Stroke Risk Assessment Research Articles

Integrated Fuzzy Cognitive Map and Chaotic Particle Swarm Optimization for Risk Assessment of Ischemic Stroke

Integrated Fuzzy Cognitive Map and Chaotic Particle Swarm Optimization for Risk Assessment of Ischemic Stroke

Predictive value of total atrial conduction time for developing cerebrovascular stroke in patients with sinus rhythm and normal left ventricular systolic function, a case-control study

Abstract Background Stroke is the second leading cause of death worldwide. Recently, atrial cardiopathy has become recognized as a substantial risk factor for cerebrovascular stroke. Greater LA size is linked to stroke in AF patients, but few data linking LA size to stroke in population with sinus rhythm. Purpose To investigate the value of total atrial conduction time (TACT) measured non-invasively by assessing PA-TDI duration as a predictor of stroke in patients who initially presented with sinus rhythm and have normal LV systolic function. Methods In our case-control study, we included 156 patients with acute ischemic stroke and 50 control subjects all presented in sinus rhythm and normal LV systolic function. All participants underwent echocardiography. The bi-plane modified Simpson's approach was used to determine the LV-EF. The left atrial (LA) diameter was measured also left atrial volume index (LAVI) was assessed. The PA-TDI interval was used to estimate the TACT. In the apical four-chamber view, the pulsed-wave tissue Doppler sample was positioned on the lateral and septal walls of the LA above the mitral annulus. The time between the onset of the P wave (lead II) and the peak of the A' wave was called the PA-TDI interval. Also, speckle tracking indices of LA strain were measured. Results LA volume index was larger in the stroke group (39.24±4 versus 33.6±2.8, p: 0.01), PA-TDI duration showed statistical highly significant difference between both groups (165.94±21.695 versus 99.4±22.960, p: <0.001). There were significantly impaired LAS parameters in the stroke group (LA reservoir, contraction & conduit strain, 22.7%, -6.6% & -16.1% versus 40%, -12%, and − 27% in the control group with P < 0.000). ROC curve showed the best cutoff point for LA volume index is >42.2 ml/m2 for prediction of stroke with area under the curve (AUC): 0.7, p value is 0.005. ROC curve showed the best cutoff point for PA-TDI is >155.5 ms. with AUC: 0.9, 90% sensitivity, 91% specificity, p value: <0.001. ROC curve showed that LA reservoir strain and LA stiffness can independently predict stroke at cut-off values of 30% & 24.5% respectively, P < 0.001. Multivariate logistic regression analysis showed that hypertension, LA volume index, LA stiffness and PA-TDI duration are independent predictors of stroke (p: < 0.05, < 0.05, < 0.01 and < 0.001 respectively). Conclusions The assessment of electromechanical asynchrony by measuring the PA-TDI duration may help to predict patients at risk for stroke. It is easy to measure without the need for special software like speckle tracking indices. We also advise patients who exceed the cutoff points of LA volume index and PA-TDI duration to receive antithrombotic therapy for better stroke prevention. Also we recommend adding PA-TDI to CHA2DS2-VASC-HSF score to assess stroke risk in patients with sinus rhythm and paroxysmal AF during their sinus rhythm intervals.

Separating Risk Prediction: Myocardial Infarction vs. Ischemic Stroke in 6.2M Screenings.

Traditional cardiovascular disease risk prediction models generate a combined risk assessment for myocardial infarction (MI) and ischemic stroke (IS), which may inadequately reflect the distinct etiologies and disparate risk factors of MI and IS. We aim to develop prediction models that separately estimate the risks of MI and IS. Our analysis included 6,242,404 individuals over 40 years old who participated in a cardiovascular health screening examination in 2009. Potential predictors were selected based on a literature review and the available data. Cox proportional hazards models were used to construct 5-year risk prediction models for MI, and IS. Model performance was assessed through discrimination and calibration. During a follow-up of 39,322,434.39 person-years, 89,140 individuals were diagnosed with MI and 116,259 with IS. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and family history. Statin use was factored into the classification of dyslipidemia. The c-indices for the prediction models were 0.709 (0.707-0.712) for MI, and 0.770 (0.768-0.772) for IS. Age and hypertension exhibited a more pronounced effect on IS risk prediction than MI, whereas smoking, body mass index, dyslipidemia, and chronic kidney disease showed the opposite effect. The models calibrated well for low-risk individuals. Our findings underscore the necessity of tailored risk assessments for MI and IS to facilitate the early detection and accurate identification of heterogeneous at-risk populations for atherosclerotic cardiovascular disease.

