Stroke In High-risk Patients Research Articles

Understanding risk factors of post-stroke mortality

Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.

Trajectory and predictors of post-stroke depression among patients with newly diagnosed stroke: A prospective longitudinal study

BackgroundPost-stroke depression (PSD) is the most prevalent neuropsychological disorder among stroke patients, affecting approximately one-third of stroke survivors at any one time after a stroke. We identified between-person associations between post-stroke depression trajectories across 3 timepoints and predictors affecting trajectory classification among stroke patients. MethodsThis is a prospective longitudinal study using a convenience sample of 119 participants from 2 tertiary hospitals from March 2022 to September 2022. Clinical assessments and data collection were performed at diagnosis (T1), 3 months (T2), and 6 months (T3) after diagnosis. The instruments were Demographic and Disease Information Sheet and PROMIS-Depression 8a. Data were analyzed using SPSS 27.0 for descriptive statistics, logistic regression, and the Mplus program for growth mixture model analysis. ResultsTwo stroke survivors depression trajectory classes (Class 1, moderate level decreasing- [37.8 %], and Class 2, high level increasing- [62.2%]) were delineated. Class 1 experienced moderate depression post-stroke, with a smooth diminishing pattern at T2 and T3, while Class 2 had a higher baseline depressive score and a significant increase at T2 and T3. The best growth mixture model was Class 2 model (LMR, p=0.010, BLRT, p≤0.01, AIC=2611.934, BIC=2650.842, aBIC=2606.583, Entropy= 0.944). The logistic regression results revealed that Class 2 of depression trajectory had a significant association with a lower score on cognitive function (B=-5.29, 95%CI: -8.80, -1.78, p <0.05) compared with Class 1. The stroke type, marital status, and monthly income were predictors of the Class 2 depression trajectory group among stroke patients. Precisely, ischemic stroke is associated with lower risk of class 2 trajectory. ConclusionThe trajectory of post-stroke depression changes over time. This research has the potential to serve as a foundation for the assessment of high-risk stroke patients, the development of precise management programs, the implementation of risk stratification, and the enhancement of prognosis.

Animal Experimental Study of Bioabsorbable Left Atrial Appendage Occluder.

Left atrial appendage (LAA) closure can prevent stroke in high-risk patients with atrial fibrillation.A bioabsorbable LAA occluder made of degradable polymer materials, such as polydioxanone (PDO) and poly-L-lactic acid (PLA), and nitinol wire was used. Occluders were successfully implanted in 18 Chinese rural dogs, 2 of which died within 48 hours after operation due to pericardial tamponade and hemorrhage, respectively. Follow-up observation was performed after transcatheter LAA closure. New tissue was found on the surface of the occluder 2 months after operation. No adjacent structures such as the mitral valve and the left superior pulmonary vein were affected by the occluder discs. Hematoxylin and eosin (HE) staining was performed at 3 months after operation, which showed intact intimal structure on the occluder surface, and unabsorbed PDO and PLA were observed. Scanning electron microscopy showed irregular arrangement of endothelial cells. New endothelial tissue was observed to completely cover the occluder at 6 months after operation. Most PDOs were replaced by fibrous connective tissue, and scanning electron microscopy showed regularly arranged endothelial cells. Pathological examination at 12 months showed only a small remnant of PDO. The gross specimens of the liver, spleen, and kidneys and pathological examination did not indicate thromboembolism.The bioabsorbable LAA occluder made of PDO, PLA, and nitinol wire was safe and effective for the occlusion of LAA in dogs. The surface of the occluder was endothelialized half a year after operation. The absorbable materials of the occluder were degraded after 1 year.

Association of diverticulitis and potential risk of ischemic stroke: population-based matched cohort study in Taiwan.

