Stroke Prediction Model Research Articles

Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.

Predicting stroke risk by Migraine using AI

Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. It is the second leading cause of death worldwide and one of the most life- threatening diseases for persons above 65 years. It damages the brain like “heart attack” which damages the heart. Every 4 minutes someone dies of stroke, but up to 80% of stroke can be prevented if we can identify or predict the occurrence of stroke in its early stage. In this paper, I used different types of machine learning algorithms for stroke prediction on the Healthcare Dataset Stroke data. Four types of machine learning classification algorithms were applied; Linear Regression, Confusion matrices, Random Forest Classifier, and Logistic Regression were used to build the stroke prediction model. Support, Precision, Recall, and F1-score were used to calculate performance measures of machine learning models. The results showed that Random Forest Classifier has achieved the best accuracy at 94 % [1].

Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion.

Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission.Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked.Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57–0.74) for LR, 0.66 (95% CI 0.57–0.74) for RLR, 0.55 (95% CI 0.45–0.64) for RF and 0.67 (95% CI 0.58–0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts.Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.

Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea.

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy.

The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011-2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

A Risk Prediction Model for Ischemic Stroke in Southern Chinese Population: Impact of Multiple Genetic Variants and Clinical/Lifestyle Factors

A Risk Prediction Model for Ischemic Stroke in Southern Chinese Population: Impact of Multiple Genetic Variants and Clinical/Lifestyle Factors

Predicting Stroke Risk in a Chinese Hypertensive Population Using Machine Learning

Background: Stroke is a major global health concern, and risk prediction is essential for primary prevention of stroke. However, uncertainty remains about the optimal methodology for analyzing stroke risk. In this study, we aim to determine the most effective stroke prediction method in a targeted population using machine learning and establish a general pipeline for future analysis. Method: Training set included 70% of data (n=14491) from China Stroke Primary Prevention Trial (CSPPT), a randomized, double-blind, multicenter clinical trial. Internal validation was processed with the rest 30% of CSPPT data (n=6211), and external validation was conducted using a Nested case-control (NCC) dataset (n=2568). Total analyzed participants were hypertensive adults who without prior history of stroke (n=23270). The primary outcome was first stroke. Four received analysis methods were processed and compared: logistic regression (LR), stepwise logistic regression (SLR), extreme gradient boosting (XGBoost), and random forest (RF). Population characteristic data with inclusion and exclusion of laboratory variables were separately analyzed. Accuracy, sensitivity, specificity, kappa, and area under receiver operating characteristic curves (AUCs) were used to make model assessments with AUCs the top concern. Data balancing techniques including random under sampling (RUS) and synthetic minority over sampling technique (SMOTE) were applied to process this unbalanced training set. Findings: Best model performance was observed in RUS applied RF model with laboratory variables. Compared with null models (sensitivity = 0, specificity = 100, mean AUCs = 0·643), data balancing techniques improved overall performance with RUS demonstrated a more satisfactory effect in current study (RUS: sensitivity = 63·9; specificity = 53·7; AUCs = 0.624 (mean). Adding laboratory variables improved performance of analysis methods. All results were reconfirmed in validation sets. The top 10 important variables were determined by the analysis method with the best performance. Interpretation: Among tested methods, the most effective stroke prediction model in targeted population is RUS applied RF. From the insights current study revealed, we provided general frameworks of building machine learning based prediction models. Funding Information: The study was supported by funding from the following: Jiangxi Outstanding Person Foundation [20192BCBL23024]; Key RD the National Natural Science Foundation of China [81960074, 81730019, 81973133]; Jiangxi Provincial Health Commission [202130440]; the National Key Research and Development Program [2016YFE0205400, 2018ZX09739010, 2018ZX09301034003], the Science and Technology Planning Project of Guangzhou, China [201707020010]; the Science, Technology and Innovation Committee of Shenzhen [JSGG20170412155639040, GJHS20170314114526143, JSGG20180703155802047]; the Economic, Trade and Information Commission of Shenzhen Municipality [20170505161556110, 20170505160926390]; the Research Fund Program of Guangdong Provincial Key Laboratory of Renal Failure Research, Clinical Innovation Research Program of Guangzhou Regenerative Medicine and Health Guangdong Laboratory [2018GZR0201003]. Declaration of Interests: Dr. Xiao Huang reports grants from Jiangxi Outstanding Person Foundation [20192BCBL23024], Key RD Jiangxi Provincial Health Commission [202130440]; Dr. Xiping Xu reports grants from the National Key Research and Development Program [2016YFE0205400, 2018ZX09739010, 2018ZX09301034003], the Department of Science and Technology of Guangdong Province [2020B121202010], the Science and Technology Planning Project of Guangzhou, China [201707020010], the Science, Technology and Innovation Committee of Shenzhen [GJHS20170314114526143, JSGG20180703155802047], the Economic, Trade and Information Commission of Shenzhen Municipality [20170505161556110, 20170505160926390, 201705051617070]. Dr. Xianhui Qin reports grants from the National Natural Science Foundation of China [81730019, 81973133]; the Research Fund Program of Guangdong Provincial Key Laboratory of Renal Failure Research, Clinical Innovation Research Program of Guangzhou Regenerative Medicine and Health Guangdong Laboratory [2018GZR0201003]. No other disclosures were reported. Ethics Approval Statement: Two data sets with similar baseline characteristics investigated by the same team were selected and analyzed in our study: The China Stroke Primary Prevention Trial (CSPPT) data set and the nested case-control (NCC) data set which is a subset from the H-type Hypertension and Stroke Prevention and Control Project (HSPCP). Both studies were approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University, Hefei, China and all participants from both studies provided written informed consent.