A Digital Twin Approach for Stroke Risk Assessment in Atrial Fibrillation Patients

Atrial fibrillation (AF) is associated with a fivefold increased risk of cerebrovascular events, contributing to 15-18% of all strokes. Stroke prevention in clinical practice is typically guided by the CHA2DS2-VASc score, which depends on general clinical risk factors but falls short in predicting risk at an individual patient level. In this study, we introduce a digital twin model of the left atrium (LA) combined with computational fluid dynamics (CFD) simulations to enhance personalized stroke risk assessment. Simulations were performed on patient-specific dynamic LA models in sinus rhythm (SR) across three patient groups: 10 controls (CTRL), 10 with paroxysmal AF (PAR-AF), and 10 with persistent AF (PER-AF). Blood flow velocity and areas susceptible to thrombogenesis, based on several factors including endothelial damage, were identified in the left atrial appendage (LAA). In general, control subjects exhibited higher average blood velocity in both the LAA and its ostium (0.11 ± 0.03 m/s and 0.28 ± 0.05 m/s, respectively) compared to those with AF. In the AF groups, the velocities were lower (LAA: PAR-AF 0.05 ± 0.02 m/s, PER-AF 0.04 ± 0.02 m/s; LAA ostium: PAR-AF 0.14 ± 0.03 m/s, PER-AF 0.11 ± 0.04 m/s). CFD analysis revealed that endothelial cell activation potential (ECAP) was significantly higher in AF patients (PAR-AF: 3.96 ± 3.28 Pa⁻1, PER-AF: 4.77 ± 2.08 Pa⁻1) compared to controls (0.93 ± 0.63 Pa⁻1). These findings suggest that AF patients experience slower and more oscillatory blood flow in the LAA, increasing their risk of thrombosis. Additionally, blood tends to stagnate within the LAA, further raising the likelihood of clot formation. This proposed method could be used to enhance stroke risk stratification in AF patients by incorporating an index that integrates blood velocity-derived parameters.

Correlating stroke risk with non-invasive cerebrovascular perfusion dynamics using a portable speckle contrast optical spectroscopy laser device.

Stroke poses a significant global health threat, with millions affected annually, leading to substantial morbidity and mortality. Current stroke risk assessment for the general population relies on markers such as demographics, blood tests, and comorbidities. A minimally invasive, clinically scalable, and cost-effective way to directly measure cerebral blood flow presents an opportunity. This opportunity has the potential to positively impact effective stroke risk assessment prevention and intervention. Physiological changes in the cerebrovascular system, particularly in response to hypercapnia and hypoxia during voluntary breath-holding can offer insights into stroke risk assessment. However, existing methods for measuring cerebral perfusion reserves, such as blood flow and blood volume changes, are limited by either invasiveness or impracticality. Herein we propose a non-invasive transcranial approach using speckle contrast optical spectroscopy (SCOS) to non-invasively monitor regional changes in brain blood flow and volume during breath-holding. Our study, conducted on 50 individuals classified into two groups (low-risk and higher-risk for stroke), shows significant differences in blood dynamic changes during breath-holding between the two groups, providing physiological insights for stroke risk assessment using a non-invasive quantification paradigm. Given its cost-effectiveness, scalability, portability, and simplicity, this laser-centric tool has significant potential for early diagnosis and treatment of stroke in the general population.

Retrieval-Augmented Generation for Extracting CHA2DS2-VASc Risk Factors from Unstructured Clinical Notes in Patients with Atrial Fibrillation.

