Predictor Of Stroke Research Articles

Moyamoya Periventricular Choroidal Collateral Registry-A Multicenter Cohort Study of Moyamoya Disease with Choroidal Anastomosis: Study Protocol.

Recent cohort studies on hemorrhagic and asymptomatic moyamoya disease have revealed that choroidal anastomosis, a type of fragile periventricular collateral pathway (periventricular anastomosis) typical of the disease, is an independent predictor of hemorrhagic stroke. However, treatment strategies for less-symptomatic nonhemorrhagic patients with choroidal anastomosis remain unclear. The Moyamoya Periventricular Choroidal Collateral (P-ChoC) Registry is an ongoing multicentered observational study that will test the hypothesis that extracranial-intracranial bypass prevents de novo hemorrhagic stroke in less symptomatic, nonhemorrhagic patients with choroidal anastomosis and may thus contribute to improving the prognosis of moyamoya disease. In this study, we report the study protocol of the moyamoya P-ChoC Registry and review the literature on choroidal anastomosis as a fragile collateral pathway.

Plasma metabolites, systolic blood pressure, lifestyle and stroke risk: a UK Biobank based cohort study.

Background To estimate the associations of stroke risk with plasma metabolites, metabolic risk score (MRS), the combinations of MRS with hypertension or lifestyle, and lifestyle-related metabolic signature. To assess the improvement of the stroke risk prediction model through the incorporation of MRS. Methods A total of 77,315 participants from the UK Biobank were included in this study. Xgboost and LASSO-COX regression were used to select metabolites and construct MRS. Elastic net regression was utilized to construct the lifestyle-related metabolic signature. Multivariate Cox regression was used to estimate the associations between metabolites, MRS, the combinations of MRS with hypertension or lifestyle, lifestyle-related metabolic signature and stroke risk. Results We identified 48, 63, 39, and 4 metabolites associated with the risk of stroke, ischemic stroke (IS), subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH), respectively. High MRS significantly increased the risk of stroke (HR= 2.65 [95%CI 2.09-3.35]), IS (HR= 2.45 [95%CI 1.89-3.17]), ICH (HR= 2.74 [95%CI 1.55-4.85]), and SAH (HR= 4.64 [95%CI 2.25-9.56]). In the combination analyses, compared with normal SBP and low MRS, normal/high SBP and high MRS significantly increased stroke risk (HR= 5.80 [95% CI: 2.75-12.27]/6.37 [95% CI 3.22-12.62]). A favorable/unfavorable lifestyle and high MRS also significantly increased stroke risk (HR= 2.38 [95% CI: 1.73-3.28]/3.86 [95% CI 2.63-5.67]) compared with a favorable lifestyle and low MRS. Incorporating MRS into the 15-year stroke and IS risk prediction model increased the AUCs from 0.746 to 0.766 and from 0.771 to 0.811, respectively. The metabolic signature was correlated with adherence to a healthy lifestyle (r = 0.414; P = 2.22e-16) and inversely associated with stroke risk (HR= 0.80 [95% CI 0.73-0.86]). Conclusions Various metabolites and MRS were significantly associated with the risk of stroke, IS, ICH and SAH. Individuals with a high MRS may face an elevated stroke risk among populations with high SBP or unhealthy lifestyle, even those with normal SBP or healthy lifestyle. MRS provided modest improvement to the stroke risk prediction model. The lifestyle-related metabolic signature could reduce 20% stroke risk.

Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy

Dynamic Perviousness of Thrombi in Acute Ischemic Stroke Predicts Clinical Outcome after Reperfusion Therapy

Exploring Feature Relationships in Brain Stroke Data Using Polynomial Feature Transformation and Linear Regression Modeling

A Cerebral vascular accident, commonly known as a stroke, is a pathological condition that impacts the brain due to the rupture of capillaries. It occurs when there is a disturbance in the typical blood circulation and essential physiological processes of the brain. Stroke prediction plays a crucial role in early diagnosis and intervention, potentially improving patient outcomes. This paper proposes a machine learning model that leverages polynomial feature transformation and linear regression modeling for stroke prediction. The model addresses the challenge of capturing non-linear relationships between features and the target variable while maintaining interpretability. The proposed approach involves preprocessing data by separating categorical and numerical features, applying one-hot encoding to categorical features, and generating polynomial features up to the second degree for numerical features. This tailored preprocessing is facilitated by a Column Transformer. For model development, a machine learning pipeline is constructed, splitting the data into training and testing sets. Despite utilizing polynomial features, linear regression is employed as the final model, allowing for the capture of both linear and non-linear relationships while maintaining interpretability. This work contributes to stroke prediction by offering a balanced approach that considers model complexity and interpretability, showcasing the potential of linear regression with polynomial features for accurate predictions and insights into feature-target relationships. The proposed model exhibited superior performance compared to other existing models, achieving a remarkable testing accuracy of 99.2%.

