1.Cardiovascular-obstetric state-of-the-art review: pulmonary hypertension in pregnancy.
Joy Yi Shan ONG ; Jeannie Jing Yi YAP ; Mahesh CHOOLANI ; Kian-Keong POH ; Pradip DASHRAATH ; Ting-Ting LOW
Singapore medical journal 2025;66(3):130-140
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*
2.Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.
Manhui ZHANG ; Xian XIA ; Qiqi WANG ; Yue PAN ; Guanyi ZHANG ; Zhigang WANG
Environmental Health and Preventive Medicine 2025;30():3-3
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
3.Expert consensus on endocrine hypertension screening (2025 version).
Chinese Journal of Internal Medicine 2025;64(4):288-301
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
4.Clinical practice guideline for the management of hypertension in China.
Chinese Medical Journal 2024;137(24):2907-2952
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*
5.Clinical research progress in pulse wave velocity in the assessment of vascular aging.
Jingjing WU ; Fei LI ; Jie WANG ; Jingjing CAI ; Hong YUAN ; Yao LU
Journal of Central South University(Medical Sciences) 2024;49(12):1991-1998
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*
6.Short-term survival survey and risk factors analysis of death in sepsis patients in intensive care unit.
Haoqi YAO ; Huijuan SHAO ; Jingxia LIU ; Jiangming ZHANG ; Dong LIU ; Dongmei LIU
Chinese Critical Care Medicine 2023;35(10):1039-1044
OBJECTIVE:
To analyze the 28-day survival status and influencing factors of adult patients with sepsis, providing reference for early diagnosis of sepsis prognosis and reducing sepsis mortality.
METHODS:
A retrospective cohort study was conducted. A total of 160 adult patients with sepsis in the department of intensive care unit of the 940th Hospital of Joint Logistic Support Force of PLA from January 2021 to December 2022 were enrolled. The general information, laboratory examination results within 24 hours after admission, clinical treatment measures, and prognosis of patients were collected. Univariate analysis and binary multivariate Logistic regression were performed to screen the risk factors that might affect the short-term prognosis of patients with sepsis. Receiver operator characteristic curve (ROC curve) was plotted to analyze the predictive value of various risk factors on the short-term death risk of sepsis patients.
RESULTS:
A total of 160 patients with sepsis were enrolled, of whom 91 survived in 28 days, and 69 died with a mortality of 43.12%. Compared with the survival group, the patients in the death group were older, more severe, had higher blood lactic acid (Lac) level, and had more complications such as hypertension and multiple organ dysfunction syndrome (MODS). A total of 22 related factors were statistically significant: age, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, length of hospital stay, Lac, interleukin-6 (IL-6), fibrinogen (FIB), international normalized ratio (INR), ratio of prothrombin time (PT) to healthy people, prothrombin activity (PTA), PT, thrombin time (TT), oxygenation index (PaO2/FiO2), aspartate aminotransferase (AST), ratio of AST to alanine amninotransferase (ALT), serum creatinine (SCr), blood urea nitrogen (BUN), site of infection, history of hypertension, concurrent MODS, implementation of continuous renal replacement therapy (CRRT), and treatment with vasoactive drugs. Combined with the results of the univariate analysis, variables that might affect the short-term prognosis of septic patients were included in the multivariate Logistic regression analysis. The results showed that APACHE II score ≥ 20 [odds ratio (OR) = 1.106, 95% confidence interval (95%CI) was 1.003-1.219], Lac ≥ 5 mmol/L (OR = 1.430, 95%CI was 1.041-1.964), combined with hypertension (OR = 13.879, 95%CI was 1.082-178.016), concurrent MODS (OR = 98.139, 95%CI was 18.252-527.672) was an independent risk factor for 28-day death in adult septic patients (all P < 0.05). ROC curve analysis showed that the combination of the four indicators including APACHE II score, Lac, combined with hypertension, concurrent MODS, had predictive value for short-term outcomes in patients with sepsis. The area under the ROC curve (AUC) was higher than that of the 4 indicators alone [AUC (95%CI): 0.952 (0.918-0.986) vs. 0.745 (0.670-0.820), 0.816 (0.748-0.883), 0.608 (0.518-0.699), 0.868 (0.810-0.927)], the sensitivity was 94.2%, and the specificity was 90.1%.
CONCLUSIONS
APACHE II score within 24 hours, Lac, combined with hypertension, and concurrent MODS are independent risk factors for short-term mortality in adult septic patients in ICU. The combination of these indicators can make meaningful early clinical judgments on the short-term prognosis of septic patients, thereby reducing the mortality.
