A stroke prediction model based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project was developed. We compared its predictive ability with the revised Framingham Stroke Risk Score (R-FSRS) for 5-year stroke incidence in a community cohort of Chinese adults, namely the Beijing Longitudinal Study of Aging (BLSA). Calibration, discrimination, and recalibration were used to compare the predictive ability between the two prediction models. Category-less net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were also assessed. During a mean follow-up duration of 5.1 years, 106 incidents of fatal or non-fatal strokes occurred among 1,203 participants aged 55–84 years. The R-FSRS applied to our cohort underestimated the 5-year risk for stroke in men and women. China-PAR performed better than the R-FSRS in terms of calibration (men, R-FSRS: χ2-value 144.2 [P < 0.001], China-PAR: 10.4 [P = 0.238]; women, R-FSRS: 280.1 [P < 0.001], China-PAR: 12.5 [P = 0.129]). In terms of discrimination, R-FSRS and China-PAR models performed modestly in our cohort (C-statistic 0.603 [95% CI: 0.560–0.644] for men using China-PAR and 0.568 [95% CI: 0.524–0.610] using the R-FSRS; the corresponding numbers for women were 0.602 [95% CI: 0.564–0.639] and 0.575 [95% CI: 0.537–0.613). The recalibrated China-PAR model significantly improved the discrimination in C statistics and produced higher category-less NRI and IDI for stroke incidence than the R-FSRS. Although China-PAR fairly estimated stroke risk in our cohort, it did not sufficiently identify adults at high risk of stroke. Caution would be exercised by practitioners in applying the original China-PAR to Chinese older adults. Further studies are needed to develop an adequate prediction model based on the recalibrated China-PAR or to find new risk markers which could upgrade this model.

Dataset

This dataset was created by Prathiksha R

Released under MIT

Contents

IntroductionThe Early Prediction of Functional Outcome after Stroke (EPOS) model for independent gait is a tool to predict between days 2 and 9 poststroke whether patients will regain independent gait 6 months after stroke. External validation of the model is important to determine its clinical applicability and generalizability by testing its performance in an independent cohort. Therefore, this study aimed to perform a temporal and geographical external validation of the EPOS prediction model for independent gait after stroke but with the endpoint being 3 months instead of the original 6 months poststroke.MethodsTwo prospective longitudinal cohort studies consisting of patients with first-ever stroke admitted to a Swiss hospital stroke unit. Sitting balance and strength of the paretic leg were tested at days 1 and 8 post-stroke in Cohort I and at days 3 and 9 in Cohort II. Independent gait was assessed 3 months after symptom onset. The performance of the model in terms of discrimination (area under the receiver operator characteristic (ROC) curve; AUC), classification, and calibration was assessed.ResultsIn Cohort I [N = 39, median age: 74 years, 33% women, median National Institutes of Health Stroke Scale (NIHSS) 9], the AUC (95% confidence interval (CI)] was 0.675 (0.510, 0.841) on day 1 and 0.921 (0.811, 1.000) on day 8. For Cohort II (N = 78, median age: 69 years, 37% women, median NIHSS 8), this was 0.801 (0.684, 0.918) on day 3 and 0.846 (0.741, 0.951) on day 9.Discussion and ConclusionExternal validation of the EPOS prediction model for independent gait 3 months after stroke resulted in an acceptable performance from day 3 onward in mild-to-moderately affected patients with first-ever stroke without severe prestroke disability. The impact of applying this model in clinical practice should be investigated within this subgroup of patients with stroke. To improve the generalizability of patients with recurrent stroke and those with more severe, neurological comorbidities, the performance of the EPOS model within these patients should be determined across different geographical areas.

