Predictors Of Stroke Recurrence Research Articles

Predictors of recurrent stroke and subsequent mortality among patients experiencing housing instability

BackgroundThe experience of homelessness has been linked with developing poor health outcomes. Little is known about the risk of recurrent stroke among these individuals. This study investigated the correlates of developing recurrent stroke and subsequent mortality among Veterans with housing instability. MethodsUsing a national sample of Veterans from the U.S. Department of Veterans Affairs who had an indicator of housing instability between 2014–2018 (n=659,987), we identified 15,566 Veterans who experienced incident stroke. We compared characteristics of Veterans who experienced incident stroke and did and did not experience recurrent stroke and conducted logistic regressions using a discrete-time survival framework to assess two outcomes: recurrent stroke and all-cause mortality. ResultsAmong our cohort, 91.3% did not experience recurrent stroke while 8.7% did during the observation period. The receipt of any level of primary care outpatient visits was associated with a reduction in the odds of recurrent stroke. Several medical diagnoses were also associated with increased odds of recurrent stroke, including hypertension (aOR 1.35, 95% CI 1.15-1.59), diabetes (aOR 1.21, 95% CI 1.07-1.36), and renal disease (aOR 1.17, 95% CI 1.02, 1.35). Veterans who used any level of VA Homeless Programs had reduced odds of all-cause mortality (high level: aOR 0.65, 95% CI 0.60-0.71; low level: aOR 0.66, 95% CI 0.60-0.73). ConclusionOur study found several predictors of developing recurrent stroke and subsequent death in a population of Veterans experiencing housing instability. Implications include the need to monitor closely high-risk patients who have experienced incident stroke and have other co-occurring needs.

An Integrated Multi-omics prediction model for stroke recurrence based on [formula omitted] transformer layer and dynamic weighting mechanism

Background and objectiveStroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. MethodsWe systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. ResultsWe compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. ConclusionMPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

HR-MRI-based nomogram network calculator to predict stroke recurrence in high-risk non-disabling ischemic cerebrovascular events patients.

To investigate the use of high-resolution magnetic resonance imaging (HR-MRI) to identify the characteristics of culprit plaques in intracranial arteries, and to evaluate the predictive value of the characteristics of culprit plaques combined with the modified Essen score for the recurrence risk of high-risk non-disabling ischemic cerebrovascular events (HR-NICE) patients. A retrospective analysis was conducted on 180 patients with HR-NICE at the First Affiliated Hospital of Xinxiang Medical University, including 128 patients with no recurrence (non-recurrence group) and 52 patients with recurrence (recurrence group). A total of 65 patients with HR-NICE were collected from the Sixth Affiliated Hospital of Shanghai Jiaotong University as a validation group, and their modified Essen scores, high-resolution magnetic resonance vessel wall images, and clinical data were collected. The culprit plaques were analyzed using VesselExplorer2 software. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for recurrence, and a nomogram was constructed using R software to evaluate the discrimination of the model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to evaluate the model performance. Calibration curves and Decision Curve Analysis (DCA) were used to evaluate the model efficacy. Intra-plaque hemorrhage (OR = 3.592, 95% CI = 1.474-9.104, p = 0.006), homocysteine (OR = 1.098, 95% CI = 1.025-1.179, p = 0.007), and normalized wall index (OR = 1.114, 95% CI = 1.027-1.222, p = 0.015) were significantly higher in the recurrent stroke group than in the non-recurrent stroke group, and were independent risk factors for recurrent stroke. The performance of the nomogram model (AUC = 0.830, 95% CI: 0.769-0.891; PR-AUC = 0.628) was better than that of the modified Essen scoring model (AUC = 0.660, 95% CI: 0.583-0.738) and the independent risk factor combination model (AUC = 0.827, 95% CI: 0.765-0.889). The nomogram model still had good model performance in the validation group (AUC = 0.785, 95% CI: 0.671-0.899), with a well-fitting calibration curve and a DCA curve indicating good net benefit efficacy for patients. High-resolution vessel wall imaging combined with a modified Essen score can effectively assess the recurrence risk of HR-NICE patients, and the nomogram model can provide a reference for identifying high-risk populations with good clinical application prospects.