Previous studies have suggested relationship between diverticular disease and cardiovascular disease. Since cardiovascular disease and cerebrovascular accident share a lot of pathogenesis, diverticulitis could also be a risk factor for stroke. This study tried to establish epidemiological evidence of the relationship between colon diverticulitis and ischemic stroke. In this retrospective cohort study, patients with newly diagnosed colon diverticulitis (N = 6238) and patients without colon diverticulitis (control group; N = 24 952) were recruited between January 1, 2000, and December 31, 2017. Both groups were matched by propensity score at a 1:4 ratio by age, sex, comorbidities and medications. Cox proportional hazard regression was applied to estimate the hazard ratio (HR) and 95% confidence interval (CI) of ischemic stroke. We also conducted 4 different regression models and 2 sensitivity analyses to test the robustness of our findings. The diverticulitis group had a higher risk of IS than the control group (adjusted HR, 1.25; 95% CI, 1.12-1.39; P < 0.001). Serial sensitivity analyses yielded consistent positive link between diverticulitis and IS. Further subgroup analysis showed that in the study group, the risk of IS was 2.54-fold higher than the matched controls in 30-39years. Our study found that colon diverticulitis was associated with a higher risk of developing subsequent ischemic stroke, especially for patients aged 30-39years, among Asian population. This result provides us a chance to undertake preventive measures for ischemic stroke in high-risk patients.

Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

Cardiovascular disease (CVD) stands as the foremost contributor to worldwide mortality, with strokes as part of significant CVD. Research on potential mortality risks and hospitalizations for stroke patients became crucial as a basis for evaluation to improve the quality and control of stroke patient services. Although machine learning technology has been widely used in health data analysis, understanding the relative performance and characteristics of machine learning (ML) models is still limited. Therefore, the study aims to broaden this understanding by comparing five ML models, namely XGBoost, Random Forest, Decision Trees, CatBoost, and Extra Trees, using stroke patient data from RSUD Banyumas Neural Poliklinik Indonesia. The model performance improvement process is the main focus, involving adjustments using the Optuna tuning library. Through this tuning approach, the key parameters of each ML model are optimally adjusted to improve their performance in predicting mortality risk and the duration of hospitalization for stroke patients. As a result, the XGBoost algorithm proved superior in predicting mortality (accuracy 86%, AUC 0.87) and the duration of hospitalization (accuracy 82%, AUC 0.79). This research has great potential to help hospitals identify high-risk stroke patients and plan more efficient treatment. This approach allows hospitals to use their resources better, improve medical services, and reduce unnecessary treatment costs

Awareness of stroke signs and symptoms among high-risk individuals in the Makkah Region, Saudi Arabia

Objective: To evaluate overall public awareness and determine knowledge gap in the early recognition of signs and symptoms of stroke among high-risk individuals in the Makkah region, and to assess the role of the physicians as a source of that knowledge. Methods: This cross-sectional study measured stroke awareness based on knowledge of warning signs and symptoms among high-risk patients in Makkah region during August 2021, using a previously validated survey through social media. Results: A total of 450 individuals were contacted, of which 292 participants were included in the study. Majority of the participants were female, aged 40–60 years, and were university educated. However, 69% were not aware of the signs and symptoms of stroke, only 37.7% were able to correctly identify limbs weakness as a symptom, and 63.7% had chosen loss of consciousness as the most common sign. Further, only 9.4% reported physicians to be the source of relevant information, while 57% chose the internet and social media. Conclusion: This study demonstrated that there is lack of awareness about the signs and symptoms of stroke in high-risk patients and that many participants possessed misguided information with significant absence of input from a physician.

Study of Cerebral Hemispheric Lateralization in Stroke and the Arterial Territories Involved in High-risk Patients

