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Background and objectives Post-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke. Methods This retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3–6 months, defined as an adjusted z -score of less than − 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables. Results A total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts. Conclusion Our findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.

Background The impact of changes in physical activity after ischemic stroke (IS) on the subsequent myocardial infarction (MI) risk is not fully understood. We aimed to investigate the effects of changes in physical activity on the risk of MI after acute IS using data from the Korean National Health Insurance Services Database. Methods 224,764 patients newly diagnosed with IS between 2010 and 2016 who underwent two serial biannual health checkups were included. The participants were divided into four categories according to changes in their physical activity: persistent non-exercisers, new exercisers, exercise dropouts, and exercise maintainers. The primary outcome was a new diagnosis of incident MI. Multivariable Cox proportional models were used to assess the effects of changes in exercise habits on the risk of MI. Results After a median of 4.25 years of follow-up, 6,611 (2.94%) MI cases were observed. After adjusting for confounders, new exercisers and exercise maintainers were significantly associated with a lower risk of incident MI than persistent non-exercisers (aHR, 0.849; 95% CI, 0.792–0.911; P -value < 0.001; and aHR, 0.746; 95% CI, 0.696–0.801; P -value < 0.001, respectively). Effects were consistent across sexes, more pronounced in those > 65 years. Notably, any level of physical activity after stroke was associated with a reduced MI risk compared to no exercise. Conclusions In this nationwide cohort study, commencing or sustaining physical activity after an IS corresponded to a diminished likelihood of subsequent MI development. Advocating physical activity in ambulatory stroke survivors could potentially attenuate the prospective risk of MI. What is New? • Initiating or maintaining regular physical activity following an ischemic stroke (IS) diagnosis significantly reduces the risk of myocardial infarction (MI), regardless of physical activity intensity, age, or sex. • This large-scale cohort study emphasizes the crucial role of physical activity in post-stroke care and secondary prevention, offering a novel perspective on the relationship between physical activity and post-IS cardiovascular outcomes. What are the Clinical Implications? • Healthcare practitioners should prioritize promoting physical activity as an integral component of post-stroke care for older adults, aiming to reduce MI risk. • The findings underscore the importance of regular physical activity for both IS and MI prevention and highlight the potential for improved patient outcomes through lifestyle modifications.

Objectives We evaluated the impact of malnutrition as estimated by the controlling nutritional status (CONUT) score and prognostic nutritional index (PNI) on hemorrhagic transformation (HT) and stroke outcomes after intravenous thrombolysis (IVT). Materials and methods Using a multicenter registry database, we enrolled 808 patients with acute ischemic stroke who received IVT between August 2013 and May 2021. We defined malnutrition as a CONUT score ≥ 2 and low PNI. The primary outcome measure was the occurrence of symptomatic HT contributing to early neurologic deterioration (END-SHT) after IVT. Multivariable analysis was performed to analyze the association between CONUT score, PNI, and END-SHT after IVT. Results The rate of END-SHT was higher with increasing CONUT scores and PNI values. In the multivariable analysis, CONUT score ≥ 5 and low PNI were significantly associated with END-SHT (odds ratio [95% confidence interval], CONUT score ≥ 5: 12.23 [2.41–62.07], p = 0.003; low PNI: 4.98 [1.76–14.09], p = 0.003). The receiver operating characteristic curve showed that both the CONUT score and PNI had good predictive ability. The cutoff values for CONUT and PNI were 5 and 42.3, respectively, for END-SHT. Conclusion Malnutrition, as denoted by a higher CONUT score and lower PNI, was associated with END-SHT. The joint application of both nutritional markers could be useful in predicting END-SHT after IVT.