A Clinical-Radiomics Nomogram for Predicting Early Ischemic Stroke Risk in Patients with Transient Ischemic Attack

To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients with transient ischemic attacks (TIA). A retrospective training dataset (n=76) and a prospective validation dataset (n=34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features selection were done in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. The performance of the models was assessed by computing the receiver-operating characteristic and decision curve analysis (DCA). High proportion of diabetes and hypertension in the recurrent than those in the stable patients in both training and validation dataset (P<0.05). Recurrent patients had significant higher ABCD2 score and plaque score than stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (OR=2.75, 95% CI: 1.47-6.40; and OR=1.20, 95% CI: 1.07-1.41). The radiomics model had AUC value of 0.737 (95% CI: 0.715-0.878) in training dataset and 0.899 (95% CI: 0.706-0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P<0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI: 0.895-0.978) in the training dataset and 0.935 (95% CI: 0.830-0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram. The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in TIA.

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence

ObjectThe aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence. MethodsThe data of this retrospective study originated from the database of JiangXi Province Medical Big Data Engineering & Technology Research Center. Moreover, the information of MMD patients admitted to the second affiliated hospital of Nanchang university from January 1st, 2007 to December 31st, 2019 was acquired. A total of 661 patients from January 1st, 2007 to February 28th, 2017 were covered in the training set, while the external validation set comprised 284 patients that fell into a scope from March 1st, 2017 to December 31st, 2019. First, the information regarding all the subjects was compared between the training set and the external validation set. The key influencing variables were screened out using the Lasso Regression Algorithm. Furthermore, the models for predicting stroke recurrence in 1, 2, and 3 years after the initial stroke were built based on five different machine learning algorithms, and all models were externally validated and then compared. Lastly, the CatBoost model with the optimal performance was explained using the SHapley Additive exPlanations (SHAP) interpretation model. ResultIn general, 945 patients suffering from MMD were recruited, and the recurrence rate of acute stroke in 1, 2, and 3 years after the initial stroke reached 11.43%(108/945), 18.94%(179/945), and 23.17%(219/945), respectively. The CatBoost models exhibited the optimal prediction performance among all models; the area under the curve (AUC) of these models for predicting stroke recurrence in 1, 2, and 3 years was determined as 0.794 (0.787, 0.801), 0.813 (0.807, 0.818), and 0.789 (0.783, 0.795), respectively. As indicated by the results of the SHAP interpretation model, the high Suzuki stage, young adults (aged 18–44), no surgical treatment, and the presence of an aneurysm were likely to show significant correlations with the recurrence of stroke in adult stroke patients subjected to MMD. ConclusionIn adult stroke patients suffering from MMD, the CatBoost model was confirmed to be effective in stroke recurrence prediction, yielding accurate and reliable prediction outcomes. High Suzuki stage, young adults (aged 18–44 years), no surgical treatment, and the presence of an aneurysm are likely to be significantly correlated with the recurrence of stroke in adult stroke patients subjected to MMD.

Stroke recurrence and osteoporotic conditions in postmenopausal patients with atherosclerotic ischemic stroke