Introduction: According to WHO, Stroke is the 2nd leading cause of death and disability after Ischemic heart disease. Prevalence rate of stroke in India varies from 44.45 to 150 per lakh population. Studies have reported a higher frequency of stroke of the left hemisphere. The artery reported to be the most affected is Left Middle Cerebral artery(MCA) followed by Right Middle Cerebral artery. The aim of this study is to determine the side of cerebral hemisphere most affected by stroke and the arterial territories involved in high-risk patients aged above 35 years presenting to the tertiary healthcare centre, as well as to study the sex predilection of stroke. Materials and Methods: A cross-sectional prospective study of 50 high risk stroke patients with Diabetes Mellitus(DM) and /or Hypertension(HTN) presenting to General medicine OPD of a tertiary healthcare centre were investigated with CT/MRI for the side of the cerebral hemisphere affected and the arterial territories involved. The data was tabulated over the course of three months and statistical analysis was carried out. Results: The left cerebral hemisphere was affected in 50%, right in 40% and bilateral involvement was found in 10% patients. Slight predilection of stroke towards the left was seen, however it was not found to be statistically significant (P-value= 0.249). MCA territory was involved in 66% of the patients, Posterior Cerebral Artery(PCA) territory in 8%, Vertebrobasilar Artery(VB) territory in 8%, Anterior Cerebral Artery(ACA) territory in 6% and MCA+ACA in 4%, MCA+VB in 2% and MCA-PCA watershed in 6% patients. Conclusions: We found a slight predilection of stroke towards the left, which was however, not found to be statistically significant. We also found that Middle Cerebral Artery(MCA) territory is the most commonly involved in stroke.

Impact of prior use of antiplatelet agents and non-vitamin K antagonist oral anticoagulants on stroke outcomes among endovascular-treated patients with high pre-stroke CHA2DS2-VASc score

BackgroundWe assessed the influence of prior non-vitamin K antagonist (NOAC) use on stroke outcomes after endovascular treatment (EVT) in patients at a high risk of stroke based on their pre-stroke...

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm.

Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data. We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models. In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke. Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

Opportunistic screening for atrial fibrillation using a photoplethysmography technique in geriatric patients, a preliminary analysis of the Dutch-GERAF Study

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Happitech. Provided the mobile photoplethysmography application Background Undiagnosed atrial fibrillation (AF) poses a great risk for stroke in high-risk patients. Different screening strategies have been developed to identify these patients. Geriatric patients often have multimorbidity, and are to be considered a population at very high risk for development of AF and cardiovascular events, but may have less access to wearable screening devices. Purpose The Dutch multicentre study into opportunistically screening of Geriatric patients for Atrial Fibrillation using a PPG smartphone App (the Dutch-GERAF Study) applies opportunistic screening to ambulatory geriatric patients, to identify new cases of AF and optimize stroke prevention. This preliminary analysis describes the feasibility and willingness to use of a smart phone photoplethysmography (PPG) application in an ambulatory cohort. Method Analysis of participation data and PPG use, of all included patients aged 70 years or older within the GERAF study. The GERAF study is a multicentre study on opportunistic screening for AF using a PPG application among patients visiting a geriatric outpatient clinic. All patients without a pacemaker/ICD, severe tremor, or severe dementia were invited to participate, during a 6 months screening period. Patients were stimulated to use the application at home on their own mobile device at least 3 times within 6 months following inclusion. If preferred by the patient, the application was installed on the mobile device of a spouse or other family member. The PPG signals were analysed using the commercially available algorithm. Results Data on PPG use for patients was available for 568 patients. Of these, 122 (21.5%) had a history of AF, and therefore not included in this analysis. Data on PPG use was analysed of 446 patients, 240 were female (54%), average age was 79 ± 5.5 years, the oldest patient was 95 years old. In total, patients performed 2232 PPG recordings within 6 months, in which a sufficient signal was acquired in 1874 (84%) recordings. The signal appeared sufficient at the first attempt in 79.3% of the recordings. In case of a failed first attempt, 60.5% of the patients immediately performed at least one extra recording until sufficient signal quality was achieved, and overall 61.2% of the patients performed at least 2 PPG recordings. Only 69 (15.5%) patients who consented to participation, did not perform any recordings. Conclusion This analysis of preliminary GERAF PPG data shows that very old subjects (with a mean age of almost 80 years) are willing and able to use eHealth applications on a mobile device. Most PPG recordings were of sufficient signal quality, and more than half of the patients were willing to perform repeated recordings.

Implantable loop recorder detection for atrial fibrillation to prevent stroke in high-risk patients.

Implantable loop recorders (ILRs) are cardiac-monitoring devices that detect dangerous dysrhythmias, such as atrial fibrillation (AF). This article discusses whether ILRs can prevent strokes in high-risk patients by detecting AF.