Assessment of stroke risk in patients with atrial fibrillation (AF) is crucial for guiding anticoagulation therapy. CHA2DS2-VASc is a widely used score for defining this risk, but current assessments rely on manual calculation by clinicians or approximations from structured EHR data elements. Unstructured clinical notes contain rich information that could enhance risk assessment. We developed and validated a Retrieval-Augmented Generation (RAG) approach to extract CHA2DS2-VASc risk factors from unstructured notes in patients with AF. We employed a RAG architecture paired with the large language model, Llama3.1, to extract features relevant to CHA2DS2-VASc scores from unstructured notes. The model was deployed on a random set of 1,000 clinical notes (934 AF patients) from Yale New Haven Health System (YNHHS). To establish a gold standard, 2 clinicians manually reviewed and labeled CHA2DS2-VASc risk factors in a random subset of 200 notes. The CHA2DS2-VASc scores were calculated for each patient using structured data alone and by incorporating risk factors identified with RAG. We assessed performance across risk factors using macro-averaged area under the receiver operating characteristic (AUROC). For external validation, we utilized 100 manually labeled clinical notes from the MIMIC-IV database. The RAG model demonstrated robust performance in extracting risk factors from clinical notes. In the 1000 clinical notes, RAG identified several risk factors more frequently than structured elements, including hypertension (82.4% vs 26.2%), stroke/TIA (62.9% vs 45.5%), vascular disease (83.4% vs 56.6%), and diabetes (84.1% vs 47.2%). In the 200 expert-annotated notes, the RAG approach achieved high performance for various risk factors, with AUROCs ranging from 0.96 to 0.98 for hypertension, diabetes, and age ≥75 years. Incorporating risk factors identified by RAG increased CHA2DS2-VASc scores compared with using structured data alone. An LLM-optimized RAG can accurately extract CHA2DS2-VASc risk factors from unstructured clinical notes in AF patients. This approach can enable computable risk assessment and guide appropriate anticoagulation therapy.

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.

Prediction for Perioperative Stroke Using Intraoperative Parameters.

Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine-learning model incorporating both pre- and intraoperative variables to predict perioperative stroke. This study included patients who underwent noncardiac surgery at 2hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion-weighted imaging within 30 days of surgery. We developed a prediction model composed of pre- and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762-0.880) versus 0.584 (95% CI, 0.499-0.667; P<0.001) in the internal validation; and 0.716 (95% CI, 0.560-0.859) versus 0.505 (95% CI, 0.343-0.654; P=0.018) in the external validation, compared to the preoperative model. We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.

Cardiovascular risk factor assessment in late-onset seizures: A study protocol to assess the value of structured intervention.

A growing body of evidence suggests patients with late-onset seizures are at an increased risk of stroke, but the potential for reducing cardiovascular morbidity through risk factor screening and management is unknown. We aim to determine whether individuals with new-onset unprovoked seizures after middle age should undergo vascular risk assessment. The long follow-up needed to assess stroke risk and the known benefit of vascular risk factor modification make a standard RCT logistically and ethically challenging. Instead, we propose and have developed a protocol for a cluster project assessing the effect of vascular risk factor screening in an intervention trial as well as a cohort study. Participating neurology clinics will implement standard cardiovascular risk factor assessment into the routine evaluation for individuals aged ≥50 years attending their first specialized consultation after an unprovoked seizure, excluding those with progressive brain disease. The project has two interlinked components: a prospective single group trial, in which risk factor assessment is performed and subsequent management is followed for one year; and a register-based cohort study examining the long-term effects of the intervention on a system level by comparing patients attending initial consultations in the 2 years after start of the study, with patients seen in the four preceding years at the same clinics. The primary outcome of the intervention trial is the proportion of patients receiving subsequent pharmacological treatment. The primary outcome of the cohort study is the incidence of acute stroke in the Swedish Stroke Register. Swedish Ethical Review Authority approval (which is valid for 2 years only) will be sought when funding is obtained. The results will be disseminated through peer-reviewed scientific publications. The study will be registered at clinicaltrials.gov. A first seizure in a middle-aged or older person indicates a higher risk of stroke. It is not known whether investigating and treating blood pressure, blood cholesterol, or similar risk factors after a first seizure is an effective way to prevent stroke. A traditional clinical study would need too many patients and it would be unethical not to treat the control group. We have designed a study in which participating neurology departments change their practice to test and treat vascular risk factors. Patients are then compared to historic controls using registered data.

Comprehensive Analysis of Anticoagulant Therapy in Patients with Isolated Atrial Flutter

Limited comparative data exist regarding the risk of cardiogenic emboli in patients with isolated atrial flutter (AFL). Some studies suggest a lower complication risk in AFL compared to atrial fibrillation (AFib), but methodological limitations and conflicting reports necessitate a comprehensive investigation. Our analysis proposes that isolated AFL carries a lower risk of ischemic events and left atrial thrombus formation than AFib. Importantly, we caution against applying stroke risk assessment approaches designed for AFib to AFL patients, as it may lead to harmful overestimations and unnecessary anticoagulant prescriptions. Furthermore, we highlight the current lack of sufficient data to determine the overall clinical benefit of prolonged anticoagulant therapy in patients with isolated AFL, especially when CHA2DS2-VASc index values are below 4. This review challenges existing perceptions, offering insights into the nuanced risk profiles of the transitional nature of isolated AFL due to the high incidence of AFib development within a year of AFL diagnosis. In conclusion, tailored risk assessments and further research are essential for precise clinical decision-making in this dynamic landscape.