Recurrence after stroke is common, and associated with a high mortality rate. Degradation of the elastic tissue in the arterial wall has been shown to aggravate atherosclerosis in blood vessels. Considering that type 1 collagen is present in both bone and vascular smooth muscle cells, we explored whether osteoporotic conditions affect the likelihood of stroke recurrence in postmenopausal women following atherosclerotic ischemic stroke. To determine actual bone mineral density (BMD), the Hounsfield unit values in the frontal skull were evaluated using brain computed tomography (CT) scans taken at admission. A multivariate Cox regression analysis was also performed to examine if osteoporosis could independently predict stroke recurrence in postmenopausal patients with large artery atherosclerosis (LAA) or small vessel occlusion (SVO) stroke. This study included 2130 consecutive patients (both males and females aged 50 and older) with acute LAA or SVO strokes. After adjusting for all covariates, hypothetical osteoporosis was identified as an independent predictor of stroke recurrence in female patients ≥50 years with acute LAA or SVO stroke (hazard ratio, 1.84; 95 % confidence interval, 1.05 to 3.24; p = 0.034). Our findings showed that osteoporosis could potentially affect the recurrence of ischemic stroke in postmenopausal patients with LAA or SVO stroke.

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.

Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence. We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models. Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74-0.77), 0.60 (95% CI, 0.58-0.61), and 0.71 (95% CI, 0.69-0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72-0.75), 0.59 (95% CI, 0.57-0.61), and 0.67 (95% CI, 0.66-0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (P<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors. PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.

Potential Protein Signatures for Recurrence Prediction of Ischemic Stroke.

Acute ischemic stroke is a major cause of mortality and disability worldwide, with approximately 7.4% to 7.7% recurrence within the first 3 months. This study aimed to identify potential biomarkers for predicting stroke recurrence. We conducted a nested case-control study using a hospital-based cohort from the Third China National Stroke Registry selecting 214 age- and sex-matched patients with ischemic stroke with hypertension and no history of diabetes or heart disease. Using data-independent acquisition for discovery and multiple reaction monitoring for quantitative validation, we identified 26 differentially expressed proteins in large-artery atherosclerosis (Causative Classification of Ischemic Stroke [CCS]1), 16 in small-artery occlusion (CCS3), and 25 in undetermined causes (CCS5) among patients with recurrent stroke. In the CCS1 and CCS3 subgroups, differentially expressed proteins were associated with platelet aggregation, neuronal death/cerebroprotection, and immune response, whereas differentially expressed proteins in the CCS5 subgroup were linked to altered metabolic functions. Validated recurrence predictors included proteins associated with neutrophil activity and vascular inflammation (TAGLN2 [transgelin 2], ITGAM [integrin subunit α M]/TAGLN2 ratio, ITGAM/MYL9 [myosin light chain 9] ratio, TAGLN2/RSU1 [Ras suppressor protein 1] ratio) in the CCS3 subgroup and proteins associated with endothelial plasticity and blood-brain barrier integrity (ITGAM/MYL9 ratio and COL1A2 [collagen type I α 2 chain]/MYL9 ratio) in the CCS3 and CCS5 subgroups, respectively. These findings provide a foundation for developing a blood-based biomarker panel, using causative classifications, which may be used in routine clinical practice to predict stroke recurrence.

Abstract 64: Biomarkers of Atrial Cardiopathy and Outcomes After Cryptogenic Stroke: The ARCADIA Trial

Background: Atrial cardiopathy is associated with incident ischemic stroke in the absence of clinical atrial fibrillation in prospective cohorts. Whether biomarkers of atrial cardiopathy predict recurrent stroke is less clear. Methods: In the ARCADIA trial, a multicenter randomized trial of apixaban versus aspirin in patients with cryptogenic stroke and evidence of atrial cardiopathy, we explored whether the biomarkers P-wave terminal force in ECG lead V 1 (PTFV1), N-terminal pro-Brain natriuretic peptide (NT-proBNP), or left atrial diameter index (LADI) predicted primary and secondary trial outcomes: (1) recurrent stroke of any type; (2) recurrent ischemic stroke or systemic embolism; and (3) recurrent stroke of any type or death. We analyzed the full cohort using Cox proportional hazard models , sequentially adjusting for treatment effect, demographic factors, and clinical risk factors. NT-proBNP concentrations were log-transformed. Results: With 1,015 of the target 1,100 participants enrolled (mean age 68 years, 54.3% female, 21.1% Black, and 8.1% Hispanic) and mean follow-up of 1.8 years, the trial was stopped for futility. Recurrent stroke occurred in 80 patients (annualized rate, 4.4%) and death in 54 patients. NT-proBNP significantly predicted recurrent stroke; recurrent ischemic stroke or systemic embolism; and recurrent stroke or death after adjusting for other risk factors, while PTFV1 and LADI did not (Table). NT-proBNP predicted recurrent stroke and death most strongly and with little change after adjusting for other risk factors. Treatment effect was not significant in any of the models. Conclusions: In this population of patients with cryptogenic stroke and evidence of atrial cardiopathy, NT-proBNP is a predictor of recurrent stroke and, with greater magnitude, of a composite of stroke or death. The role of NT-proBNP measurement in management after stroke deserves further study.