Blood pressure variability (BPV) is associated with higher cardiovascular morbidity risks; however, its association with cognitive decline remains unclear. We investigated whether higher BPV is associated with faster declines in cognitive function in ischemic stroke (IS) patients. Cognitive function was evaluated between April 2010 and August 2015 using the Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment in 1,240 Korean PICASSO participants. Patients for whom baseline and follow-up cognitive test results and at least five valid BP readings were available were included. A restricted maximum likelihood–based Mixed Model for Repeated Measures was used to compare changes in cognitive function over time. Among a total of 746 participants (64.6 ± 10.8 years; 35.9% female). Baseline mean-MMSE score was 24.9 ± 4.7. The median number of BP readings was 11. During a mean follow-up of 2.6 years, mean baseline and last follow-up MMSE scores were 25.4 ± 4.8 vs. 27.8 ± 4.4 (the lowest BPV group) and 23.9 ± 5.2 vs. 23.2 ± 5.9 (the highest BPV group). After adjusting for multiple variables, higher BPV was independently associated with faster cognitive decline over time. However, no significant intergroup difference in cognitive changes associated with mean systolic BP was observed. Further research is needed to elucidate how BPV might affect cognitive function.

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion‐symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof‐of‐concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single‐outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well‐defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single‐outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single‐outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single‐outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

Background Migraine is one of the most common primary headache disorders and a well-known risk factor for cardiovascular disorders. We aimed to investigate the association between migraine and major cardiovascular outcomes, including myocardial infarction (MI), ischemic stroke (IS), and cardiovascular death (CVD) in people with type 2 diabetes. Research design and methods A total of 2,229,598 people from the nationwide Korean National Health Insurance Service database with type 2 diabetes but without a previous history of MI and IS were included in this study. We identified patients over 20 years of age with migraine using the claim data of International Statistical Classification of Diseases Related Health Problems, Tenth Revision (ICD-10) code G43. The patients with migraine were divided according to their migraine aura status. Results Migraine was present in 6.3% of the study population. Cases observed for MI, IS, CVD, and all-cause death were 2.6%, 3.6%, 5.9%, and 7.9%, respectively. The diagnosis of migraine was significantly associated with an increased risk of MI, IS, and CVD. The results remained significant after adjusting for covariates, including age, sex, body mass index, alcohol intake, smoking habits, physical activity, economic status, hypertension history, dyslipidemia, and duration of type 2 diabetes (MI, adjusted hazard ratio [aHR]: 1.182, 95% confidence interval [CI]: 1.146–1.219; IS, aHR: 1.111, 95% CI 1.082–1.14; CVD, aHR: 1.143, 95% CI 1.12–1.167). In particular, the presence of aura was associated with a higher risk of MI development compared to the non-aura group. The difference became more prominent with progressing age. Conclusions In this nationwide population-based study, people with type 2 diabetes and migraines were found to be at a significantly higher risk for major cardiovascular events, including MI, IS, and CVD. The risk of MI and CVD significantly increased with the presence of aura symptoms among patients with migraine.

Background Futile reperfusion (poor functional status despite successful reperfusion) was observed in up to 67% of patients enrolled in recent endovascular treatment (EVT) clinical trials. We investigated the impact of baseline stroke severity on both futile reperfusion and therapeutic benefit of successful EVT. Methods Using a prospective multicenter stroke registry, we identified consecutive ischemic stroke patients with anterior circulation large artery occlusion, who were reperfused successfully by EVT (Thrombolysis in Cerebral Infarction grade 2b–3). The rate of futile reperfusion was assessed across the initial National Institutes of Health Stroke Scale (NIHSS) scores. The frequency of poor outcomes (modified Rankin scale [mRS] 3–6) according to NIHSS scores was compared between patients revascularized successfully by EVT and those who did not receive EVT, after standardizing for age. Results Among 21,591 patients with ischemic stroke, 972 (4.5%) received EVT within 12 h of onset, including 440 who met study eligibility criteria. Futile reperfusion was observed in 226 of the 440 study-eligible patients (51.4%) and was associated with stroke severity: 20.9% in NIHSS scores ≤5, 34.6% in 6–10, 58.9% in 11–20, and 63.8% in > 20 ( p  < 0.001). Nonetheless, the therapeutic benefit of EVT also increased with increasing stroke severity (p for interaction < 0.001): 0.1% in NIHSS ≤5, 18.6% in 6–10, 28.7% in 11–20, and 34.3% in > 20. Conclusions EVT is more beneficial with increasing stroke severity, although futile reperfusion also increases with higher stroke severity.