Association between the triglyceride-glucose (TyG) index and stroke risk in Chinese normal-weight adults: a population-based study

BackgroundIdentifying high-risk populations and promoting stroke prevention measures can be achieved through studies on stroke and its risk factors. As a new alternative indicator of insulin resistance (IR), the triglyceride glucose (TyG) index may potentially increase stroke risk. However, the evidence confirming this association is inadequate and inconsistent, possibly due to variations in stroke assessment criteria or characteristics of the study populations. This study aims to evaluate the association between the TyG index and stroke risk level among individuals with normal-weight.MethodsA total of 30,895 participants aged ≥ 40 years with normal-weight were enrolled in this study. The TyG index was calculated as Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. Normal-weight was described as a body mass index (BMI) of 18.5-<24.0 kg/m2. Stroke risk was assessed by the Stroke Risk Assessment Scale, developed by the China National Stroke Screening and Prevention Project. To evaluate the associations between the TyG index and stroke risk level, multivariate logistic regression models were employed.ResultsResults showed that when the TyG index was considered as a continuous variable, each one unit increase in the TyG index was associated with a significantly higher risk of stroke [Moderate-risk (OR, 2.15; 95% CI, 2.03–2.28; P<0.001); High-risk (OR, 3.83; 95% CI, 3.57–4.10; P<0.001)]. Compared with Q1 of the TyG index, Q4 was significantly associated with moderate stroke risk (OR, 2.73; 95% CI, 2.50–2.99; P<0.001) and high stroke risk (OR, 5.39; 95% CI, 4.83–6.01; P<0.001). The continuous TyG index was an important risk factor for high stroke risk in the metabolically obese, normal-weight (MONW) individuals (OR, 3.44;95% CI, 2.92–4.06; P < 0.001). In the MONW individuals, when Q1 was used as a reference, participants in Q4 (OR, 5.33; 95% CI, 4.19–6.78; P < 0.001) was significantly associated with high stroke risk. Subgroup analysis showed significant interaction in the age and sex subgroups in the overall population (Pinteraction <0.001).ConclusionThe risk of stroke is increased with the TyG index among Chinese adults of normal weight; hence, the index may be an important indicator for identifying high-risk stroke populations among individuals with normal body weight.

Neurofilament Light Chain and Risk of Stroke in Patients With Atrial Fibrillation.

Biomarkers reflecting brain injury are not routinely used in risk assessment of stroke in atrial fibrillation (AF). Neurofilament light chain (NFL) is a novel biomarker released into blood after cerebral insults. We investigated the association between plasma concentrations of NFL, other biomarkers, and risk of stroke and death in patients with AF not receiving oral anticoagulation. For this observational study, baseline plasma samples were available from 3077 patients with AF randomized to aspirin in ACTIVE A (Atrial Fibrillation Clopidogrel Trial With Irbesartan for Prevention of Vascular Events; 2003 to 2008) and AVERROES (Apixaban Versus Acetylsalicylic Acid [ASA] to Prevent Stroke in Atrial Fibrillation Patients Who Have Failed or Are Unsuitable for Vitamin K Antagonist Treatment; 2007 to 2009). Median follow-up was 1.5 years. NFL was analyzed with a Single Molecule Array (Simoa). Associations with outcomes (total stroke or systemic embolism, ischemic stroke, cardiovascular death, and all-cause death) were explored with Cox regression models. In the combined cohort, the median NFL level was 16.9 ng/L (interquartile range, 11.1-26.5 ng/L), the median age was 71 years, 58% were men, and 13% had a history of previous stroke. NFL was associated with older age, higher creatinine, lower body mass index, previous stroke, female sex, and diabetes but not cardiac rhythm. Higher NFL was associated with a higher risk of stroke or systemic embolism (n=206) independently of clinical characteristics (hazard ratio, 1.27 [95% CI, 1.10-1.46] per doubling of NFL) and other biomarkers (hazard ratio, 1.18 [95% CI, 1.01-1.37]) and including in patients without previous stroke (hazard ratio, 1.23 [95% CI, 1.02-1.48]). NFL was also independently associated with cardiovascular (n=219) and all-cause (n=311) death. The C index for stroke using only NFL was 0.642, on par with the currently used clinical risk scores. Addition of information on NFL improved discrimination in a model also including clinical information, NT-proBNP (N-terminal pro-B-type natriuretic peptide), and high-sensitivity cardiac troponin T, yielding a C index of 0.727. NFL reflects overt and covert episodes of cerebral ischemia and improves risk assessment of stroke and death in patients with AF without oral anticoagulation, including in patients without previous stroke. The combination of NFL with information on age, history of stroke, and other biomarkers should be explored as a future avenue for stroke risk assessments in patients with AF.