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis.

Symptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine learning model based on high-resolution vessel wall imaging (HR-VWI) to predict the risk of stroke recurrence in SICAS. This study retrospectively collected data from 180 SICAS stroke patients treated at the hospital between 2020.01 and 2022.01. Relevant imaging and clinical data were collected, and follow-up was conducted. The dataset was divided into a training set and a validation set in a ratio of 7:3. We employed the least absolute shrinkage and selection operator (LASSO) regression to perform a selection on the baseline data, laboratory tests, and neuroimaging data generated by HR-VWI scans collected from the training set. Finally, five machine learning techniques, including logistic regression model (LR), support vector machine (SVM), Gaussian naive Bayes (GNB), Complement naive Bayes (CNB), and k-nearest neighbors algorithm (kNN), were employed to develop a predictive model for stroke recurrence. Shapley Additive Explanation (SHAP) was used to provide visualization and interpretation for each patient. The model's effectiveness was evaluated using average accuracy, sensitivity, specificity, precision, f1 score, PR curve, calibration curve, and decision curve analysis. LASSO analysis revealed that "history of hypertension," "homocysteine level," "NWI value," "stenosis rate," "intracranial hemorrhage," "positive remodeling," and "enhancement grade" were independent risk factors for stroke recurrence in SICAS patients. In 10-fold cross-validation, the area under the curve (AUC) ranged from 0.813 to 0.912 in ROC curve analysis. The area under the precision-recall curve (AUPRC) ranged from 0.655 to 0.833, with the Gaussian Naive Bayes (GNB) model exhibiting the best ability to predict stroke recurrence in SICAS. SHAP analysis provided interpretability for the machine learning model and revealed essential factors related to the risk of stroke recurrence in SICAS. A precise machine learning-based prediction model for stroke recurrence in SICAS has been established to assist clinical practitioners in making clinical decisions and implementing personalized treatment measures.

Stroke and Risk Factors in Antiphospholipid Syndrome.

Stroke is considered one of the most common and life-threatening manifestations of antiphospholipid syndrome (APS), which leads to high mortality and permanent disability. This study investigated the prevalence and the potential risk factors of stroke in APS. We enrolled 361 APS patients retrospectively from 2009 to 2022 at Peking University People's Hospital. Stroke was found in 25.8% (93/361) of the participants. The multivariate logistic regression showed that hypertension, diabetes, livedo reticularis, and other central nervous system involvements were significant related factors for stroke. The use of hydroxychloroquine appeared to relate to a lower incidence of stroke. During a median follow-up of 3.0 years, 11.8% (11/93) of the individuals with a previous stroke developed stroke recurrence, and thrombocytopenia seemed to be a predictor of stroke recurrence.

