Comparisons between ticagrelor and clopidogrel for the secondary prevention of stroke in CYP2C19 loss-of-function carriers have not been extensively performed. We conducted a randomized, double-blind, placebo-controlled trial at 202 centers in China involving patients with a minor ischemic stroke or transient ischemic attack (TIA) who carried CYP2C19 loss-of-function alleles. Patients were assigned within 24 hours after symptom onset, in a 1:1 ratio, to receive ticagrelor (180 mg on day 1 followed by 90 mg twice daily on days 2 through 90) and placebo clopidogrel or to receive clopidogrel (300 mg on day 1 followed by 75 mg once daily on days 2 through 90) and placebo ticagrelor; both groups received aspirin for 21 days. The primary efficacy outcome was new stroke, and the primary safety outcome was severe or moderate bleeding, both within 90 days. A total of 11,255 patients were screened and 6412 patients were enrolled, with 3205 assigned to the ticagrelor group and 3207 to the clopidogrel group. The median age of the patients was 64.8 years, and 33.8% were women; 98.0% belonged to the Han Chinese ethnic group. Stroke occurred within 90 days in 191 patients (6.0%) in the ticagrelor group and 243 patients (7.6%) in the clopidogrel group (hazard ratio, 0.77; 95% confidence interval, 0.64 to 0.94; P=0.008). Secondary outcomes were generally in the same direction as the primary outcome. Severe or moderate bleeding occurred in 9 patients (0.3%) in the ticagrelor group and in 11 patients (0.3%) in the clopidogrel group; any bleeding occurred in 170 patients (5.3%) and 80 patients (2.5%), respectively. Among Chinese patients with minor ischemic stroke or TIA who were carriers of CYP2C19 loss-of-function alleles, the risk of stroke at 90 days was modestly lower with ticagrelor than with clopidogrel. The risk of severe or moderate bleeding did not differ between the two treatment groups, but ticagrelor was associated with more total bleeding events than clopidogrel. (Funded by the Ministry of Science and Technology of the People’s Republic of China and others; CHANCE-2 ClinicalTrials.gov number, NCT04078737.)

Tenecteplase is an effective thrombolytic agent for eligible patients with stroke who are treated within 4.5 hours after the onset of stroke. However, data regarding the effectiveness of tenecteplase beyond 4.5 hours are limited. In a trial conducted in China, we randomly assigned patients with large-vessel occlusion of the middle cerebral artery or internal carotid artery who had salvageable brain tissue as identified on perfusion imaging and who did not have access to endovascular thrombectomy to receive tenecteplase (at a dose of 0.25 mg per kilogram of body weight; maximum dose, 25 mg) or standard medical treatment within 4.5 to 24 hours after the time that the patient was last known to be well (including after stroke on awakening and unwitnessed stroke). The primary outcome was the absence of disability, which was defined as a score of 0 or 1 on the modified Rankin scale (range, 0 to 6, with higher scores indicating greater disability), at day 90. The key safety outcomes were symptomatic intracranial hemorrhage and death. A total of 516 patients were enrolled; 264 were randomly assigned to receive tenecteplase and 252 to receive standard medical treatment. Less than 2% of the patients (4 in the tenecteplase group and 5 in the standard-treatment group) underwent rescue endovascular thrombectomy. Treatment with tenecteplase resulted in a higher percentage of patients with a modified Rankin scale score of 0 or 1 at 90 days than standard medical treatment (33.0% vs. 24.2%; relative rate, 1.37; 95% confidence interval, 1.04 to 1.81; P = 0.03). Mortality at 90 days was 13.3% with tenecteplase and 13.1% with standard medical treatment, and the incidence of symptomatic intracranial hemorrhage within 36 hours after treatment was 3.0% and 0.8%, respectively. In this trial involving Chinese patients with ischemic stroke due to large-vessel occlusion, most of whom did not undergo endovascular thrombectomy, treatment with tenecteplase administered within 4.5 to 24 hours after stroke onset resulted in less disability and similar survival as compared with standard medical treatment, and the incidence of symptomatic intracranial hemorrhage appeared to be higher. (Funded by the National Natural Science Foundation of China and others; TRACE-III ClinicalTrials.gov number, NCT05141305.).