Atrial Fibrillation, Atrial Myopathy, and Thromboembolism: The Additive Value of Echocardiography and Possible New Horizons for Risk Stratification.

Atrial fibrillation (AF) is the most common cardiac sustained arrhythmia, and it is associated with increased stroke and dementia risk. While the established paradigm attributes these complications to blood stasis within the atria and subsequent thrombus formation with cerebral embolization, recent evidence suggests that atrial myopathy (AM) may play a key role. AM is characterized by structural and functional abnormalities of the atria, and can occur with or without AF. Moving beyond classifications based solely on episode duration, the 4S-AF characterization has offered a more comprehensive approach, incorporating patient's stroke risk, symptom severity, AF burden, and substrate assessment (including AM) for tailored treatment decisions. The "ABC" pathway emphasizes anticoagulation, symptom control, and cardiovascular risk modification and emerging evidence suggests broader benefits of early rhythm control strategies, potentially reducing stroke and dementia risk and improving clinical outcomes. However, a better integration of AM assessment into the current framework holds promise for further personalizing AF management and optimizing patient outcomes. This review explores the emerging concept of AM and its potential role as a risk factor for stroke and dementia and in AF patients' management strategies, highlighting the limitations of current risk stratification methods, like the CHA2DS2-VASc score. Echocardiography, particularly left atrial (LA) strain analysis, has shown to be a promising non-invasive tool for AM evaluation and recent studies suggest that LA strain analysis may be a more sensitive risk stratifier for thromboembolic events than AF itself, with some studies showing a stronger association between LA strain and thromboembolic events compared to traditional risk factors. Integrating it into routine clinical practice could improve patient management and targeted therapies for AF and potentially other thromboembolic events. Future studies are needed to explore the efficacy and safety of anticoagulation in AM patients with and without AF and to refine the diagnostic criteria for AM.

Diagnostic accuracy of carotid plaque instability by noninvasive imaging: a systematic review and meta-analysis.

There is increasing evidence that plaque instability in the extracranial carotid artery may lead to an increased stroke risk independently of the degree of stenosis. We aimed to determine diagnostic accuracy of vulnerable and stable plaque using noninvasive imaging modalities when compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis. Medline Ovid, Embase, Cochrane Library, and Web of Science were searched for diagnostic accuracy of noninvasive imaging modalities (CT, MRI, US) in the detection of 1) vulnerable/stable plaque, and 2) vulnerable/stable plaque characteristics, compared to histology. The quality of included studies was assessed by QUADAS-2 and univariate and bivariate random-effect meta-analyses were performed. We included 36 vulnerable and 5 stable plaque studies in the meta-analysis, and out of 211 plaque characteristics from remaining studies, we classified 169 as vulnerable and 42 as stable characteristics (28 CT, 120 MRI, 104 US characteristics). We found that MRI had high accuracy [90% (95% CI: 82-95%)] in the detection of vulnerable plaque, similar to CT [86% (95% CI: 76-92%); P > 0.05], whereas US showed less accuracy [80% (95% CI: 75-84%); P = 0.013]. CT showed high diagnostic accuracy in visualizing characteristics of vulnerable or stable plaques (89% and 90%) similar to MRI (86% and 89%; P > 0.05); however, US had lower accuracy (77%, P < 0.001 and 82%, P > 0.05). CT and MRI have a similar, high performance in detecting vulnerable carotid plaques, whereas US showed significantly less diagnostic accuracy. Moreover, MRI visualized all vulnerable plaque characteristics allowing for a better stroke risk assessment. PROSPERO ID CRD42022329690.