Left atrial strain predicts stroke recurrence and death in patients with cryptogenic stroke

Abstract Background Left atrial strain (LAS) has been widely studied as predictor of atrial fibrillation (AF) after cryptogenic stroke (CS). However, the evidence about its prognostic role in terms of stroke recurrence and death in this setting remains scarce. Methods From April 2019 to April 2022, 92 consecutive patients with ischemic stroke or transient ischemic attack with ABCD2 scale ≥4 of unknown etiology were prospectively recruited. Echocardiography, including LAS was performed during admission. The primary outcome was the composite of stroke recurrence or death. Results Mean age was 77.5±7.7 and 58% of patient were female. After a median follow up of 28 months, the primary outcome measure occurred in 15 patients (16%). The primary outcome was more frequent in patients with diabetes (53% vs 21%; p=0.02), chronic kidney disease (33% vs 10%; p=0.034) and history of heart failure (13% vs 0%; p=0.025). LAS reservoir (LASr) and LAS conduct (LAScd) were lower in patients developing the primary outcome (21±7 vs 28.8±11%; p=0.017 and 7.7±3.9 vs 13.7±7%; p=0.007, respectively). On multivariate analysis, diabetes (HR 7.4, 95%CI 1.2-47; p=0.034)), LASr (HR 0.2, 95%CI 0.05-0.8; p=0.023), LAScd (HR 0.17, 95%CI 0.04-0.7; p=0.013) and LAS contraction (HR 0.2, 95%CI 0-05-0.8; p=0.025) were independent predictors of stroke recurrence or all-cause death after CS (Table). On long-rank test (using the discriminatory cut-off values of LASr &amp;lt;23% and LAScd &amp;lt;10%), LASr (p=0.009) (Figure 1) and LAScd (p=0.016) were associated with higher risk of the primary outcome. Conclusion Lower values of LAS reservoir, conduct and contraction are independently associated with higher risk of stroke recurrence or death after CS

Left Atrial Strain Predicts Stroke Recurrence and Death in Patients With Cryptogenic Stroke

Left atrial strain (LAS) has been widely studied as a predictor of atrial fibrillation (AF) after cryptogenic stroke (CS). However, the evidence about its prognostic role in terms of stroke recurrence and death in this setting remains scarce. A total of 92 consecutive patients with ischemic stroke or transient ischemic attack with ABCD2 scale ≥4 of unknown etiology were prospectively recruited. Echocardiography, including LAS was performed during admission. The primary outcome measure was the composite of stroke recurrence or death. The mean age was 77.5 ± 7.7, and 58% of patients were female. After a median follow up of 28 months, the primary outcome measure occurred in 15 patients (16%). The primary outcome was more frequent in patients with diabetes (53% vs 21%, p=0.02), chronic kidney disease (33% vs 10%, p=0.034), and a history of heart failure (13% vs 0%, p=0.025). LAS reservoir (LASr) and LAS conduit (LAScd) were lower in patients developing the primary outcome (21% ± 7% vs 28.8% ± 11%, p=0.017 and 7.7% ± 3.9% vs 13.7% ± 7%, p=0.007, respectively). On multivariate analysis, LASr (hazard ratio 0.9, 95% confidence interval 0.85 to 0.99, p=0.048) and diabetes (hazard ratio 3.3, 95% confidence interval 1.03 to 10.4, p=0.045) were associated with stroke recurrence or all-cause death after CS. On the log-rank test (using the discriminatory cut-off value of LASr <23%), LASr (p=0.009) was associated with higher risk of the primary outcome. In conclusion, lower values of the LAS reservoir were associated with a higher risk of stroke recurrence or death after CS. LAS may identify patients at higher risk of thromboembolism and stress conditions.

Association of plaque characteristics with long-term stroke recurrence in patients with intracranial atherosclerotic disease: a 3D high-resolution MRI-based cohort study