Adult
;
Humans
;
Retrospective Studies
;
ROC Curve
;
Sepsis/diagnosis*
;
Prognosis
;
Intensive Care Units
;
Risk Factors
;
Hypertension
7.Clinical features and risk factors of left ventricular hypertrophy in children with primary hypertension.
Yang LIU ; Lin SHI ; Yao LIN ; Ya Qi LI ; Yan Yan LIU ; Hong Wei ZHANG
Chinese Journal of Pediatrics 2023;61(11):1031-1037
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
8.Accuracy and capability of tri-ponderal mass index in assessing cardio-metabolic risk factors in Chinese children and adolescents aged 3 to 17 years, compared with body mass index.
Rui CHEN ; Lang JI ; Lijuan MA ; Yitong CHEN ; Jiali DUAN ; Mingjing MA ; Ying SUN ; Jun TAI ; Linghui MENG
Chinese Medical Journal 2023;136(11):1339-1348
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
9.Severity of non-alcoholic fatty liver disease is a risk factor for developing hypertension from prehypertension.
Qirui SONG ; Qianhui LING ; Luyun FAN ; Yue DENG ; Qiannan GAO ; Ruixue YANG ; Shuohua CHEN ; Shouling WU ; Jun CAI
Chinese Medical Journal 2023;136(13):1591-1597
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
10.Cardiac Structural and Functional Features in Patients With Type 2 Diabetes Mellitus and Heart Failure With Preserved Ejection Fraction:A Study Based on Propensity Score Matching.
Ke-Ling PENG ; Yong-Ming LIU ; Xiao-Yan JIA ; Hua WANG ; Chun-Li GOU ; Li-Li XUE ; Quan ZOU ; Wen-Jun ZHANG
Acta Academiae Medicinae Sinicae 2023;45(2):264-272
Objective To investigate the cardiac structural and functional characteristics in the patients with heart failure with preserved ejection fraction (HFpEF) and type 2 diabetes mellitus (T2DM),and predict the factors influencing the characteristics. Methods A total of 783 HFpEF patients diagnosed in the Department of Geriatric Cardiology,the First Hospital of Lanzhou University from April 2009 to December 2020 were enrolled in this study.Echocardiography and tissue Doppler technique were employed to evaluate cardiac structure and function.According to the occurrence of T2DM,the patients were assigned into a HFpEF+T2DM group (n=332) and a HFpEF group (n=451).Propensity score matching (PSM)(in a 1∶1 ratio) was adopted to minimize confounding effect.According to urinary albumin excretion rate (UAER),the HFpEF+T2DM group was further divided into three subgroups with UAER<20 μg/min,of 20-200 μg/min,and>200 μg/min,respectively.The comorbidities,symptoms and signs,and cardiac structure and function were compared among the groups to clarify the features of diabetes related HFpEF.Multivariate linear regression was conducted to probe the relationship of systolic blood pressure,blood glucose,glycosylated hemoglobin,and UARE with cardiac structural and functional impairment. Results The HFpEF+T2DM group had higher prevalence of hypertension (P=0.001) and coronary heart disease (P=0.036),younger age (P=0.020),and larger body mass index (P=0.005) than the HFpEF group,with the median diabetic course of 10 (3,17) years.After PSM,the prevalence of hypertension and coronary heart disease,body mass index,and age had no significant differences between the two groups(all P>0.05).In addition,the HFpEF+T2DM group had higher interventricular septal thickness (P=0.015),left ventricular posterior wall thickness (P=0.040),and left ventricular mass (P=0.012) and lower early diastole velocity of mitral annular septum (P=0.030) and lateral wall (P=0.011) than the HFpEF group.Compared with the HFpEF group,the HFpEF+T2DM group showed increased ratio of early diastolic mitral filling velocity to early diastolic mitral annular velocity (E/e') (P=0.036).Glycosylated hemoglobin was correlated with left ventricular mass (P=0.011),and the natural logarithm of UAER with interventricular septal thickness (P=0.004),left ventricular posterior wall thickness (P=0.006),left ventricular mass (P<0.001),and E/e' ratio (P=0.049). Conclusion The patients with both T2DM and HFpEF have thicker left ventricular wall,larger left ventricular mass,more advanced left ventricular remodeling,severer impaired left ventricular diastolic function,and higher left ventricular filling pressure than the HFpEF patients without T2DM.Elevated blood glucose and diabetic microvascular diseases might play a role in the development of the detrimental structural and functional changes of the heart.
Humans
;
Aged
;
Heart Failure/diagnosis*
;
Diabetes Mellitus, Type 2
;
Stroke Volume
;
Glycated Hemoglobin
;
Blood Glucose
;
Propensity Score
;
Ventricular Function, Left
;
Hypertension

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