Clinical Impact of the Maximum Mean Value of Home Blood Pressure on Cardiovascular Outcomes: A Novel Indicator of Home Blood Pressure Variability.

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Mean Platelet Volume is a Prognostic Marker in Acute Ischemic Stroke Patients Treated with Intravenous Thrombolysis

Background and objectivesThere is conflicting data regarding the association between platelet parameters and prognosis of stroke patients treated with intravenous thrombolysis. We aimed to analyze this association in a cohort of patients treated with rtPA. Material and MethodsRetrospective, observational study in adult ischemic stroke patients treated with rtPA between January 2015 and February 2017. Demographic and clinical characteristics, stroke severity (NIHSS), etiology (TOAST), mean platelet volume (MPV), platelet count (PC), platelet distribution width (PDW) and functional outcome (mRS) at discharge and 90 days were recorded. The association between platelet parameters and unfavorable prognosis (mRS 3–6) was tested using non-parametric tests and logistic regression analysis. Results267 patients were included, 134 (50.2%) females, with a median (IQR) age of 74 years (64–82). The median admission NIHSS was 14 (8-19) and the most frequent etiology was cardioembolism (n = 115, 43.1%). At discharge, 170 (63.7%) patients had mRS 3–6. MPV values were higher in patients with mRS 3–6 (median 8.2fL versus 7.8fL, p = 0.013). This association remained significant (OR = 1.36, 95% CI 1.003-1.832, p = 0.048) after adjustment for variables associated with prognosis. There were no significant associations between other platelet parameters and prognosis. There was a trend to unfavorable prognosis at 90 days in patients with higher MPV. Regarding the association between platelet parameters and hemorrhagic transformation, higher PDW was associated with more severe hemorrhagic transformation (PH1/PH2). ConclusionsHigher MPV values were associated with unfavorable prognosis at discharge in patients treated with intravenous thrombolysis. Future studies should address its added value in stroke prediction models.

COMPARISON OF THE EUROPEAN SOCIETY OF HYPERTENSION STRATIFICATION AND EUROPEAN SOCIETY OF CARDIOLOGY HEARTSCORE FOR PREDICTION OF CORONARY ARTERY DISEASE AND STROKE IN ESSENTIAL HYPERTENSION