ObjectivesTo evaluate the predictive ability of plaque characteristics for long-term stroke recurrence among patients with symptomatic intracranial atherosclerotic disease (ICAD).MethodsThis cohort study included 132 patients with acute ischemic stroke (AIS) attributed to ICAD who were recruited between July 2017 and December 2020 and followed until stroke recurrence or December 2021. Plaque surface irregularity, degree of stenosis, plaque burden, remodeling ratio, enhancement ratio, and intraplaque hemorrhage were assessed with 3-dimensional high-resolution magnetic resonance vessel wall imaging (3D HR-MRI). Data were analyzed using Cox models, receiver operating characteristic (ROC) curves, and Kaplan–Meier survival analysis.ResultsOf the 132 patients, during a median follow-up of 2.8 years, stroke recurrence occurred in 35 patients. The multivariable-adjusted hazard ratio (95% confidence interval) of stroke recurrence was 3.15 (1.34–7.42) per 10% increase in plaque burden and 2.17 (1.27–3.70) for enhancement ratio. The area under the curve (AUC) to predict stroke recurrence was 0.725 (95% CI 0.629–0.822) for plaque burden, 0.692 (95% CI 0.593–0.792) for enhancement ratio, and only 0.595 (95% CI 0.492–0.699) for the Essen stroke risk score. The Kaplan–Meier survival analysis further demonstrated significant differences in survival of free recurrent stroke between patients with plaque burden or enhancement ratio below and above the optimum cut-offs (both p < 0.001).ConclusionHigher plaque burden and enhancement ratio are independent risk factors for long-term stroke recurrence among patients with symptomatic ICAD, and valuable imaging markers for predicting and stratifying risk of stroke recurrence.Clinical relevance statementIn patients with symptomatic ICAD, the results of this high-resolution magnetic resonance vessel wall imaging study have potential implications for optimal management of intracranial plaques and secondary prevention of stroke recurrence based on plaque burden and enhancement ratio.Key Points• Identification of intracranial plaque characteristics responsible for stroke recurrence is essential to preventing stroke recurrence in patients with symptomatic intracranial atherosclerotic disease.• Higher plaque burden and enhancement ratio are independent risk factors for stroke recurrence.• Plaque burden and enhancement ratio are valuable imaging markers in the prediction and stratification of the risk of stroke recurrence.

The prognostic nutrition index is a predictor for long-term outcomes after revascularization in adult moyamoya disease.

The prognostic nutrition index (PNI) has been associated with the prognosis of various medical disorders. This study aimed to explore the correlation between PNI and the long-term outcomes of adult patients afflicted with moyamoya disease (MMD). This prospective study initially employed 138 adult patients diagnosed with MMD. After excluding 15 patients who did not meet the criteria, a total of 123 patients were included. Participants were divided into three groups based on the tertile of change in the PNI score. Statistical analysis compared clinical information and lab tests among the groups. The study was conducted between July 1 and December 31, 2019. After adjusting for multiple variables, patients in the upper two tertiles (tertiles 2-3) exhibited a significantly lower risk of adverse long-term outcomes compared to those in the lowest tertile (tertile 1) (OR, 0.089; 95% CI, 0.009-0.895; P = 0.040). Furthermore, adding PNI tertile to traditional risk factors substantially improved predicting adverse long-term outcomes (net reclassification improvement: 98.03%, P = 0.000; integrated discrimination improvement: 4.65%, P = 0.030). However, there was no statistically significant difference between the first PNI tertile (tertile 1) and the upper two tertiles (tertiles 2-3) in the Kaplan-Meier curve of stroke incidence (log-rank test, P = 0.244). A higher PNI level was significantly associated with a reduced risk of unfavorable long-term outcomes. Nevertheless, the PNI score did not predict stroke recurrence during extended follow-up. This study provides insights into a potential predictor of adverse long-term outcomes after revascularization in MMD patients. ChiCTR2000031412.