An intelligent learning system based on electronic health records for unbiased stroke prediction

Stroke has a negative impact on people’s lives and is one of the leading causes of death and disability worldwide. Early detection of symptoms can significantly help predict stroke and promote a healthy lifestyle. Researchers have developed several methods to predict strokes using machine learning (ML) techniques. However, the proposed systems have suffered from the following two main problems. The first problem is that the machine learning models are biased due to the uneven distribution of classes in the dataset. Recent research has not adequately addressed this problem, and no preventive measures have been taken. Synthetic Minority Oversampling (SMOTE) has been used to remove bias and balance the training of the proposed ML model. The second problem is to solve the problem of lower classification accuracy of machine learning models. We proposed a learning system that combines an autoencoder with a linear discriminant analysis (LDA) model to increase the accuracy of the proposed ML model for stroke prediction. Relevant features are extracted from the feature space using the autoencoder, and the extracted subset is then fed into the LDA model for stroke classification. The hyperparameters of the LDA model are found using a grid search strategy. However, the conventional accuracy metric does not truly reflect the performance of ML models. Therefore, we employed several evaluation metrics to validate the efficiency of the proposed model. Consequently, we evaluated the proposed model’s accuracy, sensitivity, specificity, area under the curve (AUC), and receiver operator characteristic (ROC). The experimental results show that the proposed model achieves a sensitivity and specificity of 98.51% and 97.56%, respectively, with an accuracy of 99.24% and a balanced accuracy of 98.00%.

Predictors of ischemic stroke in Chagas disease: Insights into mechanisms beyond cardiomyopathy severity

BackgroundChagas disease is a risk factor for ischemic stroke, which causes high mortality rates and significant disability. This study aims to determine the incidence and risk factors for ischemic strokes in a large cohort of Chagas cardiomyopathy patients, with a particular focus on the mechanisms involved in the pathophysiology of stroke in this condition. MethodsThe study enrolled 517 patients with Chagas cardiomyopathy who were referred to our institution from March 2000 to December 2021. All patients underwent systematic cardiological and neurological assessments. The primary outcome was the occurrence of ischemic stroke during the follow-up period, classified based on the SSS-TOAST and CCS criteria. Natural cubic splines functions were applied to examine the potential nonlinear association between continuous variables and stroke risk. ResultsThe mean age of the cohort was 52 ± 13 years, and 299 (58 %) were men. During a mean follow-up period of 4.8 years (interquartile range-IQR 1.1 to 7.1 years), a total of 72 patients (14.8 %) had an ischemic stroke, being fatal in 10. The overall incidence rate of ischemic stroke was 3.0/100 patient-years (95 % confidence interval 2.4 to 3.8). The stroke subtypes were cardioembolic (n = 41), undetermined (n = 11), and other subtypes (n = 20). The predictors of stroke were age, left atrial volume, left ventricular ejection fraction (LVEF), LV thrombus and prior stroke with thrombus. There was a nonlinear relationship between stroke risk, LVEF, and left atrial volume. A bimodal distribution of stroke occurrences was observed according to the severity of LV dysfunction, with a threshold for LVEF of 45 %. The final model for stroke risk prediction showed good discrimination, with a C statistic of 0.775. ConclusionsIn a contemporary cohort of Chagas disease patients with a broad spectrum of disease severity, stroke incidence remains high despite anticoagulation. Stroke risk shows a nonlinear association with ventricular dysfunction and left atrial size, highlighting a distinct bimodal pattern of stroke occurrence in Chagas disease.

The most efficient machine learning algorithms in stroke prediction: A systematic review.