Background and Purpose: Treatment for mild stroke remains an open question. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who would be at high risk of post-stroke disability (PSD) if they only received medical therapy and, more importantly, to aid neurologists in making individual clinical decisions in emergency contexts.Methods: Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020. The exclusion criteria were patients who received thrombolytic therapy, age <18 years, lack of 3-month modified Rankin Scale (mRS), disabled before the index stroke, with an admission National Institute of Health stroke scale (NIHSS) > 5. The primary outcome was PSD, corresponding to 3-month mRS ≥ 2. We developed five ML models and assessed the area under curve (AUC) of receiver operating characteristic, calibration curve, and decision curve analysis. The optimal ML model was selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. Finally, rapid-DAMS (R-DAMS) was constructed for a more urgent situation based on DAMS.Results: A total of 1,905 mild stroke patients were enrolled in this study, and patients with PSD accounted for 23.4% (447). There was no difference in AUCs between the five models (ranged from 0.691 to 0.823). Although there was similar discriminative performance between ML models, the support vector machine model exhibited higher net benefit and better calibration (Brier score, 0.159, calibration slope, 0.935, calibration intercept, 0.035). Therefore, this model was selected for DAMS. In addition, SHAP approach showed that the most crucial feature was NIHSS on admission. Finally, R-DAMS was constructed and there was similar discriminative performance between R-DAMS and DAMS, but the former performed worse on calibration.Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, were designed to aid clinical decision-making for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.

Background and purposeHemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient’s risk of HT within 30 days of initial ischemic stroke.MethodsWe utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia.ResultsIn the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60–0.78.ConclusionsA HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.

Circular RNAs (CircRNAs) have shown promising potential in the diagnosis and the prediction of outcomes of stroke. This study aimed to explore the potential value of circRNAs for identifying acute neurological deterioration and estimating long-term survival for acute ischemic stroke (AIS). One hundred healthy controls and 200 patients with AIS within 72 h were recruited, 140 of whom were admitted within 24 h after onset. CircRNA levels in peripheral blood were measured by quantitative polymerase chain reaction (qPCR). Compared to the controls, the levels of three circRNAs were significantly increased in three subgroups of patients, including large artery atherosclerosis (LAA) stroke, small artery occlusion (SAO) stroke, and cardioembolism (CE) stroke (all P < 0.001). Among, LAA stroke patients had higher levels of circular RNA FUNDC1 (circFUNDC1) compared to SAO stroke patients (P = 0.015). CircFUNDC1 levels were positively correlated with National Institutes of Health Stroke Scale (NIHSS) scores on the 7th day only in LAA patients (P = 0.048, r = 0.226). It should be noted that the levels of circFUNDC1 in patients with early neurological deterioration (END), admitted within 24 h after onset, were significantly higher than those without END (P = 0.013). In addition, circFUNDC1 levels positively correlated with baseline NIHSS scores (P = 0.016, r = 0.203) or the 7th day NIHSS scores (P = 0.001, r = 0.289) in patients within 24 h after onset. Importantly, after 18 months of follow-up, a significant difference was observed on survival Kaplan-Meier curves (P = 0.042) between AIS patients with low (below cut-off) or high circFUNDC1 levels (above cut-off). Circulating circFUNDC1 could be a potential biomarker for predicting acute-phase outcome and long-term survival in AIS.

This multi-center dataset consists of 149 acute ischemic stroke cases, representing the training set of the ISLES'24 challenge.

All data are provided in NIfTI format (.nii.gz) and organized according to the BIDS standard. For each case, the following data are included:

• Admission imaging: non-contrast CT (NCCT), CT angiography (CTA), 4D CT perfusion (CTP) time series, and perfusion maps (Tmax, CBF, CBV, MTT).

• Follow-up imaging: post-treatment MRI (DWI and ADC).

• Clinical data: demographics, patient history, admission NIHSS, 3‑month functional outcome (mRS), etc.

• Annotations: binary infarct masks derived from follow-up MRI (lesion-msk.nii.gz), large vessel occlusion binary masks derived from CTA (lvo-msk.nii.gz), and the multi-labeled Circle of Willis anatomy generated with an automatic algorithm over CTA (cow-msk.nii.gz)

This dataset combines multimodal imaging, longitudinal follow-up, and structured clinical variables to support benchmarking of stroke infarct prediction methods.

