Pulmonary hypertension in pregnancy has been associated with negative maternal and fetal outcomes over the past decades. With the emergence of novel treatment modalities, morbidity and mortality of women who have pulmonary hypertension in pregnancy have improved. In this review, we aim to explore the contemporary updates in the management of pre-capillary and post-capillary pulmonary hypertension in pregnancy.
Humans ; Pregnancy ; Female ; Hypertension, Pulmonary/physiopathology* ; Pregnancy Complications, Cardiovascular/diagnosis* ; Pregnancy Outcome ; Antihypertensive Agents/therapeutic use*

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension. METHODS: We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension. RESULTS: A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles. CONCLUSION We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.
Humans ; China/epidemiology* ; Hypertension/diagnosis* ; Machine Learning ; Male ; Female ; Middle Aged ; Adult ; Nutrition Surveys ; Algorithms ; Aged ; Risk Factors

Endocrine hypertension is a common form of secondary hypertension. Affected patients often present without specific symptoms or signs, making the condition easy to overlook or misdiagnose. Early screening can greatly enhance the level of diagnosis while prompt treatment can improve the prognosis of patients. Based on clinical evidence and practice, the Chinese Endocrinologist Association and the Chinese Endocrine Hypertension Collaboration Group reached this consensus after extensive discussions on target populations, screening methods, interpretation of screening results, and other issues related to endocrine hypertension.
Humans ; Hypertension/diagnosis* ; Consensus ; Mass Screening

UNLABELLED: In China, hypertension is the most common chronic non-communicable disease and the most significant risk factor for cardiovascular mortality among urban and rural residents. To standardize the clinical diagnosis and treatment of hypertension and to improve the prevention and control level of hypertension in China, Chinese Society of Cardiology, Chinese Medical Association; Hypertension Committee of Cross-Straits Medicine Exchange Association; Cardiovascular Disease Prevention and Rehabilitation Committee, Chinese Association of Rehabilitation Medicine, jointly collaborated to formulate the Clinical Practice Guideline for Hypertension Management in China. The Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach was used to rate the quality of evidence and strength of recommendations, and the reporting items for practice guidelines in healthcare (RIGHT) were followed to establish the guideline. Detailed evidence-based recommendations for the diagnosis, evaluation, and treatment of 44 clinical questions in the field of hypertension, including essential and secondary hypertension, have been provided to guide clinical practice. REGISTRATION International Practice Guidelines Registry Platform, http://www.guidelines-registry.cn/ , No. IPGRP-2021CN346.
Humans ; Hypertension/diagnosis* ; China ; Antihypertensive Agents/therapeutic use*

Vascular aging refers to the degenerative changes in vascular wall structure and vasodilatory function, forming the pathophysiological basis for the onset and progression of cardiovascular disease (CVD). Pulse wave velocity (PWV), a non-invasive method for evaluating and detecting early vascular aging, has achieved significant results in predicting CVD risk and evaluating the efficacy of pharmacological treatments. PWV can effectively predict CVD risk across various populations, including healthy individuals, patients with hypertension, diabetes, and chronic inflammatory diseases. In patients with comorbidities such as hypertension, pharmacological interventions, such as anti-inflammatory, lipid-lowering, anti-hypertensive, and anti-diabetic treatments, can effectively reduce PWV and thus slow down vascular aging. Therefore, PWV is not only a vital tool for assessing early vascular aging but also an important indicator for evaluating treatment outcomes. Regular monitoring of PWV levels is of great significance in predicting CVD risk, evaluating therapeutic efficacy, and guiding clinical decision-making.
Humans ; Pulse Wave Analysis/methods* ; Cardiovascular Diseases/diagnosis* ; Aging/physiology* ; Vascular Stiffness/physiology* ; Hypertension/physiopathology* ; Risk Factors ; Blood Vessels/physiopathology*

