External validation of ABCD series scores for predicting early stroke events following transient ischemic attack in a large nationwide registry.

Introduction: In the context of modern guideline-based strategies, new validations of prognostic scores for predicting early stroke risk are needed. We aimed to compare the validity of the ABCD series scores and assess the incremental values of risk components for predicting in-hospital stroke events in patients with transient ischemic attack (TIA). Patients and methods: We abstracted data from the Chinese Stroke Center Alliance (CSCA), a nationwide registry with 68,433 TIA patients admitted within 7 days of symptom onset from 1476 hospitals. TIA was defined by time-based criteria according to the World Health Organization (WHO). The discrimination of ABCD, ABCD2, ABCD2-I, and ABCD3 scores for predicting in-hospital stroke events was assessed by the area under the receiver-operating characteristics curves (AUC). The incremental predictive values of added risk predictor were determined by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: A total of 29,286 TIA patients were included, of whom 1466 (5.0%) had in-hospital stroke events. Compared with ABCD2-I score (AUC 0.79, 95% confidence interval [CI] 0.77-0.80), ABCD (AUC 0.58, 95% CI 0.57-0.60), ABCD2 (AUC 0.58, 95% CI 0.56-0.59), and ABCD3 (AUC 0.58, 95% CI 0.56-0.60) had lower predictive utility. An incremental value was observed when adding infarction on DWI (IDI = 0.0597, NRI = 1.1036) into ABCD2 score to be ABCD2-I. Conclusion: The traditional scales utilizing medical history (ABCD, ABCD2, and ABCD3 scores) show fair ability for predicting in-hospital stroke events after TIA, but the ABCD2-I score, which adds infarction on DWI, improves the predictive ability.

Systematic estimation of BMI: A novel insight into predicting overweight/obesity in undergraduates.

The prevalence of overweight-obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight-obese. This cross-sectional study aimed to investigate the prevalence of overweight-obesity and explore in depth the connection between eating habits and overweight-obesity among Chinese undergraduates.The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight-obesity were investigated by the Pearson χ test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis.The prevalence of undergraduate overweight-obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147-3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178-3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137-0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090-15.933) were significantly associated with overweight-obesity. Eating very fast was positively associated with overweight-obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622-18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614-187.363; P = .019).Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.