Prevalence of Stroke Associated Pneumonia in Stroke Patients

Background and Aim: Stroke patients are most commonly susceptible to pulmonary infection that adversely affects the duration of hospitalization and clinical outcomes. Stroke affecting the respiratory system and causing neurological damage contributed to higher morbidity and mortality rate due to complications such as stroke-associated pneumonia (SAP). Early identification of high risk stroke patients and their management are imperative to reduce the prevalence of stroke-associated pneumonia. The present study aim was to determine the prevalence of stroke-associated pneumonia in stroke patients. Methodology: This cross-sectional descriptive case series was carried out on 226 stroke patients in the Department of Neurology, Fauji Foundation Hospital, Rawalpindi from January 2020 to December 2021. Stroke patients aged 30 to 70 years of either gender diagnosed with stroke were enrolled. Cough, fever and non-homogenous opacities observed on chest X-rays were used for the diagnosis of stroke. Patients with a history of pneumonia were excluded. Detailed history and complete examination was obtained for stroke diagnosis followed up for 1 month. SPSS version 25 was used for data analysis. Results: Of the total 226 stroke patients, there were 118 (52.2%) males and 108 (47.8%) females. The overall mean age was 48.62±8.45 years. Age-wise distribution of all patients were as follows; 72 (31.9%) 30-40 years, 106 (46.8%) 41-50 years, 32 (14.2%) 51-60 years, and 16 (7.1%) 61-70 years. The prevalence of stroke-associated pneumonia was 46 (20.4%). The incidence of diabetes mellitus, hypertension, ischemic heart disease, atrial fibrillation, congestive cardiac failure, and thrombophilia state was 48 (21.2%), 36 (15.9%), 28 (12.4%), 19 (8.4%), 14 (6.2%), and 8 (3.5%) respectively. Diabetes mellitus patients were more susceptible to stroke-associated pneumonia followed by hypertension and cardiac diseases. The incidence of stroke-associated pneumonia was 33 (14.6%) among patients having 6 to 12 hours stroke length against 13 (5.8%) with shorter duration. Conclusion: The present study found that the prevalence of stroke-associated pneumonia was 20.4% among stroke patients. Diabetes, hypertension, cardiac diseases, atrial fibrillation among stroke patients are the different risk factors that develop stroke-associated pneumonia. Early identification and better management could prevent the stroke-associated pneumonia in stroke patients. Keywords: Prevalence, Stroke-associated pneumonia, Stroke

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.

A machine learning approach for predicting suicidal ideation in post stroke patients

Currently, the identification of stroke patients with an increased suicide risk is mainly based on self‐report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p < 0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI. Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.

2019 Chinese expert consensus statement on left atrial appendage closure in patients with atrial fibrillation.

The left atrial appendage closure (LAAC), the efficacy and safety of which has been proved by a number of randomized controlled trials and registries, is recommended by several guidelines to prevent stroke in high‐risk patients with non‐valvular atrial fibrillation. However, current guidelines only discuss the indications and contraindications of LAAC, as an emerging technology, there still lacks comprehensive recommendations involved with LAAC, including devices, image assessment modality, identification and treatment of complications, perioperative medication, and postoperative management. Therefore, the Chinese Society of Cardiology (CSC) of Chinese Medical Association (CMA) and the Editorial Board of Chinese Journal of Cardiology jointly issued the expert consensus statement on LAAC in the prevention of stroke in patients with atrial fibrillation after comprehensive discussion by experts with different backgrounds. This consensus provided three levels of recommendations to guide and standardize the clinical application of LAAC based on existing evidence and clinical practice experience, including appropriate (more potential benefits or fewer harms), uncertain (somehow reasonable but need more evidence), and inappropriate (unlikely to benefit, or have more complications).