Stroke is one of the most common causes of death worldwide, leading to numerous complications and significantly diminishing the quality of life for those affected. The purpose of this study is to systematically review published papers on stroke prediction using machine learning algorithms and introduce the most efficient machine learning algorithms and compare their performance. The papers have published in period from 2019 to August 2023. The authors conducted a systematic search in PubMed, Scopus, Web of Science, and IEEE using the keywords "Artificial Intelligence," "Predictive Modeling," "Machine Learning," "Stroke," and "Cerebrovascular Accident" from 2019 to August 2023. Twenty articles were included based on the inclusion criteria. The Random Forest (RF) algorithm was introduced as the best and most efficient stroke ML algorithm in 25% of the articles (n = 5). In addition, in other articles, Support Vector Machines (SVM), Stacking and XGBOOST, DSGD, COX& GBT, ANN, NB, and RXLM algorithms were introduced as the best and most efficient ML algorithms in stroke prediction. This research has shown a rapid increase in using ML algorithms to predict stroke, with significant improvements in model accuracy in recent years. However, no model has reached 100% accuracy or is entirely error-free. Variations in algorithm efficiency and accuracy stem from differences in sample sizes, datasets, and data types. Further studies should focus on consistent datasets, sample sizes, and data types for more reliable outcomes.

Non-alcoholic fatty liver disease: A new predictor of recurrent ischemic stroke and transient ischemic attack in patients with carotid atherosclerosis

PurposeNon-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. This study focused on assessing the predictive significance of NAFLD for recurrent stroke and transient ischemic attack (TIA) risk to determine the value of NAFLD. MethodThis study included 742 participants (mean age: 64.26 ± 9.42 years, 497 males) with carotid atherosclerosis who underwent carotid CT angiography (CTA) between January 2013 and December 2021 in this retrospective study. NAFLD was diagnosed by non-enhanced abdominal CT. The clinical endpoint was a recurrent ischemic stroke or TIA. Cox proportional hazards and Kaplan-Meier analysis assessed whether NAFLD was associated with the endpoint. We accessed the predictive values of NAFLD, clinical, plaque characteristics, and combined model by the C statistics. The predictive performance of the combined model was assessed by receiver operating characteristic curve (ROC) analysis. ResultsA total of 742 participants (mean age: 64.26 ± 9.42 years, 497 males) were included. During 2.9 years of follow-up (interquartile range, 2.1–3.9), 166 patients reached the clinical endpoint. Multivariable cox analyses showed NAFLD was associated with recurrent stroke or TIA in all groups (all P<0.05). Patients with NAFLD had a lower event-free survival (EFS) rate than those without NAFLD (P<0.05). The combined model, including NAFLD, clinical data and plaque features, showed the best performance in predicting the clinical endpoint (AUC=0.79). ConclusionsNAFLD contributes to the prediction of recurrent ischemic stroke or TIA. NAFLD may be a novel imaging marker that offers a new perspective on preventing cardiovascular disease in the clinic.

A New Perspective on the Prediction and Treatment of Stroke: The Role of Uric Acid

A New Perspective on the Prediction and Treatment of Stroke: The Role of Uric Acid

Additional predictors of stroke and transient ischaemic attack in BEFAST positive patients in out-of-hours emergency primary care

Introduction In patients suspected of stroke or transient ischemic attack (TIA), rapid triaging is imperative to improve clinical outcomes. For this purpose, balance-eye-face-arm-speech-time (BEFAST) items are used in out-of-hours primary care (OHS-PC). We explored the risk of stroke and TIA among BEFAST positive patients calling to the OHS-PC, and assessed whether additional predictors could improve risk stratification. Methods This is a cross-sectional study of retrospectively gathered routine care data from telephone triage tape-recordings of patients calling the OHS-PC with neurological deficit symptoms, classified as BEFAST positive. Four models–with the predictors age, sex, a history of cardiovascular or cerebrovascular disease, and cardiovascular risk factors–were fitted using logistic regression to predict the outcome stroke or TIA. Likelihood ratio testing was used to select the best model, which was subsequently internally validated. Results The risk of stroke or TIA diagnosis was 52% among 1,289 BEFAST positive patients, median age 72 years, 56% female sex. Of patients with the outcome stroke/TIA, 24% received a low urgency allocation, while 92% had signs or symptoms when calling. Only the addition of age and sex improved predicting stroke or TIA (internally validated c-statistic 0.72, 95%CI 0.69–0.75). The predicted risk of stroke or TIA remained below 20% in those aged below 40. Females aged 70 or over and males aged 55 or over, had a predicted risk above 50%. Discussion Urgency allocation appears to be suboptimal in BEFAST positive patients calling the OHS-PC. Risk stratification could be improved in this setting by adding age and sex.

