Clinical Risk Factors Associated with Ambulatory Outcome in Acute Ischemic Stroke Patient Smokers Treated with Thrombolytic Therapy.

BACKGROUND: Patients who have suffered an acute ischemic stroke (AIS) and are smokers may have a better outcome following thrombolytic therapy when compared with non-smokers. While this finding is controversial, data on baseline clinical risk factors to predict treatment efficacy of thrombolytic therapy using ambulatory status in patients who suffered AIS and are smokers is not common. METHODS: Between 2010 and 2016, retrospective data on patients who have suffered an AIS and received recombinant tissue plasminogen activator (rtPA) were obtained from Greenville health system registry. Assessment of clinical risk factors and the likelihood of an improvement in post-stroke ambulation among smokers and non-smokers was carried out using multivariate logistic regression. RESULTS: Of 1001 patients, 70.8% were smokers and 29.2% non-smokers. Among the smokers and non-smokers, 74.6% and 84.6% improvement in ambulation respectively at discharge. The odds of improved ambulation decrease among smokers as age group increases compared to those below 50 [(60-69 years, aOR, 0.30, 95% C.I, 0.108-0.850, p < 0.05), (70-79 years aOR, 0.27, 95% C.I, 0.096-0.734, p < 0.05), (80+ years aOR, 0.16, 95% C.I, 0.057-0.430, P < 0.01). Patients with National Institute of Health Stroke Scale Score (NIHSS) score > 7 (reference <7) were 91% less likely to have improved ambulation among smokers and non-smokers (aOR, 0.09, 95% C.I, 0.055-0.155, P = 0.01), and (aOR, 0.08, 95% C.I, 0.027-0.214, P = 0.01) respectively. Atrial fibrillation was an independent predictor of decreased improvement in ambulation only among smokers (aOR, 0.58, 95% C.I, 0.356-0.928 P < 0.05). CONCLUSION: Our findings suggest that elderly smokers with atrial fibrillation would benefit more from aggressive management of atrial fibrillation than non-smokers.

HIV-Associated Neurocognitive Disorder (HAND): Relative Risk Factors.

This chapter will address the issue of risk for HIV-associated neurocognitive disorder (HAND), focusing on HIV-associated dementia (HAD), among persons living with HIV in relationship to the risk for other dementias. Advances in effective antiretroviral therapy (ART) have led to an increase in the prevalence of older persons surviving with HIV - in addition to older persons who become infected by HIV later in life. Hence, HIV is no longer a disease of younger persons, and additional attention has been brought to bear against the plight of older persons living with HIV - not only as it pertains to treatment but also to prevention. The additional risk caused by aging among older persons living with HIV is complex to asses, and HIV infection is a research area that requires a robust approach to multiple other factors causing neurocognitive impairment with older age. The long-term and potentially neurotoxic exposure to ART and the deleterious consequences of chronic infection with HIV and its associated neuro-inflammation have been described for health. This aids in the understanding of dementia risk factors in this patient population, but the comorbidities (HIV- and non-HIV-associated) occurring among older persons living with HIV must also be addressed to properly assess the overall impact on dementia risk in this group. This need also warrants our examination of the risk factors for other dementias (and comorbid dementias) in persons living with HIV versus the general population through the assessment and quantification of modifiable and non-modifiable risk factors identified as major contributors toward dementia.

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.