Predictors of Stroke Outcome Extracted from Multivariate Linear Discriminant Analysis or Neural Network Analysis.

AIM: The prediction of functional outcome is essential in the management of acute ischemic stroke patients. We aimed to explore the various prognostic factors with multivariate linear discriminant analysis or neural network analysis and evaluate the associations between candidate factors, baseline characteristics, and outcome. METHODS: Acute ischemic stroke patients (n=1,916) with premorbid modified Rankin Scale (mRS) scores of 0-2 were analyzed. The prediction models with multivariate linear discriminant analysis (quantification theory type II) and neural network analysis (log-linearized Gaussian mixture network) were used to predict poor functional outcome (mRS 3-6 at 3 months) with various prognostic factors added to age, sex, and initial neurological severity at admission. RESULTS: Both models revealed that several nutritional statuses and serum alkaline phosphatase (ALP) levels at admission improved the predictive ability. Of the 1,484 patients without missing data, 560 patients (37.7%) had poor outcomes. The patients with poor outcomes had higher ALP levels than those without (294.3±259.5 vs. 246.3±92.5 U/l, P＜0.001). Multivariable logistic analyses revealed that higher ALP levels (1-SD increase) were independently associated with poor stroke outcomes after adjusting for several confounding factors, including the neurological severity, malnutrition status, and inflammation (odds ratio 1.21, 95% confidence interval 1.02-1.49). Several nutritional indicators extracted from prediction models were also associated with poor outcome. CONCLUSION: Both the multivariate linear discriminant and neural network analyses identified the same indicators, such as nutritional status and serum ALP levels. These indicators were independently associated with functional stroke outcome.

The right hemisphere is important for driving-related cognitive function after stroke.

Considering quality of life (QOL) after stroke, car driving is one of the most important abilities for returning to the community. In this study, directed attention and sustained attention, which are thought to be crucial for driving, were examined. Identification of specific brain structure abnormalities associated with post-stroke cognitive dysfunction related to driving ability would help in determining fitness for car driving after stroke. Magnetic resonance imaging was performed in 57 post-stroke patients (51 men; mean age, 63 ± 11 years) who were assessed for attention deficit using a standardized test (the Clinical Assessment for Attention, CAT), which includes a Continuous Performance Test (CPT)-simple version (CPT-SRT), the Behavioral Inattention Test (BIT), and a driving simulator (handle task for dividing attention, and simple and selective reaction times for sustained attention). A statistical non-parametric map (SnPM) that displayed the association between lesion location and cognitive function for car driving was created. From the SnPM analysis, the overlay plots were localized to the right hemisphere during handling the hit task for bilateral sides (left hemisphere damage related to right-side neglect and right hemisphere damage related to left-side neglect) and during simple and selective reaction times (false recognition was related to damage of both hemispheres). A stepwise multiple linear regression analysis confirmed the importance of both hemispheres, especially the right hemisphere, for cognitive function and car driving ability. The present study demonstrated that the right hemisphere has a crucial role for maintaining directed attention and sustained attention, which maintain car driving ability, improving QOL for stroke survivors.

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms are complex and difficult to understand. In this study, we aimed to clarify the cross-sectional relationship between mood disorders and motor/cognitive functions in stroke patients. An artificial neural network architecture was devised to predict three types of mood disorders from 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients. The relationship between mood disorders and motor/cognitive functions were comprehensively analysed by performing input dimensionality reduction for the neural network. The receiver operating characteristic curve from the prediction exhibited a moderate to high area under the curve above 0.85. Moreover, the input dimensionality reduction retrieved the evaluation indices that are more strongly related to mood disorders. The analysis results suggest a stress threshold hypothesis, in which stroke-induced lesions promote stress vulnerability and may trigger mood disorders.