Although the obesity paradox is an important modifiable factor in cardiovascular diseases, little research has been conducted to determine how it affects post-stroke cognitive function. We aimed to investigate the association between body mass index (BMI) and domain-specific cognitive outcomes, focusing on the subdivision of each frontal domain function in post-ischemic stroke survivors. A total of 335 ischemic stroke patients were included in the study after completion of the Korean-Mini Mental Status Examination (K-MMSE) and the vascular cognitive impairment harmonization standards neuropsychological protocol at 3 months after stroke. Frontal lobe functions were analyzed using semantic/phonemic fluency, processing speed, and mental set shifting. Our study participants were categorized into four groups according to BMI quartiles. The z-scores of K-MMSE at 3 months differed significantly between the groups after adjustment for initial stroke severity ( p  = 0.014). Global cognitive function in stroke survivors in the Q1 (the lowest quartile) BMI group was significantly lower than those in Q2 and Q4 (the highest quartile) BMI groups (K-MMSE z-scores, Q1: − 2.10 ± 3.40 vs. Q2: 0.71 ± 1.95 and Q4: − 1.21 ± 1.65). Controlled oral word association test findings indicated that phonemic and semantic word fluency was lower in Q4 BMI group participants than in Q2 BMI group participants ( p  = 0.016 and p  = 0.023 respectively). BMI might differentially affect cognitive domains after ischemic stroke. Although being underweight may negatively affect global cognition post-stroke, obesity could induce frontal lobe dysfunctions, specifically phonemic and semantic word fluency.

Core technologies of the fourth industrial revolution include AI (Artificial Intelligence), IoT (Internet of Things), and Big Data. The field of effective use of these technologies is the field of navigation. However, in indoor navigation, the accuracy is very low due to objects and walls placed in various positions. In this paper, we propose an indoor location tracking technology based on LiFi (Light Fidelity) communication to determine the exact location of a user using location tracking technology and communication technology. First of all, the fingerprinting database is constructed by measuring the CIR (Channel Impulse Response) value of each SP (Sample Point) using the fingerprinting technique. Then we use the Weighted Fuzzy Matching Algorithm to track the user's location. The simulation results show that the performance of the LiFi environment is superior to that of the WiFi (Wireless Fidelity) environment.

Identifying acute ischemic stroke (AIS) among potential stroke cases is crucial for stroke research based on claims data. However, the accuracy of using the diagnostic codes of the International Classification of Diseases 10th revision was less than expected. From the National Health Insurance Service (NHIS) claims data, stroke cases admitted to the hospitals participating in the multicenter stroke registry (Clinical Research Collaboration for Stroke in Korea, CRCS-K) during the study period with principal or additional diagnosis codes of I60-I64 on the 10th revision of International Classification of Diseases were extracted. The datasets were randomly divided into development and validation sets with a ratio of 7:3. A stroke identification algorithm using the claims data was developed and validated through the linkage between the extracted datasets and the registry database. Altogether, 40,443 potential cases were extracted from the NHIS claims data, of which 31.7% were certified as AIS through linkage with the CRCS-K database. We selected 17 key identifiers from the claims data and developed 37 conditions through combinations of those key identifiers. The key identifiers comprised brain CT, MRI, use of tissue plasminogen activator, endovascular treatment, carotid endarterectomy or stenting, antithrombotics, anticoagulants, etc. The sensitivity, specificity, and diagnostic accuracy of the algorithm were 81.2%, 82.9%, and 82.4% in the development set, and 80.2%, 82.0%, and 81.4% in the validation set, respectively. Our stroke identification algorithm may be useful to grasp stroke burden in Korea. However, further efforts to refine the algorithm are necessary.