Exploring the Link Between Genetic Predictors of Cardiovascular Disease and Psoriasis.

The epidemiological link between immune-mediated diseases (IMIDs) and cardiovascular disease has often been attributed to systemic inflammation. However, the direction of causality and the biological mechanisms linking cardiovascular disease with IMIDs are incompletely understood. Given the robust epidemiological association and the growing body of supportive mechanistic evidence, psoriasis is an exemplary IMID model for exploring this relationship. To assess the bidirectional relationships between genetic predictors of psoriasis and the 2 major forms of cardiovascular disease, coronary artery disease (CAD) and stroke, and to evaluate the association between genetic predictors of cardiovascular disease with 9 other IMIDs. This was a genetic association study using mendelian randomization (MR), a powerful genetic tool to help distinguish causation from associations observed in epidemiological studies, to provide supportive evidence for causality between traits. The study conducted 2-sample MR analyses using summary-level data from large-scale genome-wide association meta-analysis studies (GWAS) for each trait. The analysis focused on individuals of European descent from GWAS meta-analyses, involving CAD, stroke, psoriasis, and 9 other IMIDs. Data were analyzed from January 2023 to May 2024. Genetic predictors of CAD, stroke, psoriasis, and 9 other IMIDs. The primary outcomes were the associations of genetic predictors of CAD and stroke with the risk of psoriasis and 9 other IMIDs, determined using inverse-variance weighted (IVW) MR estimates. This study included 181 249 cases and 1 165 690 controls with CAD, 110 182 cases and 1 503 898 controls with stroke, 36 466 cases and 458 078 controls with psoriasis, for a total of approximately 3 400 000 individuals, and 9 other IMIDs. In contrast to previous assumptions, genetic predictors of psoriasis were found to have no association with CAD or stroke. In the reverse direction, genetic predictors of both CAD (MR estimate IVW odds ratio [OR], 1.07; 95% CI, 1.04-1.10; P = .003) and stroke (IVW OR, 1.22; 95% CI, 1.05-1.41; P = .01) were found to have risk-increasing associations with psoriasis. Adjusting for stroke rendered the associations of genetically predicted CAD with psoriasis risk nonsignificant (and vice versa), suggesting that a shared effect underlying genetic risk for CAD and stroke associates with increased psoriasis risk. No risk-increasing associations were observed for genetic predictors of cardiovascular disease with other common IMIDs, including rheumatoid arthritis and inflammatory bowel disease. Findings of this mendelian randomization study indicate that genetic predictors of cardiovascular disease were associated with increased psoriasis risk with no reciprocal effect or association with other IMIDs. Elucidating mechanisms underpinning this association could lead to novel therapeutic approaches in both diseases.

Association of diabetes and white blood cell count with stroke in patients with carotid artery dissection

BackgroundCarotid artery dissection is an important cause of stroke. However, the predictors of ischemic stroke in patients with carotid artery dissection are controversial. The study aimed to analyze the predictors of ischemic stroke in patients with carotid artery dissection through retrospective medical records.MethodsData of discharged patients diagnosed with carotid artery dissection during 2019–2023 were retrospectively collected. Based on the occurrence of ischemic stroke, the patients were divided into the ischemic stroke or non-ischemic stroke groups. Based on the results of univariate analyses, variables with an associated P value < 0.05 were introduced into the multivariable logistic regression analysis. .ResultsA total of 165 patients were included in the study, with an average age of 55.00 (48.00, 66.00) years, including 86 patients with internal carotid artery dissection and 79 patients with vertebral artery dissection. Ischemic stroke occurred in 69 patients with carotid artery dissection. Multivariate logistic regression analysis indicated that diabetes (odds ratio [OR]: 3.144, 95% confidence interval [CI]: 1.552–6.508, P<0.002) and high white blood cells count (OR: 1.157, 95% CI: 1.02–1.327,P = 0.028) were related to the incidence of ischemic stroke in patients with carotid artery dissection.ConclusionIschemic stroke caused by carotid artery dissection causes severe damage to the nervous system. This study found that diabetes and high white blood cells count were associated with the incidence of ischemic stroke in patients with carotid artery dissection. Therefore, monitoring and controlling blood glucose levels and infections is essential in patients with carotid artery dissection to reduce the incidence of stroke.

Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System

Strokes are a leading cause of disability and death worldwide, and timely diagnosis is critical for effective treatment. Prehospital stroke diagnosis by emergency medical services (EMS) is essential for ensuring prompt care. The FAST (Face, Arms, Speech, and Time) scoring system is a common tool used to identify stroke symptoms quickly. However, the traditional FAST system has limitations, including reliance on subjective assessments and potential misdiagnoses due to symptom variability. This study aims to optimize the FAST scoring system and compare its performance with various machine learning models to enhance stroke prediction accuracy. A dataset from an ambulance service, including prehospital data such as age, sex, and FAST test results, was used. Data preprocessing involved handling missing values, encoding categorical variables, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. A grid search was conducted to test different weighting schemes for the FAST system. Machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Decision Tree, were trained and evaluated using cross-validation on the training set and tested on a separate test set. The traditional FAST system showed an accuracy of 62.95%. Optimizing the FAST system through grid search marginally increased accuracy to 64.36%. However, machine learning models, particularly Random Forest, significantly outperformed the FAST system, achieving a test set accuracy of 88.54%. XGBoost also demonstrated strong performance with an accuracy of 77.07%, while Logistic Regression, SVM, and Decision Tree showed lower accuracies. These findings suggest that integrating machine learning models into EMS protocols could substantially improve the accuracy of prehospital stroke diagnosis, leading to quicker and more accurate identification and treatment of stroke patients. Although the optimized FAST system offers slight improvements, machine learning models present a promising avenue for enhancing stroke prediction and improving patient outcomes in emergency settings while acknowledging practicality issues.

Predicting stroke risk: An effective stroke prediction model based on neural networks

BackgroundStroke is the leading worldwide cause of disability and death. Effective stroke prevention and management depend on early identification of stroke risk. MethodsEight machine learning algorithms are applied to predict stroke risk using a well-curated dataset with pertinent clinical information. This paper describes a thorough investigation of stroke prediction using various machine learning methods. ResultsThe empirical evaluation yields encouraging results, with the logistic regression, support vector machine, and K-nearest neighbors models achieving an impressive accuracy of 95.04%, and the random forest and neural network models scoring even better, with accuracies of 95.10% and 95.16%, respectively. The neural network exhibits slightly superior performance, indicating its potential as a reliable model for stroke risk assessment. ConclusionsThe empirical evaluation underscores the ability of neural networks to discern intricate data relationships. These findings offer valuable insights for healthcare professionals and researchers, aiding in the development of improved stroke prevention strategies and timely interventions, ultimately enhancing patient outcomes.

Predictors of Ischemic Stroke After Acute Coronary Syndrome: A Retrospective Analysis.

Background Ischemic stroke (IS) is a fatal complication of acute coronary syndrome (ACS). Factors that speed up IS development after ACS are understudied, especially in developing countries like Pakistan. Thus, this study was designed to identify the potential risk factors of IS in patients with a preceding episode of ACS. Methodology This retrospective study was performed on 208 patients whose ACS and its consequent complications such as IS were managed in the cardiac and neurology units of Benazir Bhutto Hospital, Rawalpindi, from January 2022 to March 2023. Patients were enrolled via consecutive sampling and pre-defined inclusion and exclusion criteria. Before data collection, informed consent and ethical approval were obtained. Data were retrieved from the medical records of the patients. A self-structured proforma was applied to collect data. SPSS version 25 (IBM Corp., Armonk, NY, USA) was used for data analysis. The study variables between patients with and without IS were compared using descriptive and inferential statistics. The association between IS and its possible risk factors in patients who had previously experienced ACS was determined using multivariate logistic regression. Results Of the 208 enrolled patients, 24 (11.54%) had IS following ACS. Sedentary lifestyle (odds ratio (OR) = 3.099, 95% confidence interval (CI) = 1.025~4.219, p = 0.009), hypertension (OR = 3.060, 95% CI = 1.798~4.876, p = 0.002), diabetes mellitus (OR = 2.899, 95% CI = 1.126~4.112, p = 0.009), dyslipidemia (OR = 2.907, 95% CI = 1.332~4.254, p = 0.007), history of smoking (OR = 2.760, 95% CI = 1.234~4.122, p = 0.018), and non-adherence to ACS medication (OR = 2.966, 95% CI = 1.300~4.266, p = 0.030), were the risk factors of IS among patients with preceding ACS. Conclusions In the study population, the incidence of IS following ACS was significant. Sedentary lifestyle, hypertension, diabetes mellitus, dyslipidemia, smoking history, and non-compliance with ACS therapy all played a significant role in the development of IS in patients with prior ACS. Proper management of ACS and associated risk factors could lead to the prevention of serious complications such as IS.