Abstract 57: Seeking Support For Symptoms Of Stroke Delays Emergency Medical Service Activation For Thai Women And Men

Background: National stroke campaigns have been implemented to improve stroke knowledge and awareness for the Thai public. Despite these efforts, only a minority of people recognize stroke symptoms and seek treatments within 4.5 hours after symptom onset. Studies reveal sex differences in stroke knowledge, which contributes to delayed treatment-seeking actions. Purpose: This study aimed to examine sex differences in stroke knowledge, symptoms, stroke recognition, and initial action between men and women after experiencing acute strokes. Methods: A cross-sectional study was conducted at three hospitals in Thailand between September and November 2020. Medical record reviews and patient interviews were completed within seven days of admission. Standardized questionnaires for prior stroke knowledge (stroke warning signs &amp; proper actions), clinical manifestation, stroke recognition, first action, and the activation of the emergency medical service (EMS) were used. Results: A total of 170 patients (54% men) with a median age of 63 years were included. Mean scores for prior stroke knowledge were higher in men compared to women (2.97 vs. 2.23, p = .02). Men were more likely than women to identify sudden numbness or weakness and sudden trouble walking or dizziness as stroke warning signs (62% vs. 42%, p = .01; 57% vs. 40%, p = .03, respectively). Women more frequently experienced severe headache (37% vs. 18%, p &lt; .01). No significant differences between men and women were found in stroke recognition (20% vs. 26%) and activation of the EMS system (22% vs. 19%). Seeking support from someone nearby or on the phone was the most frequent first action in men (47%) and women (55%), which often delayed EMS activation. Conclusion: Even though men scored higher in stroke knowledge than women, challenges to activating the EMS were found in both sexes, highlighting the need to explore targeted educational material and knowledge translation among high-risk stroke patients and their relatives, regardless of sex.

TYPE OF CYP450 2C19 GENE METABOLIZERS IN PATIENTS UNDERGOING PERCUTANEOUS CORONARY INTERVENTION IN THE REPUBLIC OF NORTH MACEDONIA -A PILOT STUDY

Clopidogrel has an antiplatelet effect, which is used to reduce the risk of myocardial infarction and stroke in high-risk patients in the context of primary prophylaxis. In the body it is subjected to metabolism by the liver enzyme system CYP450. In this project, laboratory conditions have been established to determine the polymorphisms of the CYP450 2C19 gene. The structure of the polymorphisms in the CYP450 2C19 gene and the type of metabolizer has been determined.This study included 42 heart patients with St. post PCI, mean age 65-9.7 years, treated with 75 mg Clopidogrel daily. The following polymorphisms were examined: c.681G&gt; A; c.636G&gt; A; c.1A&gt; G; c.1297C&gt; T; c.395G&gt; A; c.819 + 2T&gt; A; c.358T&gt; C; c.-806C&gt; T. Appropriate laboratory conditions have been established for the precise determination of polymorphisms in the CYP450 2C19 gene and the determination of the type of metabolizer. The results showed that in our patients the largest number of polymorphisms occur at the position c.681G&gt; A which leads to the creation of an intermediate metabolizer, while the polymorphism at the position c.-806C&gt; T leads to the creation of an ultrafast metabolite. The largest number of polymorphisms occurs in c.681G&gt; A CYP450 2C19 gene, which leads to the creation of an intermediate metabolizer. Because of this, and before starting the therapy in patients St. post PCI, it is necessary to first determine what clopidogrel metabolizers they are and whether they possess LF or GF alleles. Key words: clopidogrel, metabolite, polymorphisms, CYP450 2C19 gene.

Ischemic stroke in low risk patients with atrial fibrillation: a nationwide, registry-based cohort study