Humans ; Hypertension/diagnosis*

Humans ; Hypertension/diagnosis*

Objective: To assess the clinical features and relative factors of left ventricular hypertrophy (LVH) in children with primary hypertension. Methods: In this retrospective cohort study, 430 children diagnosed with primary hypertension in Children's Hospital, Capital Institute of Pediatrics from January 2019 to September 2022 were enrolled. Their clinical data was analyzed and LVH was assessed by echocardiography. According to left ventricular geometry, these children were assigned to the LVH group and normal geometry group. General conditions, laboratory indicators and ambulatory blood pressure parameters between two groups were compared by independent sample t-test or Mann-Whitney U test. Spearman correlation coefficient was used to analyze the correlation between LVH and clinical indicators including blood pressure, biochemical and metabolic indicators. The independent risk factors of LVH were analyzed by multivariable logistic regression. The receiver operating characteristic (ROC) curve was used to explore the value of risk factors in the diagnosis of LVH. Results: Among the 430 children with primary hypertension, 342 (79.5%) were males and 88 (20.5%) females. Their age was (12.6±2.3) years, and 123 children (28.6%) of them had LVH. Body mass index (BMI) ((30.0±5.2) vs. (26.2±4.3) kg/m²), ratio of stage 2 hypertension (75.6% (93/123) vs. 59.6% (183/307)), 24-hour systolic blood pressure (24 h SBP)((131±10) vs. (128±10) mmHg,1 mmHg=0.133 kPa), daytime systolic blood pressure (SBP) ((135±11) vs. (131±11) mmHg), nighttime SBP ((128±11) vs. (123±10) mmHg), cholesterol level ((4.0±0.7) vs. (3.9±0.7) mmol/L), serum uric acid level ((447±81) vs. (426±91) μmol/L) and incidence of hyperinsulinemia (69.9% (86/123) vs.59.0% (181/307)) were significantly elevated in the LVH group compared with those in the normal geometry group (all P<0.05). There were more patients with a disease course over 5 years in the LVH group than in the normal geometry group, with a statistically significant difference (χ²=8.90,P=0.031). Spearman correlation analysis showed that BMI, 24 h SBP, daytime SBP, nighttime SBP, triglyceride, uric acid, and serum sodium level were positively correlated with LVMI (r=0.43, 0.20, 0.18, 0.18, 0.18, 0.16, and 0.12, all P<0.05). BMI, hyperinsulinemia, and cholesterol level were positively correlated with relative wall thickness (RWT) (r=0.22, 0.12, and 0.16, all P<0.05). The multivariate logistic regression analysis showed that BMI (OR=1.17, 95%CI 1.10-1.25) and 24 h SBP (OR=1.04, 95%CI 1.01-1.08) were the independent risk factors for LVH (both P<0.05). The area under the receiver operator characteristic curve, combined with BMI and 24 h SBP, was 0.72 (95%CI 0.67-0.77, P<0.05), with a sensitivity and specificity of 71.5% and 64.8%, respectively. Conclusions: BMI and 24 h SBP are the independent risk factors for LVH in children with primary hypertension, and the combination of BMI and 24 h SBP has an acceptable diagnostic value for LVH. Early monitoring of these indexes is necessary to predict preclinical cardiac damage.
Male ; Female ; Humans ; Child ; Adolescent ; Hypertension/diagnosis* ; Hypertrophy, Left Ventricular/etiology* ; Uric Acid ; Blood Pressure Monitoring, Ambulatory ; Retrospective Studies ; Blood Pressure/physiology* ; Risk Factors ; Essential Hypertension ; Hyperinsulinism ; Cholesterol

BACKGROUND: Tri-ponderal mass index (TMI) has been reported to be a more accurate estimate of body fat than body mass index (BMI). This study aims to compare the effectiveness of TMI and BMI in identifying hypertension, dyslipidemia, impaired fasting glucose (IFG), abdominal obesity, and clustered cardio-metabolic risk factors (CMRFs) in 3- to 17-year-old children. METHODS: A total of 1587 children aged 3 to 17 years were included. Logistic regression was used to evaluate correlations between BMI and TMI. Area under the curves (AUCs) were used to compare discriminative capability among indicators. BMI was converted to BMI- z scores, and accuracy was compared by false-positive rate, false-negative rate, and total misclassification rate. RESULTS: Among children aged 3 to 17 years, the mean TMI was 13.57 ± 2.50 kg/m 3 for boys and 13.3 ± 2.33 kg/m 3 for girls. Odds ratios (ORs) of TMI for hypertension, dyslipidemia, abdominal obesity, and clustered CMRFs ranged from 1.13 to 3.15, higher than BMI, whose ORs ranged from 1.08 to 2.98. AUCs showed similar ability of TMI (AUC: 0.83) and BMI (AUC: 0.85) in identifying clustered CMRFs. For abdominal obesity and hypertension, the AUC of TMI was 0.92 and 0.64, respectively, which was significantly better than that of BMI, 0.85 and 0.61. AUCs of TMI for dyslipidemia and IFG were 0.58 and 0.49. When 85th and 95th of TMI were set as thresholds, total misclassification rates of TMI for clustered CMRFs ranged from 6.5% to 16.4%, which was not significantly different from that of BMI- z scores standardized according to World Health Organization criteria. CONCLUSIONS TMI was found to have equal or even better effectiveness in comparison with BMI in identifying hypertension, abdominal obesity, and clustered CMRFs TMI was more stable than BMI in 3- to 17-year-old children, while it failed to identify dyslipidemia and IFG. It is worth considering the use of TMI for screening CMRFs in children and adolescents.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Body Mass Index ; Dyslipidemias ; East Asian People ; Hypertension ; Obesity, Abdominal ; Pediatric Obesity/diagnosis* ; Cardiometabolic Risk Factors

BACKGROUND: There is little published evidence about the role of non-alcoholic fatty liver disease (NAFLD) in the progression from prehypertension to hypertension. This study was conducted to investigate the association of NAFLD and its severity with the risk of hypertension developing from prehypertension. METHODS: The study cohort comprised 25,433 participants from the Kailuan study with prehypertension at baseline; those with excessive alcohol consumption and other liver diseases were excluded. NAFLD was diagnosed by ultrasonography and stratified as mild, moderate, or severe. Univariable and multivariable Cox proportional hazard regression was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) of incident hypertension according to the presence and 3 categories of severity of NAFLD. RESULTS: During a median of 12.6 years of follow-up, 10,638 participants progressed to hypertension from prehypertension. After adjusting for multiple risk factors, patients with prehypertension and NAFLD had a 15% higher risk of incident hypertension than those without NAFLD (HR = 1.15, 95% CI 1.10-1.21). Moreover, the severity of NAFLD was associated with the incidence of hypertension, which was higher in patients with more severe NAFLD (HR = 1.15 [95% CI 1.10-1.21] in the mild NAFLD group; HR = 1.15 [95% CI 1.07-1.24] in the moderate NAFLD group; and HR = 1.20 [95% CI 1.03-1.41] in the severe NAFLD group). Subgroup analysis indicated that age and baseline systolic blood pressure may modify this association. CONCLUSIONS NAFLD is an independent risk factor for hypertension in patients with prehypertension. The risk of incident hypertension increases with the severity of NAFLD.
Humans ; Non-alcoholic Fatty Liver Disease/complications* ; Prehypertension/diagnosis* ; Risk Factors ; Hypertension ; Incidence