Validation and comparison of the stroke prognosis instrument (SPI-II) and the essen stroke risk score (ESRS) in predicting stroke recurrence in Asian population

BackgroundCurrently, there are limited data on the accuracy of available risk scores to predict stroke recurrence in the Asian population.MethodA single-center, retrospective cohort study was conducted among patients with acute ischemic stroke during January 2014 - December 2018. Longitudinal data with three years of follow-up among these patients were collected and validated through both electronic and manual chart review. The area under the receiver-operating curve (AUROC) method or C-statistic and calibration plot were used to evaluate and compare the Stroke Prognosis Instrument II (SPI-II) and the Essen Stroke Risk Score (ESRS) in predicting the long-term risk of recurrent ischemic stroke. The predictive performances of the two scores were compared using DeLong’s method.ResultsThe study cohort consisted of 543 patients, including 181 and 362 patients with and without recurrent events. There were no significant differences in mean age and gender between the two groups. Recurrence cases tended to have significant more risk factors compared to those without events. Among cases with recurrent events, 134 (74.03%) and 65.74% (119) cases were classified as high-risk based on SPI-II and ESRS, respectively. The AUROC curve of the SPI-II and ESRS score was 0.646 (95% CI, 0.594–0.697) and 0.614 (95%CI, 0.563–0.665), respectively (p = 0.394). Based on the calibration plot, the SPI-II and ESRS scores showed similar moderate predictive performance on recurrence stroke with a C statistic (95% CI) of 0.655 (95% CI: 0.603–0.707) and 0.631 (95% CI 0.579–0.684), respectively.ConclusionBoth ESRS and SPI-II scores had moderate predictive performance in Thai population.

Body mass index and waist circumference as predictors of recurrent vascular events after a recent ischemic stroke

ObjectivesAlthough elevated body mass index (BMI) is a risk factor for stroke, it appears to protect against recurrent vascular events. We tried to evaluate BMI and waist circumference (WC) as predictors of recurrent stroke and vascular events in a cohort of stroke survivors who were followed for 12 months. Materials and methodsWe analyzed the stroke registry database of 6 hospitals and recruited patients with a first-ever stroke who were admitted from January 2011 to November 2019 and had their BMI and WC measured. Cox proportional hazards models were used to compare risks of recurrent stroke and major vascular events (a composite of stroke, myocardial infarction, or vascular death) between different BMI and WC quintiles. Reference categories were patients in the lowest quintiles. ResultsA total of 14 781 patients were analyzed. Patients in the second quintile of BMI had the lowest risk of recurrent stroke (adjusted hazard ratio (HR) 0.72; 95% confidence interval (CI) 0.58–0.91); patients in the highest quintile had the lowest risk or a major vascular event (adjusted HR 0.71; 95% CI 0.58–0.86). Patients in the fourth quintile of WC had the lowest risk of recurrent stroke (adjusted HR 0.73; 95% CI 0.59–0.91) and a major vascular event (adjusted HR 0.72; 95 % CI 0.60–0.86). ConclusionsOur results show favorable effects of excess body weight and intra-abdominal fat on avoidance of vascular events after stroke and a favorable effect of intra-abdominal fat on avoidance of recurrent stroke.

Serum alkaline phosphatase was independently associated with depression in patients with cerebrovascular disease.