Objective: For estimation of overall cardiovascular risk ESH proposes stratification according to BP, prevalence of risk factors, asymptomatic TOD, diabetes, kidney and symptomatic cardiovascular disease, while the ESC HeartScore constitutes another potent predictive tool of adverse outcome. The purpose is to compare the predictive role of ESH stratification and ESC HeartScore for the incidence of the composite end-point of coronary artery disease (CAD) and stroke in a cohort of essential hypertensive patients. Design and method: We followed up 2150 essential hypertensives (mean age 55.7 years, 1085 males, office BP = 145/91 mmHg) for a mean period of 8 years. All subjects had at least one annual visit. CAD was defined as the history of myocardial infarction or significant coronary artery stenosis revealed by angiography or coronary revascularization procedure. Stroke was defined as rapid onset of a new neurological deficit persisting at least 24 hours unless death supervened confirmed by imaging findings. Results: The incidence of CAD, stroke and their composite over the follow-up period were 2.8% 1.11% and 3.9% respectively. By using the ESH stratification, 15.3% was of low and moderate risk, 54.4% was of moderate to high and high risk and 30.3% was high to very high and very high risk. According to the ESC HeartScore 89.2% was of low to moderate risk, 10% of high risk and 0.8% of very high risk. Cox-regression analyses revealed that high to very high and very high ESH risk category was related to increased risk for the composite end-point of CAD and stroke (hazard ratio = 4.5, p &lt; 0.0001), while focusing on the ESC Heart Score the composite end-point was predicted by the high risk category (hazard ratio = 3.43, p &lt; 0.0001). The ESH risk model had better fit than the ESC HeartScore due to the lowest Akaike's values (1442.66 vs 1498.31, respectively). Conclusions: The ESH risk stratification constitutes a better prediction model for CAD and stroke than the ESC HeartScore in essential hypertension and its estimation is essential in order to improve overall risk assessment in this setting.

Arm Motor Recovery After Ischemic Stroke: A Focus on Clinically Distinct Trajectory Groups.

Recovery of arm function poststroke is highly variable with some people experiencing rapid recovery but many experiencing slower or limited functional improvement. Current stroke prediction models provide some guidance for clinicians regarding expected motor outcomes poststroke but do not address recovery rates, complicating discharge planning. This study developed a novel approach to defining recovery groups based on arm motor recovery trajectories poststroke. In addition, between-group differences in baseline characteristics and therapy hours were explored. A retrospective cohort analysis was conducted where 40 participants with arm weakness were assessed 1 week, 6 weeks, 3 months, and 6 months after an ischemic stroke. Arm recovery trajectory groups were defined on the basis of timing of changes in the Fugl-Meyer Assessment Upper Extremity (FMA-UE), at least the minimal clinically important difference (MCID), 1 week to 6 weeks or 6 weeks to 6 months. Three recovery trajectory groups were defined: Fast (n = 19), Extended (n = 12), and Limited (n = 9). Between-group differences in baseline characteristics and therapy hours were assessed. Associations between baseline characteristics and group membership were also determined. Three baseline characteristics were associated with trajectory group membership: FMA-UE, NIH Stroke Scale, and Barthel Index. The Fast Recovery group received the least therapy hours 6 weeks to 6 months. No differences in therapy hours were observed between Extended and Limited Recovery groups at any time points. Three clinically relevant recovery trajectory groups were defined using the FMA-UE MCID. Baseline impairment, overall stroke severity, and dependence in activities of daily living were associated with group membership and therapy hours differed between groups. Stratifying individuals by recovery trajectory early poststroke could offer additional guidance to clinicians in discharge planning.

Utility of the CHA2DS2-VASc score for predicting ischaemic stroke in patients with or without atrial fibrillation: a systematic review and meta-analysis.

Anticoagulants are the mainstay treatment for stroke prevention in patients with non-valvular atrial fibrillation (NVAF), and the CHA2DS2-VASc score is widely used to guide anticoagulation therapy in this cohort. However, utility of CHA2DS2-VASc in NVAF patients is debated, primarily because it is a vascular scoring system, which does not incorporate atrial fibrillation related parameters. Therefore, we conducted a meta-analysis to estimate the discrimination ability of CHA2DS2-VASc in predicting ischaemic stroke overall, and in subgroups of patients with or without NVAF. PubMed and Embase databases were searched till June 2020 for published articles that assessed the discrimination ability of CHA2DS2-VASc, as measured by C-statistics, during mid-term (2-5 years) and long-term (>5 years) follow-up. Summary estimates were reported as random effects C-statistics with 95% confidence intervals (CIs). Seventeen articles were included in the analysis. Nine studies (n = 453747 patients) reported the discrimination ability of CHA2DS2-VASc in NVAF patients, and 10 studies (n = 138262 patients) in patients without NVAF. During mid-term follow-up, CHA2DS2-VASc predicted stroke with modest discrimination in the overall cohort [0.67 (0.65-0.69)], with similar discrimination ability in patients with NVAF [0.65 (0.63-0.68)] and in those without NVAF [0.69 (0.68-0.71)] (P-interaction = 0.08). Similarly, at long-term follow-up, CHA2DS2-VASc had modest discrimination [0.66 (0.63-0.69)], which was consistent among patients with NVAF [0.63 (0.54-0.71)] and those without NVAF [0.67 (0.64-0.70)] (P-interaction = 0.39). This meta-analysis suggests that the discrimination power of the CHA2DS2-VASc score in predicting ischaemic stroke is modest, and is similar in the presence or absence of NVAF. More accurate stroke prediction models are thus needed for the NVAF population.