Intima-Media Thickness in the Carotid Bifurcation is Related to Silent Brain Infarction: A Cross-Sectional Study.

Carotid intima-media thickness (IMT) measurement is used to assess subclinical atherosclerosis. We aimed to examine the association between the maximum IMT by location and the occurrence of silent brain infarction (SBI). Overall, 280 Japanese individuals (92 females, 52.6±5 years old) underwent a medical check-up at our hospital in Tokyo in 2015. Carotid IMT was measured at each site on ultrasound images (common carotid artery [CCA], internal carotid artery, or bifurcation). The risk factors for arterial dysfunction were evaluated. SBI was assessed using magnetic resonance imaging (MRI). The cross-sectional relationship between carotid maximum IMT and SBI was evaluated. Of the 280 individuals, 18 (6.4%) were diagnosed with SBI on MRI. The mean age of the SBI(-) and SBI(+) groups was 51.9±10.6 and 63.6±18.6 years, respectively. The correlation coefficients between the carotid maximum IMT at each location were very weak (correlation coefficient range: 0.180-0.253). The percentage of participants with SBI increased significantly with increasing maximum CCA and bIMT values. After adjusting for confounders, SBI was found to be significantly associated with the maximum bIMT (per 0.1-mm increase) (adjusted odds ratio [aOR], 1.10; 95% confidence interval [CI]: 1.03-1.17). When bIMT was categorized according to three groups (＜1.0 mm, 1.0-＜2.0 mm, and ≥ 2.0 mm), a significant SBI risk was also observed with an increase by each category of bIMT (aOR: 3.96, 95% CI: 1.63-9.52, P=0.002). The maximum bIMT was found to be the main determinant of SBI. A significant SBI risk was associated with an increase in each category of the maximum bIMT. Therefore, the maximum bIMT might be a useful predictor of future stroke in Japanese stroke-free medical check-up participants.

Interpretable prediction of acute ischemic stroke after hip fracture in patients 65 years and older based on machine learning and SHAP

BackgroundHip fracture and acute ischemic stroke (AIS) are prevalent conditions among the older population. The prognosis for older patients who experience AIS subsequent to hip fracture is frequently unfavorable. MethodsPatients were categorized into the AIS group and the non-AIS group. A predictive model was developed using six different machine learning algorithms. The SHapley Additive exPlanations (SHAP) method was then utilized to provide both local and global explanations. We performed adjusted mediation analyses. Furthermore, a nomogram was created to present the outcomes obtained from the LASSO regression examination. The main objective was to ascertain influential elements that can predict the occurrence of AIS. To alleviate the influence of confounding variables, propensity score matching was utilized to compare the occurrence of additional complications. Survival was compared by Kaplan-Meier methods. ResultsThe AUC of 6 ML models ranged from 0.73 to 0.87. The SVM model exhibited the greatest efficacy in forecasting AIS among older individuals with hip fractures. The leading 6 variables in the support vector machines (SVM) model were identified as systemic inflammatory response index (SIRI), carotid atherosclerosis, prior stroke, C-reactive protein (CRP), fibrinogen (FIB), and hypertension. The leading 2 variables in SHAP were identified as FIB at admission and SIRI index. There wasn't potential mediating effect of admission FIB between the SIRI index and AIS. There were statistically significant differences between the two groups in survival (P=0.003). ConclusionsThe model displayed good performance for prediction of AIS after hip fracture in patients 65 years and older, which might facilitate to establishment of a better clinical assessment plan.

Application Value of the Apolipoprotein in the Prediction of Acute Ischemic Death and in the Assessment of Functional Outcomes.