Abstract Background Despite the good evidence on the prevention of ischemic stroke in high risk patients with atrial fibrillation (AF), the knowledge in AF patients without any cardiovascular risk factors is still limited and the treatment has been highly questioned. Methods We linked data from Swedish health registers to identify all patients with AF but without any previous cardiovascular comorbidity. The risk of ischemic stroke was investigated by using Cox regression models and patients with AF compared with two controls without AF, matched for age and sex. Results Altogether, 229,613 patients with AF and 457,332 matched controls without AF were included in the study; 44.4% of the population were women. Both the 1-year and the 5-year risk to develop ischemic stroke was higher in patients with AF compared to their matched controls without AF, hazard ratio (HR) of 3.7 (95%, confidence interval (CI) 3.5–3.8) and 2.5 (95%, CI 2.5–2.6) respectively. According to the age groups, patients with age of 35 to 49 years had the highest risk to develop ischemic stroke within the first year after AF diagnosis, HR 8.3 (95%, CI 4.0–17.1). Women with AF had 4.4 times higher risk to develop ischemic stroke (HR 4.4, CI 4.2–4.7) compared to matched women without AF. Conclusions In this large, register-based, nationwide cohort study, we found that the 1-risk and 5-year risk to develop ischemic stroke was significantly higher in patients with AF but without any cardiovascular risk factor for stroke, compared to matched controls without AF. Women and middle-aged patients with AF carried the highest risk to develop ischemic stroke indicating the need of further research on risk stratification despite traditional factors. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): The Swedish state and The Swedish Research Council

Radiofrequency vs. Cryoballoon vs. Thoracoscopic Surgical Ablation for Atrial Fibrillation: A Single-Center Experience.

Background and Objectives: Cryoballoon ablation (CBA) and totally thoracoscopic surgical ablation (TTA) have emerged as alternatives to radiofrequency catheter ablation (RFCA) for atrial fibrillation. In this study, we describe our experience comparing patient characteristics and outcomes of RFCA, CBA, and TTA. Materials and Methods: We retrospectively analyzed data from patients who underwent RFCA, CBA, or TTA. Both atrial fibrillation (AF)- and atrial tachyarrhythmia (ATa)-free survival rates were compared using time to recurrence after a 3-month blanking period (defined by a duration of more than 30 s). All patients were regularly followed using 12-lead ECGs or Holter ECG monitoring. Results: Of 354 patients in this study, 125 underwent RFCA, 97 underwent CBA and 131 underwent TTA. The TTA group had more patients with persistent AF, a larger LA diameter, and a history of stroke. The CBA group showed the shortest procedure time (p < 0.001). The CBA group showed significantly lower AF-free survival at 12 months than the RFCA and TTA groups (RFCA 84%, CBA 74% and TTA 85%, p = 0.071; p = 0.859 for TTA vs. RFCA, p = 0.038 for RFCA vs. CBA and p = 0.046 for TTA vs. CBA). There were no significant differences in ATa-free survival among the three groups (p = 0.270). There were no procedure-related adverse events in the RFCA group, but some complications occurred in the CBA group and the TTA group (6% and 5%, respectively). Conclusions: RFCA and CBA are effective and safe as first-line treatments for paroxysmal and persistent AF. In some high-risk stroke patients, TTA may be a viable option. It is important to consider patient characteristics when selecting an ablation method for AF.

Optimizing cerebral perfusion and hemodynamics during cardiopulmonary bypass through cannula design combining in silico, in vitro and in vivo input

Cardiopulmonary bypass (CPB) is a standard technique for cardiac surgery, but comes with the risk of severe neurological complications (e.g. stroke) caused by embolisms and/or reduced cerebral perfusion. We report on an aortic cannula prototype design (optiCAN) with helical outflow and jet-splitting dispersion tip that could reduce the risk of embolic events and restores cerebral perfusion to 97.5% of physiological flow during CPB in vivo, whereas a commercial curved-tip cannula yields 74.6%. In further in vitro comparison, pressure loss and hemolysis parameters of optiCAN remain unaffected. Results are reproducibly confirmed in silico for an exemplary human aortic anatomy via computational fluid dynamics (CFD) simulations. Based on CFD simulations, we firstly show that optiCAN design improves aortic root washout, which reduces the risk of thromboembolism. Secondly, we identify regions of the aortic intima with increased risk of plaque release by correlating areas of enhanced plaque growth and high wall shear stresses (WSS). From this we propose another easy-to-manufacture cannula design (opti2CAN) that decreases areas burdened by high WSS, while preserving physiological cerebral flow and favorable hemodynamics. With this novel cannula design, we propose a cannulation option to reduce neurological complications and the prevalence of stroke in high-risk patients after CPB.