Optimal Carotid Plaque Features on Computed Tomography Angiography Associated With Ischemic Stroke.

BackgroundStenosis has historically been the major factor used to determine carotid stroke sources. Recent evidence suggests that specific plaque features detected on imaging may be more highly associated with ischemic stroke than stenosis. We sought to determine computed tomography angiography (CTA) imaging features of carotid plaque that optimally discriminate ipsilateral stroke sources.Methods and ResultsIn this institutional review board–approved retrospective cross‐sectional study, 494 ipsilateral carotid CTA‐brain magnetic resonance imaging pairs were available for analysis after excluding patients with alternative stroke sources. Carotid CTA and clinical markers were recorded, a multivariable Poisson regression model was fitted, and backward elimination was performed with a 2‐sided threshold of P<0.10. Discriminatory value was determined using receiver operating characteristic analysis, area under the curve, and bootstrap validation. The final CTA carotid‐source stroke prediction model included intraluminal thrombus (prevalence ratio, 2.8 [P<0.001]; 95% CI, 1.6–4.9), maximum soft plaque thickness (prevalence ratio, 1.2 [P<0.001]; 95% CI, 1.1–1.4), and the rim sign (prevalence ratio, 2.0 [P=0.007]; 95% CI, 1.2–3.3). The final discriminatory value (area under the curve=78.3%) was higher than intraluminal thrombus (56.4%, P<0.001), maximum soft plaque thickness (76.4%, P=0.007), or rim sign alone (69.9%, P=0.001). Furthermore, NASCET (North American Symptomatic Carotid Endarterectomy Trial) stenosis categories (cutoffs of 50% and 70%) had lower stroke discrimination (area under the curve=67.4%, P<0.001).ConclusionsOptimal discrimination of ipsilateral carotid sources of stroke requires information on intraluminal thrombus, maximum soft plaque thickness, and the rim sign. These results argue against the sole use of carotid stenosis to determine stroke sources on CTA, and instead suggest these alternative markers may better diagnose vulnerable carotid plaque and guide treatment decisions.

Development and Validation of a Prediction Model for Stroke, Cardiac, and Mortality Risk After Non-Cardiac Surgery.

BackgroundCommonly used cardiovascular risk calculators do not provide risk estimation of stroke, a major postoperative complication with high morbidity and mortality. We developed and validated an accurate cardiovascular risk prediction tool for stroke, major cardiac complications (myocardial infarction or cardiac arrest), and mortality after non‐cardiac surgery.Methods and ResultsThis retrospective cohort study included 1 165 750 surgical patients over a 4‐year period (2007–2010) from the American College of Surgeons National Surgical Quality Improvement Program Database. A predictive model was developed with the following preoperative conditions: age, history of coronary artery disease, history of stroke, emergency surgery, preoperative serum sodium (≤130 mEq/L, >146 mEq/L), creatinine >1.8 mg/dL, hematocrit ≤27%, American Society of Anesthesiologists physical status class, and type of surgery. The model was trained using American College of Surgeons National Surgical Quality Improvement Program data from 2007 to 2009 (n=809 880) and tested using data from 2010 (n=355 870). Risk models were developed using multivariate logistic regression. The outcomes were postoperative 30‐day stroke, major cardiovascular events (myocardial infarction, cardiac arrest, or stroke), and 30‐day mortality. Major cardiac complications occurred in 0.66% (n=5332) of patients (myocardial infarction, 0.28%; cardiac arrest, 0.41%), postoperative stroke in 0.25% (n=2005); 30‐day mortality was 1.66% (n=13 484). The risk prediction model had high predictive accuracy with area under the receiver operating characteristic curve for stroke (training cohort=0.869, validation cohort=0.876), major cardiovascular events (training cohort=0.871, validation cohort=0.868), and 30‐day mortality (training cohort=0.922, validation cohort=0.925). Surgery types, history of stroke, and coronary artery disease are significant risk factors for stroke and major cardiac complications.ConclusionsPostoperative stroke, major cardiac complications, and 30‐day mortality can be predicted with high accuracy using this web‐based predictive model.

Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults.

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.

Development and validation of a 2-year new-onset stroke risk prediction model for people over age 45 in China.

Multiple factors, including increasing incidence, poor knowledge of stroke and lack of effective, noninvasive and convenient stroke risk prediction tools, make it more difficult for precautions against stroke in China. Effective prediction models for stroke may assist to establish better risk awareness and management, healthier lifestyle, and lower stroke incidence for people.The China Health and Retirement Longitudinal Survey was the development cohort. Logistic regression was applied to model's development, in which the candidate variables with statistically significant coefficient were included in the prediction model. The area under receiver operating characteristic curve (AUC) and 10-times cross-validation were used for internal validation. Cutoff point of high-risk group was measured by Youden index. The China Health and Nutrition Survey was the validation cohort.The development cohort and the validation cohort included 16557 and 5065 participants, and the incidence density was 358.207/100,000 person-year and 350.701/100,000 person-year, respectively. The model for 2-year new-onset stroke risk prediction included age, hypertension, diabetes, heart disease, and smoking. The AUC and cross-validation AUC were 0.707 (95% confidence interval[CI]: 0.664, 0.750) and the 0.710 (95% CI: 0.650, 0.736). The sensitivity, specificity and accuracy of the cutoff point were 0.774, 0.545, and 0.319. The AUC and cross-validation AUC were 0.800 (95% CI: 0.744, 0.856) and 0.811(95% CI:0.714, 0.847), and the sensitivity, specificity and accuracy of cutoff point being 0.857,0.569, and 0.426 in external validation.A simple prediction tool using 5 noninvasive and easily accessible factors can assist in 2-year new-onset stroke risk prediction in Chinese people over 45 years old, which is believed to be applicable in identifying high-risk individuals and health management in China.

Paradoxical Increase of Stroke in Patients with Defect of High Molecular Weight Multimers of the von Willebrand Factors following Transcatheter Aortic Valve Replacement.

Stroke is a major cause of disability after transcatheter aortic valve replacement (TAVR) and stroke prediction models and data are crucially needed. Following TAVR, high molecular weight (HMW) multimers defect of von Willebrand factor (VWF) as assessed by closure time of adenosine diphosphate (CT-ADP) value > 180 seconds is an independent predictor of bleeding events. This study sought to identify predictors of ischemic neurological events in patients who underwent TAVR and the specific impact of HMW multimers defect of VWF. Patients were prospectively enrolled between November 2012 and May 2018 at our institution. The CT-ADP, a point-of-care measure of hemostasis, was assessed the day before and 24 hours after the procedures. The rate of ischemic stroke and transient ischemic attack (TIA) was recorded up to 30 days after the procedures. Of 565 TAVR patients, ischemic stroke/TIA was observed in 21 (3.7%) patients within 30 days. Ischemic stroke/TIA was associated with major/life-threatening bleeding complications (MLBCs) (9 [43%] vs. 88 [16%], p = 0.002) and postprocedure CT-ADP > 180 seconds (10 [48%] vs. 116 [21%], p = 0.01). By multivariate analysis, MLBCs (odds ratio [OR]: 3.58; 95% confidence interval [CI]: 1.45-8.84; p = 0.006) and postprocedure CT-ADP > 180 seconds (OR: 3.38; 95% CI: 1.38-8.25; p = 0.008) were evidenced as independent predictors of ischemic stroke/TIA. MLBCs and CT-ADP > 180 seconds were identified as predictors for ischemic stroke or TIA. The present study suggests that the defects of HMW multimers of the VWFs may contribute not only to bleeding events but also to thrombotic events.