Behavioral risk factors such as tobacco use, unhealthy diet, insufficient physical activity and the harmful use of alcohol are known and modifiable contributors to a number of NCDs and health mediators. The purpose of this paper is to describe the distribution of main risk factors for NCDs by socioeconomic status (SES) among adults aged 50 years and older within a country and compare these risk factors across six lower- and upper-middle income countries. The study population in this paper draw from SAGE Wave 1 and consisted of adults aged 50-plus from China (N=13,157), Ghana (N=4,305), India (N=6,560), Mexico (N=2,318), the Russian Federation (N=3,938) and South Africa (N=3,836). Seven main common risk factors for NCDs were identified: daily tobacco use, frequent heavy drinking, low level physical activity, insufficient vegetable and fruit intake, high risk waist-hip ratio, obesity and hypertension. Multiple risk factors were also calculated by summing all these risk factors. The prevalence of daily tobacco use ranged from 7.7% (Ghana) to 46.9% (India), frequent heavy drinker was the highest in China (6.3%) and lowest in India (0.2%), and the highest prevalence of low physical activity was in South Africa (59.7%). The highest prevalence of respondents with high waist-to-hip ratio risk was 84.5% in Mexico, and the prevalence of self-reported hypertension ranging from 33% (India) to 78% (South Africa). Obesity was more common in South Africa, the Russia Federation and Mexico (45.2%, 36% and 28.6%, respectively) compared with China, India and Ghana (15.3%, 9.7% and 6.4%, respectively). China, Ghana and India had a higher prevalence of respondents with multiple risk factors than Mexico, the Russia Federation and South Africa. The occurrence of three and four risk factors was more prevalent in Mexico, the Russia Federation and South Africa. There were substantial variations across countries and settings, even between upper-middle income countries and lower-middle income countries. The baseline information on the magnitude of the problem of risk factors provided by this study can help countries and health policymakers to set up interventions addressing the global non-communicable disease epidemic.

Ischemic stroke is a leading cause of global mortality and long-term disability. However, there is a paucity of whole-genome sequencing studies on ischemic stroke, resulting in limited knowledge of the interplay between genomic and phenotypic variations among affected patients. Here, we outline the STROMICS design and present the first whole-genome analysis on ischemic stroke by deeply sequencing and analyzing 10,241 stroke patients from China. We identified 135.59 million variants, > 42% of which were novel. Notable disparities in allele frequency were observed between Chinese and other populations for 89 variants associated with stroke risk and 10 variants linked to response to stroke medications. We investigated the population structure of the participants, generating a map of genetic selection consisting of 31 adaptive signals. The adaption of the MTHFR rs1801133-G allele, which links to genetically evaluated VB9 (folate acid) in southern Chinese patients, suggests a gene-specific folate supplement strategy. Through genome-wide association analysis of 18 stroke-related traits, we discovered 10 novel genetic-phenotypic associations and extensive cross-trait pleiotropy at 6 lipid-trait loci of therapeutic relevance. Additionally, we found that the set of loss-of-function and cysteine-altering variants present in the causal gene NOTCH3 for the autosomal dominant stroke disorder CADASIL displayed a broad neuro-imaging spectrum. These findings deepen our understanding of the relationship between the population and individual genetic layout and clinical phenotype among stroke patients, and provide a foundation for future efforts to utilize human genetic knowledge to investigate mechanisms underlying ischemic stroke outcomes, discover novel therapeutic targets, and advance precision medicine.

Objective: To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU). Methods: In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare. Results: A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients’ 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively. Conclusions: RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.

Background: In-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.Materials and methods: A retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.Results: A total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.Conclusion: The prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.