Blood markers have important value in the diagnosis of depressive disorders. Serum alkaline phosphatase (ALP) not only predicts stroke recurrence and poor functional prognosis in cerebrovascular disease (CVD) patients but also increases significantly in middle-aged women with depression. Thus, it has not been reported whether serum ALP is associated with the development of depression and/or vascular depression (VDe) in CVD patients. This was a cross-sectional study of 353 CVD patients (stroke patients, n = 291; cerebral small vessel disease (CSVD) patients, n = 62). Baseline demographic information, fasting blood markers (such as blood counts, liver function, kidney function and lipids), and brain CT/MRI scans were collected. CVD patients were divided into non-depression, suspected vascular depression (SVD), and positive vascular depression (PVD) groups according to their Hamilton Rating Scale for Depression (HAMD) scores. Univariate analysis of baseline data, blood markers, and the prevalence of lesions (> 1.5 cm) was performed. Subsequently, the diagnostic performance of the univariate and combined variables for SVD and PVD was analyzed using binary logistic regression. The diagnostic value of the multivariate model for VDe was analyzed by ordinal logistic regression. (1) Serum ALP (p = 0.003) and hypersensitive C-reactive protein (hs-CRP, p = 0.001) concentrations increased as HAMD scores increased, and the prevalence of brain atrophy (p = 0.016) and lesions in the basal ganglia (p = 0.001) and parietal (p = 0.001), temporal (p = 0.002), and frontal lobes (p = 0.003) also increased, whereas the concentrations of hemoglobin (Hb, p = 0.003), cholinesterase (ChE, p = 0.001), and high-density lipoprotein cholesterol (HDL-C, p = 0.005) declined. Among these variables, hs-CRP (r = 0.218, p < 0.001) had a weak positively association with HAMD scores, and ChE (r = -0.226, p < 0.001) had a weak negative association. (2) The combination of Hb, hs-CRP, ChE, ALP, and HDL-C improved diagnostic performance for VDe [AUC = 0.775, 95% CI (0.706, 0.844), p < 0.001]. (3) Hb (OR = 0.986, p = 0.049), ChE (OR = 0.999, p = 0.020), ALP (OR = 1.017, p = 0.003), and basal ganglia lesions (OR = 2.197, p < 0.001) were important factors impacting VDe development. After adjusting for Hb, hs-CRP, ChE, HDL-C, lesions in the above mentioned four locations, sex, age and the prevalence of CSVD and brain atrophy, ALP [OR = 1.016, 95% CI (1.005, 1.027), p = 0.004] was independently associated with VDe. Hb, hs-CRP, ChE, ALP, and HDL-C concentrations are potential blood markers of depression in CVD patients and, when combined, may improve diagnostic performance for VDe. Serum ALP was independently associated with VDe in patients with CVD.

Co-existing intracranial and extracranial carotid atherosclerosis predicts large-artery atherosclerosis stroke recurrence: a single-center prospective study utilizing combined head-and-neck vessel wall imaging.

Intracranial and extracranial plaque features on high-resolution vessel wall imaging (HR-VWI) are associated with large-artery atherosclerosis (LAA) stroke recurrence. However, most studies have focused on a single vascular bed, and the prognostic value of combined intracranial and extracranial plaque features has yet to be studied. This study aimed to investigate the roles of plaque features, plaque number, and co-existing atherosclerosis in predicting stroke recurrence, utilizing combined head-and-neck HR-VWI. From September 2016 to March 2020, participants with acute LAA ischemic strokes were prospectively enrolled and underwent combined head-and-neck HR-VWI. The participants were followed for stroke recurrence for at least 12months or until a subsequent event occurred. The imaging features at baseline, including conventional and histogram plaque features, plaque number, and co-existing atherosclerosis, were evaluated. Univariable Cox regression analysis and the least absolute shrinkage and selection operator (lasso) method were used for variable screening. Multivariable Cox regression analyses were used to determine the independent risk factors of stroke recurrence. A total of 97 participants (59 ± 12years, 63 men) were followed for a median of 30.9months, and 21 participants experienced recurrent strokes. Multivariable Cox analysis identified co-existing intracranial high signal on T1-weighted fat-suppressed images (HST1) and extracranial carotid atherosclerosis (HR, 6.12; 95% CI, 2.52-14.82; p = 0.001) as an independent imaging predictor of stroke recurrence. Co-existing intracranial HST1 and extracranial carotid atherosclerosis independently predicted LAA stroke recurrence. Combined head-and-neck HR-VWI is a promising technique for atherosclerosis imaging. This prospective study using combined head-and-neck HR-VWI highlighted the necessity of both intracranial culprit plaque evaluation and multi-vascular bed assessment, adding value to the prediction of stroke recurrence. • This study highlighted the necessity of both intracranial culprit plaque evaluation and multi-vascular bed assessment, adding value to the prediction of stroke recurrence. • This prospective study using combined head-and-neck HR-VWI found co-existing intracranial HST1 and extracranial carotid atherosclerosis to be independent predictors of stroke recurrence.

A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study.

Recurrent stroke accounts for 25-30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS). A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator. Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly. This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.