Application of the China-PAR stroke risk equations in a rural northern Chinese population

To validate five-year risk prediction models for stroke in a contemporary rural Northern Chinese population. Totally 6 483 rural adults aged 40 to 79 years without cardiovascular diseases were enrolled at baseline between June and August 2010, and followed up through January 2017. Expected prediction risk using the China-PAR (prediction for atherosclerotic cardiovascular disease risk in China) stroke risk equations were compared with the new Framingham stroke risk profile (FSRP). The recalibrated models were applied by adjusting the five-year baseline survival rate and the mean score to our rural northern Chinese population, while keeping other coefficient parameters the same as the original models. Kaplan-Meier analysis was used to obtain the observed event (nonfatal or fatal stroke) rate for the five years, and the expected-observed ratios were calculated to evaluate overestimation or underestimation in the cohort. The models were assessed by discrimination C statistic, calibration χ2, and calibration charts and plots for illustration as well. Over an average of (5.83 ± 1.14) years of the follow-up in this validation cohort with 6 483 rural Chinese participants, 438 subjects deve-loped a first stroke event. Recalibrated China-PAR stroke risk equations and FSRP well-performed for predicting five-year stroke risk in men, and had C statistics of 0.709 (95%CI, 0.675 - 0.743) and 0.721 (95%CI, 0.688 - 0.754), with calibration χ2 values being 5.7 (P = 0.770) and 13.6 (P = 0.137), respectively. However, both China-PAR and FSRP overestimated stroke events by 11.6% and 30.0% in women, and had C statistics of 0.713 (95%CI, 0.684-0.743) and 0.710 (95%CI, 0.679-0.740), respectively. Calibration χ2 values in women were 12.5 (P = 0.188) for China-PAR and 24.0 (P = 0.004) for FSRP. In addition, the calibration charts and plots illustrated good agreement between the observations and the predictions only in the China-PAR stroke risk equations, especially for men. In this validation cohort of rural northern Chinese adults, the China-PAR models had better performance of five-year stroke risk prediction than the FSRP, indicating that recalibrated China-PAR stroke risk equations might be appropriate tools for risk assessment and primary prevention of stroke in China.

Performance Evaluation of Model-Based Controller with On-board Polytropic Index Prediction Model for Compression Stroke under Transient Driving Condition of CI Engine

Performance Evaluation of Model-Based Controller with On-board Polytropic Index Prediction Model for Compression Stroke under Transient Driving Condition of CI Engine

P3409Comparison of the European Society of Hypertension stratification and European Society of Cardiology HeartScore for prediction of coronary artery disease and stroke in essential hypertension

Abstract Background/Introduction For estimation of overall cardiovascular risk the European Society of Hypertension (ESH) proposes stratification according to blood pressure (BP), prevalence of risk factors, asymptomatic target organ damage, diabetes, kidney and symptomatic cardiovascular disease, while the European Society of Cardiology (ESC) HeartScore constitutes another potent predictive tool of adverse outcome. Purpose The aim of the present study was to compare the predictive role of ESH stratification and ESC HeartScore for the incidence of the composite end-point of coronary artery disease (CAD) and stroke in a cohort of essential hypertensive patients. Methods We followed up 2150 essential hypertensives (mean age 55.7 years, 1085 males, office BP=145/91 mmHg) for a mean period of 8 years. All subjects had at least one annual visit. Patients were divided based on the ESH risk categories as well as according to the ESC HeartScore. CAD was defined as the history of myocardial infarction or significant coronary artery stenosis revealed by angiography or coronary revascularization procedure. Stroke was defined as rapid onset of a new neurological deficit persisting at least 24 hours unless death supervened confirmed by imaging findings. Results The incidence of CAD, stroke and their composite over the follow-up period were 2.8% (n=60), 1.11% (n=24) and 3.9% (n=84), respectively. By using the ESH stratification, regarding the total population 15.3% (n=329) was of low and moderate risk, 54.4% (n=1170) was of moderate to high and high risk and 30.3% (n=651) was high to very high and very high risk. According to the ESC HeartScore 89.2% (n=1918) was of low to moderate risk, 10% (n=215) of high risk and 0.8% (n=17) of very high risk. Cox-regression analyses revealed that high to very high and very high ESH risk category was related to increased risk for the composite end-point of CAD and stroke (hazard ratio=4.5, p&lt;0.0001), while focusing on the ESC Heart Score the composite end-point was predicted by the high risk category (hazard ratio=3.43, p&lt;0.0001). Using the Akaike's information criterion the ESH risk model had better fit than the ESC HeartScore due to the lowest Akaike's values (1442.66 vs 1498.31, respectively). Conclusions In essential hypertensive patients categorization of patients by means of the ESH stratification and the ESC HeartScore are both predictive of future cardiovascular events. Based on our results, the ESH risk stratification constitutes a better prediction model for CAD and stroke than the ESC HeartScore in essential hypertension and its estimation is essential in order to improve overall risk assessment in this setting.