Intraplaque neovascularization combined with plaque elasticity for predicting ipsilateral stroke in patients with asymptomatic mild carotid stenosis.

Intraplaque neovascularization (IPN) is a biomarker for vulnerable atherosclerotic plaques and can be effectively visualized via contrast-enhanced ultrasound (CEUS). Plaque elasticity is influenced by elements such as lipid core and fibrosis and can be quantitatively assessed on shear wave elastography (SWE). Studies combining the use of CEUS and SWE for the assessment of stroke risk are currently lacking. Our study thus aimed to determine the predictive value of IPN combined with plaque elasticity among patients with asymptomatic carotid plaque. Consecutive patients with mild carotid stenosis who underwent CEUS and SWE were retrospectively analyzed. IPN was graded according to the presence and location of microbubbles within the plaque, while plaque elasticity was measured in terms of mean shear wave velocity (SWV). All patients were followed up for 6 months to monitor the development of ischemic stroke. The predictive values of IPN and SWV, individually and in combination, were assessed. A total of 121 patients were included, of whom 95 (78.5%) were male. The mean age was 63.1±10.7 years. Both grade 2 IPN [hazard ratio (HR) =2.37, 95% confidence interval (CI): 1.58-9.65; P=0.039] and SWV (HR =0.43, 95% CI: 0.20-0.95; P=0.038) were independently associated with future ischemic stroke events. The combined model demonstrated a significantly better predictive performance (HR =3.243, 95% CI: 1.87-6.17; P=0.027). The combination of IPN and SWV demonstrated significantly better predictive value for the risk of stroke. Our combined model thereby has the potential to guide the clinical stratification and management of patients with asymptomatic mild carotid stenosis.

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79–0.85) and 0.93 (95% CI: 0.9–0.95), respectively. These results not only highlight our system’s advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions.

WHAT SCALES SHOULD THE CARDIOLOGIST USE IN PATIENTS WITH ATRIAL FIBRILLATION? WHAT IS NEW?

The current treatment algorithm for patients with non-valvular atrial fibrillation (AF) includes anticoagulation to prevent stroke and systemic embolism, improvement of AF symptom control by heart rate reduction or restoration and maintenance of sinus rhythm, and treatment of cardiovascular and other comorbidities. The evaluation of patients with AF should be structured and include assessment of stroke risk, symptom severity, severity of the AF burden (type of arrhythmia, number and duration of episodes, etc.) and predisposing condition. The use of the CHA2DS2-VASc (risk of stroke), HAS-BLED (risk of bleeding), EHRA (severity of AF symptoms), and 2MACE (risk of cardiovascular outcomes) scales is important to help assess the likelihood of adverse outcomes and select the optimal treatment to protect not only against stroke but also against cardiovascular events. It should be noted that the HAS-BLED scale is primarily necessary for identification of bleeding risk factors, the modification of which allows to increase the safety of anticoagulant therapy, and a high index value according to this scale can’t serve as a reason to refuse anticoagulation in a patient with AF. New scales of stroke and hemorrhagic complications risk assessment in patients with AF on the basis of clinical parameters and laboratory biomarkers have been proposed, but their possible advantages over the existing indices need to be confirmed in special studies

Time trends in stroke risk management among high-risk patients with non-valvular atrial fibrillation in Australia between 2011–2019

