African American patients have a higher probability of cognitive impairment after incident stroke: An analysis of national electronic health record data.

BACKGROUND: Cognitive impairment (CI) and stroke are diseases with significant disparities in race and geography. Post stroke cognitive impairment (PSCI) can be as high as 15-70% but few studies have utilized large administrative or electronic health records (EHR) to evaluate trends in PSCI. We utilized an EHR database to evaluate for disparities in PSCI in a large sample of patients after first recorded stroke to evaluate for disparities in race. METHODS: This is a retrospective cohort analysis of Cerner Health Facts® EHR database, which is comprised of EHR data from hundreds of hospitals/clinics in the US from 2009-2018. We evaluated patients ≥40 years of age with a first time ischemic stroke (IS) diagnosis for PSCI using ICD9/10 codes for both conditions. Patients with first stroke in the Cerner database and no pre-existing cognitive impairment were included, we compared hazard ratios for developing PSCI for patient characteristics RESULTS: A total of 150,142 IS patients with follow-up data and no pre-existing evidence of CI were evaluated. Traditional risk factors of age, female sex, kidney injury, hypertension, and hyperlipidemia were associated with PSCI. Only African American stroke survivors had a higher probability of developing PSCI compared to White survivors (HR 1.347, 95% CI (1.270, 1.428)) and this difference was most prominent in the South. Among those to develop PSCI, median time to documentation was 1.8 years in African American survivors. CONCLUSION: In a large national database, African American stroke survivors had a higher probability of PSCI five years after stroke than White survivors.

Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.

The rate of progression of Alzheimer's disease (AD) differs dramatically between patients. Identifying the most is critical because when their numbers differ between treated and control groups, it distorts the outcome, making it impossible to tell whether the treatment was beneficial. Much recent effort, then, has gone into identifying RPs. We pooled de-identified placebo-arm data of three randomized controlled trials (RCTs), EXPEDITION, EXPEDITION 2, and EXPEDITION 3, provided by Eli Lilly and Company. After processing, the data included 1603 mild-to-moderate AD patients with 80 weeks of longitudinal observations on neurocognitive health, brain volumes, and amyloid-beta (Aß) levels. RPs were defined by changes in four neurocognitive/functional health measures. We built deep learning models using recurrent neural networks with attention mechanisms to predict RPs by week 80 based on varying observation periods from baseline (e.g., 12, 28 weeks). Feature importance scores for RP prediction were computed and temporal feature trajectories were compared between RPs and non-RPs. Our evaluation and analysis focused on models trained with 28 weeks of observation. The models achieved robust internal validation area under the receiver operating characteristic (AUROCs) ranging from 0.80 (95% CI 0.79-0.82) to 0.82 (0.81-0.83), and the area under the precision-recall curve (AUPRCs) from 0.34 (0.32-0.36) to 0.46 (0.44-0.49). External validation AUROCs ranged from 0.75 (0.70-0.81) to 0.83 (0.82-0.84) and AUPRCs from 0.27 (0.25-0.29) to 0.45 (0.43-0.48). Aß plasma levels, regional brain volumetry, and neurocognitive health emerged as important factors for the model prediction. In addition, the trajectories were stratified between predicted RPs and non-RPs based on factors such as ventricular volumes and neurocognitive domains. Our findings will greatly aid clinical trialists in designing tests for new medications, representing a key step toward identifying effective new AD therapies.

Population-Based Mini-Mental State Examination Norms in Adults of Mexican Heritage in the Cameron County Hispanic Cohort.

BACKGROUND: Accurately identifying cognitive changes in Mexican American (MA) adults using the Mini-Mental State Examination (MMSE) requires knowledge of population-based norms for the MMSE, a scale which has widespread use in research settings. OBJECTIVE: To describe the distribution of MMSE scores in a large cohort of MA adults, assess the impact of MMSE requirements on their clinical trial eligibility, and explore which factors are most strongly associated with their MMSE scores. METHODS: Visits between 2004-2021 in the Cameron County Hispanic Cohort were analyzed. Eligible participants were ≥18 years old and of Mexican descent. MMSE distributions before and after stratification by age and years of education (YOE) were assessed, as was the proportion of trial-aged (50-85- year-old) participants with MMSE <24, a minimum MMSE cutoff most frequently used in Alzheimer's disease (AD) clinical trials. As a secondary analysis, random forest models were constructed to estimate the relative association of the MMSE with potentially relevant variables. RESULTS: The mean age of the sample set (n = 3,404) was 44.4 (SD, 16.0) years old and 64.5% female. Median MMSE was 28 (IQR, 28-29). The percentage of trial-aged participants (n = 1,267) with MMSE <24 was 18.6% overall and 54.3% among the subset with 0-4 YOE (n = 230). The five variables most associated with the MMSE in the study sample were education, age, exercise, C-reactive protein, and anxiety. CONCLUSION: The minimum MMSE cutoffs in most phase III prodromal-to-mild AD trials would exclude a significant proportion of trial-aged participants in this MA cohort, including over half of those with 0-4 YOE.

Mapping of the Somatosensory Cortex.

Intraoperative sensory cortical mapping is a reliable and safe method for the functional localization of the central sulcus (CS). It is utilized during neurosurgical procedures performed near eloquent brain tissue. It helps in identifying the somatosensory cortex and CS, hence preventing any postoperative neurological deficits. When executed properly, this method can identify the somatosensory cortex for both the upper and lower limbs by locating the CS. This technical report outlines the benefits of cortical sensory mapping (CsM) and detailed methodology. With the help of a properly trained intraoperative neuromonitoring staff who can accurately interpret the signals being monitored, CsM can help in injury prevention during brain surgeries.

Financial strain and cognitive-based smoking processes: The explanatory role of depressive symptoms among adult daily smokers.

Little work has focused on the underlying mechanisms that may link financial strain and smoking processes. The current study tested the hypothesis that financial strain would exert an indirect effect on cognitive-based smoking processes via depressive symptoms. Three clinically significant dependent variables linked to the maintenance of smoking were evaluated: negative affect reduction motives, negative mood abstinence expectancies, and perceived barriers for quitting. Participants included 102 adult daily smokers (Mage=33.0years, SD=13.60; 35.3% female) recruited from the community to participate in a self-guided (unaided; no psychological or pharmacological intervention) smoking cessation study. Results indicated that depressive symptoms explain, in part, the relation between financial strain and smoking motives for negative affect reduction, negative mood abstinence expectancies, and perceived barriers for quitting. Results indicate that smoking interventions for individuals with high levels of financial strain may potentially benefit from the addition of therapeutic tactics aimed at reducing depression.