in Alzheimer's disease (AD) are also implicated in the central autonomic nervous system (ANS) regulation. For example, deficits in central cholinergic function in AD could likely lead to autonomic dysfunction. We recently developed a simple, readily applicable evaluation for monitoring ANS disturbances in response to traumatic brain injury (TBI). This ability to monitor TBI allows for the possible detection and targeted prevention of long-term, detrimental brain responses caused by TBI that lead to neurodegenerative diseases such as AD. We randomly selected and extracted de-identified medical record information from subjects who have been assessed using the ANS evaluation protocol. Using machine learning strategies in the analysis of information from individual as well as a combination of ANS evaluation protocol components, we identified a novel prediction model that is effective in correctly segregating between cases with or without a documented history of TBI exposure. Results from our study support the hypothesis that trauma-induced ANS dysfunctions may contribute to clinical TBI features. Because autonomic dysfunction is very common in AD patients it is possible that TBI may also contribute to AD and/or other forms of dementia through these novel mechanisms. This study provides a novel prediction model to physiologically assess the likelihood of subjects with prior history of TBI to develop clinical TBI complications, such as AD.