Data structure

'Raw_data' refers to the 'raw' acquired scans, which are released in their original space, just defaced. 'Derivatives' include all modalities linearly co-registered to the NCCT space. Ses-0001 points to the acute imaging data, while Ses-0002 refers to the follow-up imaging data (sub-acute stroke phase). A single case-sample is structured as follows.

raw_data/
├── sub-strokecase0001/
│ └── ses-0001/
│ ├── perfusion-maps/
│ │ ├── sub-strokecase0001_ses-0001_tmax.nii.gz
│ │ ├── sub-strokecase0001_ses-0001_mtt.nii.gz
│ │ ├── sub-strokecase0001_ses-0001_cbf.nii.gz
│ │ └── sub-strokecase0001_ses-0001_cbv.nii.gz
│ ├── sub-strokecase0001_ses-0001_ncct.nii.gz
│ ├── sub-strokecase0001_ses-0001_cta.nii.gz
│ └── sub-strokecase0001_ses-0001_ctp.nii.gz

derivatives/
├── sub-strokecase0001/
│ ├── ses-0001/
│ │ ├── perfusion-maps/
│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_tmax.nii.gz
│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_mtt.nii.gz
│ │ │ ├── sub-strokecase0001_ses-0001_space-ncct_cbf.nii.gz
│ │ │ └── sub-strokecase0001_ses-0001_space-ncct_cbv.nii.gz
│ │ ├── sub-strokecase0001_ses-0001_space-ncct_cta.nii.gz
│ │ ├── sub-strokecase0001_ses-0001_space-ncct_ctp.nii.gz
│ │ ├── sub-stroke0086_ses-01_space-ncct_cow-msk.nii.gz
│ │ └── sub-stroke0086_ses-01_space-ncct_lvo-msk.nii.gz
│ └── ses-0002/
│ ├── sub-strokecase0001_ses-02_space-ncct_dwi.nii.gz
│ ├── sub-strokecase0001_ses-02_space-ncct_adc.nii.gz
│ └── sub-strokecase0001_ses-02_space-ncct_lesion-msk.nii.gz

phenotype/
├── ses-0001/
│ └── sub-strokecase0001_ses-0001_demographic_baseline.csv
└── ses-0002/
└── sub-strokecase0001_ses-0001_outcome.csv

Please cite the following two works when using this dataset:

Riedel, O. E., de la Rosa, E., Hernandez Petzsche, M., Baazaoui, H., Yang, K., Musio, F. A., … & Kirschke, J. S. (2024). ISLES’24 – A Real-World Longitudinal Multimodal Stroke Dataset. arXiv e-prints, arXiv:2408.09259.

de la Rosa, E., Su, R., Reyes, M., Wiest, R., Riedel, E. O., Kofler, F., … & Menze, B. (2024). ISLES’24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand? arXiv preprint, arXiv:2408.10966.

If you use the Circle of Willis masks, please ALSO cite:

Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., ... & Menze, B. (2024). Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv, arXiv-2312.

1. The file titled "ich_plots_dlnm.Rmd" contains the code in R for calculating Spearman and Pearson's correlation coefficients as well as designing distributed lag non-linear models (DLNMs).

2. ich_prediction_nn notebook contains data analysis, feature importance estimation and prediction on stroke severity and outcomes (NHSS and MRS scores). Different models were used for prediction (namely, logistic regression, random forest, extra treees, ADAboost, SVC, and dense neural network).