In recent years, research on the apolipoprotein E (APOE) gene has gradually proven that many diseases, including atherosclerosis, coronary heart disease, and neurological diseases, are closely related to ApoE gene diversity. However, the relationship between the APOE gene and the prediction and prognosis evaluation of ischemic stroke has not been determined or unified so far. The purpose of this study was to investigate the application value of APOE allele-4 combined with high-resolution vascular wall imaging in predicting the occurrence and prognosis of acute ischemic stroke. A total of 511 patients with acute ischemic stroke (AIS), who were admitted from January 2022 to December 2023, were included in the study, including 317 patients with intracranial artery stenosis. Blood lipids, lipoproteins, apolipoprotein E (including allelic typing), and lipoproteins (a) were measured in all cases, and high-resolution magnetic resonance imaging of the vascular walls was performed. At 6 months, the functional outcomes of the AIS patients were followed up, assessed by using the modified Rankin Scale (mRS) (a score of 2 - 6 was rated as poor prognosis), and the high-definition vascular wall imaging results were followed up as well. High-definition vascular wall imaging ensures the accurate location of vascular stenosis and the accurate diagnosis of acute stroke. There were no significant differences in the total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, or lipoprotein (a) in patients with and without intracranial artery stenosis, but the plasma apolipoprotein E (APOE) levels were significantly reduced in patients with intracranial artery stenosis (ICAS). At the 6-month follow-up, 230 patients with the APOE-ε4 gene were enrolled, out of which 104 had a poor prognosis (mRS score ≥ 2), accounting for 45.22%. Among 281 patients without the APOE-ε4 gene, 45 had a poor prognosis (mRS score ≥ 2), accounting for 16.01%. Patients with the APOE-ε4 gene had a worse functional prognosis after 6 months. It is suggested that low plasma APOE levels may be a high risk factor for ICAS in patients with acute ischemic stroke, and carrying the APOE-ε4 gene may be a high risk factor for a poor functional prognosis in AIS patients. The APOE-ε4 genotype, combined with high-resolution vascular wall imaging, has certain clinical application value in predicting the occurrence of acute ischemic death and evaluating the functional outcome.

Enhancing stroke prediction using the waikato environment for knowledge analysis

&lt;p&gt;State-of-the-art data mining tools incorporate advanced machine learning (ML) and artificial intelligence (AI) models, and it is widely used in classification, association rules, clustering, prediction, and sequential models. Data mining is important for the process of diagnosing and predicting diseases in the early stages, and this contributes greatly to the development of the health services sector. This study utilized classification to predict the stroke of a sample of the patient dataset that was taken from Kaggle. The classification model was created using the data mining program waikato environment for knowledge analysis (WEKA). This data mining tool helped identify individuals most at risk of stroke based on analysis of features extracted from the patient’s dataset. These features were used in classification processes according to the naive Bayes (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. Analysis of the classification results of the previous algorithms showed that the SVM outperformed other algorithms in terms of accuracy (94.4%), sensitivity (100%), and F-measure (97.1%). However, the NB algorithm had the best performance in terms of precision (95.7%).&lt;/p&gt;

Nutrition-associated markers and outcomes among patients receiving enteral nutrition after ischemic stroke: a retrospective cohort study

BackgroundEarly nutrition after acute ischemic stroke is crucial. We explored early enteral nutrition for stroke patients and evaluated changes in blood indicators as a predictor of stroke prognosis.MethodsAll hospitalized stroke patients receiving enteral nutrition were included in the study. We retrospectively collected the protein, energy, fat, and carbohydrate values for 7 days after admission. Serum albumin, total protein, and hemoglobin values were reviewed at admission and at one week. The main outcome indicators were the Modified Rankin Score, Barthel Index, and Quality of Life at 3 months.ResultsA total of 354 patients (mean age, 70.7 years; 59.0% male) were included. The change in serum albumin at day 7 relative to at admission was positively correlated with the Quality of Life score (p = 0.001), the Barthel Index (p = 0.004), and the modified Rankin Score (p = 0.029). The change in total protein at day 7 relative to at admission was positively correlated with the Quality of Life score (p = 0.002), the Barthel Index (p = 0.001), and the modified Rankin score (p = 0.011). The change in hemoglobin values at day 7 relative to at admission was positively correlated with the Barthel Index (p = 0.037 but not with the Quality of Life score (p = 0.237) or the modified Rankin score (p = 0.730).ConclusionsImproved nutrition-related blood indicators one week after admission were independently associated with good stroke outcomes. Nutritional support for acute ischemic stroke patients during the early hospitalization stage appears to be advisable.Trial registrationThis review was a retrospective cohort study. The study was retrospectively registered in the Chinese Clinical Trial Registry (No: ChiCTR2300077228). Registration date: 1/11/2023.