BackgroundAtrial fibrillation (AF) is associated with stroke. Major changes to AF management recommendations in 2016–2018 advised that: 1. Stroke risk be estimated using the CHA2DS2-VA score; 2. Antiplatelet agents (APAs) do not effectively mitigate stroke risk; 3. Anticoagulation is prioritised above bleeding risk among high-risk patients; and 4. Non-vitamin K oral anticoagulants (NOACs) are used as first-line anticoagulants. AimTo examine trends in stroke risk management among high-risk patients with non-valvular AF in Australia between 2011–2019. MethodDe-identified data of patients were obtained from 164 separate general practices. Data included information on patient demographics, diagnoses, health risk factors and recent prescriptions. Patients with a diagnosis of non-valvular AF were identified and stroke risk was calculated by CHA2DS2-VA score. High risk patients (i.e. CHA2DS2-VA ≥ 2) were categorised as being managed by oral anticoagulants (OACs, i.e., warfarin or NOACs), APAs only, or neither (i.e., no OACs or APAs) and time trends in prescribing were examined. Multivariate analyses examined the characteristics of patients receiving the guideline recommended OAC management. ResultsData were available for 337,964 patients; 8696 (2.6 %) had AF. Most patients with AF (85.8 %, n = 7116) had high stroke risk. The proportion of high-risk patients managed on OACs increased from 56.7 % in 2011 to 73.7 % in 2019, while the proportion prescribed APAs declined from 31.1 % to 14.0 %. Those receiving neither treatment remained steady (around 12 %). Overall, 26.3 % of patients were inadequately anticoagulated at the end of the study period. There were no age or gender differences in receiving the guideline-recommended therapy, and patients with comorbidities associated with increased stroke risk were more likely to receive OAC therapy. ConclusionsStroke risk management among patients with AF has improved between 2011–2019, however there is still scope for further gains as many high-risk patients remain inadequately anticoagulated. Better stroke risk assessment by clinicians coupled with addressing practitioner concerns about bleeding risk may improve management of high-risk patients.

Interactive effects of Composite Dietary Antioxidant Index with Body Mass Index for the risk of stroke among U.S. adults: insight from NHANES 2001-2018.

This cross-sectional study aims to explore the interactive effects of the Composite Dietary Antioxidant Index (CDAI) and Body Mass Index (BMI) on stroke risk among U.S. adults, utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2018. The analysis involved 42,042 participants from a representative sample of non-institutionalized U.S. civilians, selected through a stratified, multistage probability sampling method. Dietary intake data were collected over two 24-h periods using the Automated Multiple-Pass Method. The study calculated a modified CDAI to assess dietary antioxidant intake, excluding supplements and water sources. Statistical methods included multivariable logistic regression and Generalized Additive Models (GAM) to evaluate the interaction between CDAI scores and BMI in relation to stroke risk, adjusting for a wide range of demographic, lifestyle, and health covariates. The research identified a significant interaction between CDAI scores and BMI categories in stroke risk assessment. While a negative correlation was observed between CDAI scores and stroke risk across the total population (OR 0.97, 95% CI 0.96-0.99), this relationship varied notably across different BMI groups. In participants with a BMI ≥25, a statistically significant negative association persisted, displaying a non-linear pattern. The study also revealed an inflection point in the CDAI score, indicating a shift in the relationship between dietary antioxidants and stroke risk. This study underscores the complex interaction between dietary antioxidant intake and BMI in determining stroke risk among U.S. adults. The findings suggest that individuals with higher BMI may experience more pronounced benefits from dietary antioxidants in stroke prevention. These insights could inform targeted dietary recommendations and public health strategies aimed at reducing stroke risk, particularly in populations with higher BMI. Further research is needed to fully understand these interactions and their implications for stroke prevention guidelines.

Sustained increase in annual transcranial Doppler screening rates in children with sickle cell disease: A quality improvement project.

Individuals with sickle cell disease (SCD) at increased risk for stroke should undergo annual stroke risk assessment using transcranial Doppler (TCD) screening between the ages of 2 and 16. Though this screening can significantly reduce morbidity associated with SCD, screening rates at Boston Children's Hospital (and nationwide) remain below the recommended 100% screening adherence rates. Three plan-do-study-act (PDSA) cycles were designed and implemented. The Specific, Measurable, Achievable, Relevant, and Time-Bound (SMART) aim of our quality improvement (QI) initiative was to sustainably increase the proportion of eligible patients receiving a TCD within 15months of their last TCD to greater than 95%. An interrupted time series (ITS) analysis was performed, comparing TCD adherence rates from PDSA Cycle 1 to those from PDSA Cycles 2 and 3. Mean TCD adherence increased across all three PDSA cycles, from a baseline of 67% in the first cycle (January 2015 to September 2020) to 92% in the third cycle (May 2021 to March 2023). In the ITS analysis of TCD adherence rates, there was a significant difference in the final TCD adherence rate achieved compared to the rate predicted, with a total estimated increase in adherence of 17.9% being attributable to the interventions from PDSA Cycles 2 and 3. Although other QI initiatives had demonstrated ability to increase adherence to TCD screening for patients with SCD, this is the first QI project to collect data over such a prolonged period of time to demonstrate a sustained increase in screening rates throughout the intervention (an 8-year period).