Objective: To explore the utility of serum neurofilament light chain (sNfL) as a biomarker for primary and secondary neuroaxonal injury after ischemic stroke (IS) and study its value for the prediction of clinical outcome. Methods: We used an ultrasensitive single-molecule array (Simoa) assay to measure sNfL levels in healthy controls (HC, N=30) and two independent cohorts of patients with IS: (1) with serial serum sampling at hospital arrival (N=196), at days 2, 3, and 7 (N=89), and up to 6 months post-stroke; (2) with standardized MRI at baseline and at 6 months post-stroke, and with cross-sectional serum sampling at 6 months (N=95). We determined the temporal profile of sNfL levels, their association with imaging markers of neuroaxonal injury, and with clinical outcome. Results: Patients with IS had higher sNfL levels compared with HC starting from admission until 6 months post-stroke. sNfL levels peaked at day 7 (211.2 pg/ml [104.7–442.6], median [IQR]) and correlated with infarct volumes (day 7: partial r=0.736, p=1.5x10-15). 6 months post-stroke patients with recurrent ischemic lesions on MRI (N=19) had higher sNfL compared to those without new lesions (N=76, p=0.002). sNfL levels 6 months post-stroke further correlated with a quantitative measure of secondary neurodegeneration obtained from DTI MRI (r=0.361, p=0.001). sNfL levels 7 days post-stroke independently predicted modified Rankin scale scores 3 months post-stroke (cumulative OR [95% confidence interval] = 2.35 [1.60-3.45]; p=1.24x10-05). Conclusions: sNfL holds promise as a biomarker for monitoring primary and secondary neuroaxonal injury after IS and for predicting functional outcome.

Reperfusion injury following ischemic stroke treatment significantly affects patient prognosis. Myocardin-related transcription factor A (MRTF-A) has been shown to alleviate this injury, making it a promising therapeutic target. In this study, MRTF-A expression was modulated in rat middle cerebral artery occlusion/reperfusion (MCAO/R) models. MRTF-A mitigated reperfusion injury by enhancing the co-localization of PARD3 and Tiam1, thereby preserving synaptic structure and function. Since MRTF-A activation requires its dissociation from G-actin, an active molecular screening zone was defined based on the binding site of the G-actin/MRTF-A RPEL domain. A dataset was constructed using batch molecular docking to train an AttentiveFP deep learning regression model for predicting the binding affinity of small molecules to the G-actin domain. Through molecular scaffold analysis, ADME/T prediction, and molecular dynamics simulation, the potential agonist Pranlukast was identified. Pranlukast competes with the RPEL domain for G-actin binding, thereby promoting MRTF-A activation. Surface plasmon resonance (SPR) and immunofluorescence confirmed Pranlukast's strong binding affinity to G-actin and its ability to promote MRTF-A nuclear translocation. In vivo experiments demonstrated Pranlukast's efficacy in preventing ischemic stroke reperfusion injury and reversing synaptic structural and functional impairments caused by ischemic injury. This study, which integrates specific disease targets with deep learning-based intelligent screening, reveals for the first time that Pranlukast targets the G-actin/MRTF-A interaction via a non-classical pathway. By promoting MRTF-A nuclear translocation, it provides neurosynaptic protection post-ischemic reperfusion, offering a novel therapeutic strategy for ischemic stroke reperfusion injury.

