Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.

Background: Prognostic prediction and the identification of prognostic factors are critical during the early period of atrial-fibrillation (AF)-related strokes as AF is associated with poor outcomes in stroke patients. Methods: Two independent datasets, namely, the Korean Atrial Fibrillation Evaluation Registry in Ischemic Stroke Patients (K-ATTENTION) and the Korea University Stroke Registry (KUSR), were used for internal and external validation, respectively. These datasets include common variables such as demographic, laboratory, and imaging findings during early hospitalization. Outcomes were unfavorable functional status with modified Rankin scores of 3 or higher and mortality at 3 months. We developed two machine learning models, namely, a tree-based model and a multi-layer perceptron (MLP), along with a baseline logistic regression model. The area under the receiver operating characteristic curve (AUROC) was used as the outcome metric. The Shapley additive explanation (SHAP) method was used to evaluate the contributions of variables. Results: Machine learning models outperformed logistic regression in predicting both outcomes. For 3-month unfavorable outcomes, MLP exhibited significantly higher AUROC values of 0.890 and 0.859 in internal and external validation sets, respectively, than those of logistic regression. For 3-month mortality, both machine learning models exhibited significantly higher AUROC values than the logistic regression for internal validation but not for external validation. The most significant predictor for both outcomes was the initial National Institute of Health and Stroke Scale. Conclusion: The explainable machine learning model can reliably predict short-term outcomes and identify high-risk patients with AF-related strokes.

Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning.

OBJECTIVE: To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. METHODS: This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant's caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen's kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. RESULTS: The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. CONCLUSION: This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

Positional headache induced by isolated middle cerebral artery dissection: Two case reports.

BACKGROUND: Isolated middle cerebral artery dissection is uncommon and occurs in patients reporting headaches as the only symptom. This makes intracranial artery dissection challenging to diagnose and treat.Case description: We describe two cases of positional headache caused by isolated middle cerebral artery dissection, confirmed using high-resolution magnetic resonance imaging. The two patients presented with sudden-onset headache, occurring when lying in the lateral decubitus position. When lying down in the decubitus position ipsilateral to the intracranial artery dissection, the headache aggravated and middle cerebral artery flow velocity increased on transcranial Doppler ultrasonography compared to when in the supine position. Both patients were treated with antiplatelet agents, and the headache completely resolved within 1-2 weeks. CONCLUSION: We recommend additional imaging studies evaluating intracranial artery dissection as a cause of positional headache.

Conversion From The Korean Dementia Screening Questionnaire To The Informant Questionnaire On Cognitive Decline In The Elderly.

PURPOSE: The ability to convert scores between cognitive measurements would facilitate the longitudinal assessment of cognition in clinical practice and the comparison and synthesis of cognitive data from international, multicenter, or longitudinal studies. The primary aim of this study was to apply a simple and reliable method for converting scores from the Korean Dementia Screening Questionnaire (KDSQ) to those of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). PATIENTS AND METHODS: A total of 627 participants, with and without cognitive dysfunction, received both the KDSQ and the IQCODE at the same visit. The scores of both tools were calculated, and equipercentile equating was used to create a method for converting scores from the KDSQ to the IQCODE. RESULTS: KDSQ scores were highly correlated with IQCODE scores (Pearson r = 0.905, P < 0.01). We developed scores for converting the KDSQ to the IQCODE using equipercentile equating and log-linear smoothing. We provide an easy-to-use table that enables the conversion of KDSQ scores to IQCODE scores. CONCLUSION: We delivered a simple and reliable method for converting scores from the KDSQ to the IQCODE. The conversion score table reported here enables direct and easy comparison of these cognitive measurements in older adults.