It has been estimated that hypertension is the cause of approximately 13% of all deaths worldwide each year. 1 In light of the world's population growth and aging demographic, hypertension is a global burden, along with other cardiovascular and age-related diseases. 2,3 Early intervention with lifestyle modifications and treatment of "prehypertension" may reduce the incidence and long-term consequences of clinical hypertension. [4][5][6][7] Recent guidelines lowered the recommended thresholds for diagnosing hypertension or abnormal "elevated blood pressure (BP)" and the BP goal during antihypertensive therapy. 8-10 Therefore, the ability to predict an individual's risk of developing hypertension would be helpful for clinicians. They could then plan and prescribe personalized lifestyle modifications or make therapeutic decisions designed to prevent or postpone the development of hypertension. There are several models available to predict the risk of new-onset hypertension; these have been developed in Western and Asian countries using traditional statistical methods (eg, Cox regression or logistic regression). 11,12 Arterial stiffness is increasingly being recognized as making an important contribution to increases in systolic BP (SBP) and the development of hypertension in general populations, independent of traditional hypertension risk factors. [13][14][15][16] In addition, arterial AbstractHypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset hypertension. In Japan, Industrial Safety and Health Law requires employers to provide annual health checkups to their employees. We used 2005-2016 health checkup data from 18 258 individuals, at the time of hypertension diagnosis [Year (0)] and in the two previous annual visits [Year (−1) and Year (−2)].Data were entered into models based on machine learning methods (XGBoost and ensemble) or traditional statistical methods (logistic regression). Data were randomly split into a derivation set (75%, n = 13 694) used for model construction and development, and a validation set (25%, n = 4564) used to test performance of the derived models. The best predictor in the XGBoost model was systolic blood pressure during cardio-ankle vascular index measurement at Year (−1). Area under the receiver operator characteristic curve values in the validation cohort were 0.877, 0.881, and 0.859 for the XGBoost, ensemble, and logistic regression models, respectively. We have developed a highly precise prediction model for future hypertension using machine learning methods in a general normotensive population. This could be used to identify at-risk individuals and facilitate earlier non-pharmacological intervention to prevent the future development of hypertension.

BACKGROUND Although many studies have reported that the presence of minor or major ST-T change of electrocardiography (ECG) was associated with a risk of cardiovascular events, it is not clear whether there is a difference in the prognostic power depending on the summation of ST-T area (ST-Tarea) assessed by a quantitative method. METHODS Electrocardiograms were performed in 834 clinical patients with one or more cardiovascular risks. ST-Tarea was assessed as the area enclosed by the baseline from the end of the QRS complex to the end of the ST-T segment using a computerized quantitative method. We used the lower magnitude of ST-Tarea in the V5 or V6 lead for the analysis. RESULTS After a mean follow-up 8.4 ± 2.9 years (7,001 person-years), there were 92 cardiovascular events. With adjustment for covariates, the results from Cox proportional hazards models (Model 1) suggested that the lowest quartile of ST-Tarea was associated with a higher risk for cardiovascular outcome compared with the remaining quartile groups (hazard ratio, 2.08; 95% confidence interval, 1.36–3.16, P < 0.01). Even when adding the ECG left ventricular hypertrophy by Cornell voltage (Model 2) and Cornell product (Model 3) to Model 1, the significance remained (both P < 0.01). When we used ST-Tarea as a continuous variable substitute for the lowest quartile of ST-Tarea, these associations were similar in all models (all P < 0.01). CONCLUSION The lower summations of ST-T area assessed by a computerized quantitative method were associated with increased risk of cardiovascular disease incidence in a clinical population.

Background: Aortic valve stenosis (AS) is the most common valve disease in an elderly population, therefore, simple screening examination for AS is needed. Although a prolonged carotid upstroke time (UT), and prolonged ejection time (ET) of a brachial pulse wave (BPW) have been observed in severe AS patients, it has been unclear which BPW parameters have a better correlation with the severity of AS. The aim of this study was to examine which BPW parameters are most relevant to the severity of AS.Methods: Sixty-five Consecutive moderate and severe AS patients who were evaluated by trans-thoracic echocardiography were enrolled in this study. Control patients who were adjusted for age, gender, and blood pressure among outpatients were enrolled (N = 110). UT, ET, initial upstroke time (iUT), and half rise time of upstroke (1/2 hrUT) were evaluated correlations between mean pressure gradient (mPG) among AS patients.Results: iUT and 1/2 hrUT have significant correlations with mPG among AS patients (iUT: R = 0.50, 95% CI = 0.29-0.67, p < 0.0001; 1/2 hrUT: R = 0.41, 95% CI = 0.19-0.60, p < 0.001), whereas UT and ET did not. Multivariate logistic regression analysis showed area under curve (AUC) of iUT and 1/2 hrUT were higher than UT and ET to predict mPG >40 mmHg (AUC: iUT vs 1/2 hrUT vs UT vs ET = 0.90 vs 0.89 vs 0.69 vs 0.77). Conclusion:The severity of AS appeared strongly in the first half of the BPW upstroke. iUT and 1/2 hrUT may be a simple and useful screening test to assess the severity of AS.