BackgroundIschemic stroke poses a major threat to human health and represents the third leading cause of death worldwide and in Taiwan. Post-acute care (PAC) training has been reported to be beneficial for post-index stroke events. However, knowledge is still lacking on the outcome of stroke events with cardiac origin. The focus of the current study is to investigate the effectiveness of PAC in this subgroup of patients as well as identify key baseline pointers that are capable of early prediction of patients' physical recovery. In addition, the authors hypothesize that the routinely arranged non-invasive carotid duplex that evaluates the characteristics of the carotid lumen could play a significant role in providing an early outcome prediction.MethodsFor the current research, 142 ischemic stroke patients with underlying cardiac arrhythmia (atrial fibrillation) were retrospectively recruited. The patients' basic demographics, neuroimaging, carotid duplex, and basic biochemistry datasets were accurately documented. The pre and post-admission National Institutes of Health Stroke Scale (NIHSS) (6-month follow-ups), Barthel Index, and mRS score (12-month follow-ups) were also recorded. All statistical analyses were performed using R for Windows (version 3.6.3). Barthel Index, NIHSS, and mRS scores obtained before and after hospitalization were compared to determine the patients' outcomes and were classified as improved or unimproved. A multivariate logistic analysis was designed and applied to assess the significance of risk factors and to obtain the odds ratios (ORs). The receiver operating characteristic (ROC) curve and the Youden Index was used to find the important cut-off point information, and the area under the curve (AUC) was calculated to provide accuracy.ResultsThe average age of the 142 ischemic stroke patients enrolled in the current study was about 66 years, of which 88 patients were male and 54, female. Many of them had other comorbidities: 86 patients had mixed hyperlipidemia (60.56%), 115 had hypertension (80.99%), and 49 suffered from diabetes mellitus (34.51%). The mRS showed an improvement in the condition of only 40 patients (28.175%), whereas the Barthel Index showed improvement in 71 patients (50%), and 68 patients (47.89%) showed recovery on the NIHSS. The Barthel Index and NIHSS were selected because they already had an almost equal number of samples among the improved and unimproved groups (50%), rather than mRS, which had a lower number (28.17%) of improved cases. While conducting the EuroQol-5 Dimension (EQ-5D) assessment, anxiety/depression stood out as the most prominent issue, affecting 44 patients (30.99%). Self-care was another factor that was involved in the ongoing improvement of 36 patients (25.35%). Multivariate logistic analysis of both NIHSS and Barthel Index showed improvement with a contralateral plaque index statistical significance (P

Contribution of unique changes in HR and SV to changes in CO (Multiple linear regression analyses using change in CO as an outcome variable).

Detailed results are shown for the main ethnicity groups.

Background and objectivesStroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.Materials and methodsA total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.ResultsThe genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.ConclusionThe pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.

Correlation between measured and predicted VO2peak generated by different equation models.

Data in mean±SD.

Correlation between VO2peak and 6MWD, maximal HR, SV and CO during both 6MWT and CPET.

Introduction: Stroke remains a major cause of death and disability in Japan and worldwide. Detecting individuals at high risk for stroke to apply preventive approaches is recommended. This study aimed to develop a stroke risk prediction model among urban Japanese using cardiovascular risk factors. Methods: We followed 6,641 participants aged 30–79 years with neither a history of stroke nor coronary heart disease. The Cox proportional hazard model estimated the risk of stroke incidence adjusted for potential confounders at the baseline survey. The model’s performance was assessed using the receiver operating characteristic curve and the Hosmer-Lemeshow statistics. The internal validity of the risk model was tested using derivation and validation samples. Regression coefficients were used for score calculation. Results: During a median follow-up duration of 17.1 years, 372 participants developed stroke. A risk model including older age, current smoking, increased blood pressure, impaired fasting blood glucose and diabetes, chronic kidney disease, and atrial fibrillation predicted stroke incidence with an area under the curve = 0.76 and p value of the goodness of fit = 0.21. This risk model was shown to be internally valid (p value of the goodness of fit in the validation sample = 0.64). On a risk score from 0 to 26, the incidence of stroke for the categories 0–5, 6–7, 8–9, 10–11, 12–13, 14–15, and 16–26 was 1.1%, 2.1%, 5.4%, 8.2%, 9.0%, 13.5%, and 18.6%, respectively. Conclusion: We developed a new stroke risk model for the urban general population in Japan. Further research to determine the clinical practicality of this model is required.

Comparison of recall (R) and precision (P) in the negative class.

Reported metrics include sensitivity, specificity, precision, negative predictive value (NPV), and positive predictive value (PPV) at the probability threshold of 0.5. The overall c-statistic (AUC) and calibration χ2 results are also presented. The results refer to the aggregated population.