Developing and validating a new precise risk-prediction model for new-onset hypertension: The Jichi Genki hypertension prediction model (JG model)

Hypertension and its determinants in Abu Dhabi population: a retrospective cohort study

BACKGROUND

Preventing high blood pressure and its complications requires identifying its risk factors. This study assessed predictors of hypertension and its associated complications among Emirati adults in Abu Dhabi, United Arab Emirates (UAE).

METHODS

This retrospective cohort study was conducted by retrieving data from the Electronic Medical Records (EMR) of Emiratis who participated in a national cardiovascular screening program between 2011 and 2013. The study cohort comprised 8456 Emirati adults (18 years and above): 4095 women and 4361 men. The average follow-up period was 9.2 years, with a maximum of 12 years.

RESULTS

The age-adjusted hypertension prevalence in Abu Dhabi increased from 24.5% at baseline to 35.2% in 2023. At baseline, 61.8% of hypertensive patients had controlled blood pressure, which increased to 74.3% in 2023. Among those free from hypertension at screening, 835 patients (12.3%) were newly diagnosed during the follow-up period. Using Cox regression, the hypertension prediction model developed included age [ P value <0.001, hazard ratio 1.051, 95% confidence interval (CI) 1.046-1.056], SBP ( P value <0.001, hazard ratio 1.017, 95% CI 1.011-1.023) and DBP ( P value <0.001, hazard ratio 1.029, 95% CI 1.02-1.037), glycated hemoglobin ( P  < 0.001, hazard ratio 1.132, 95% CI 1.077-1.191), and high-density lipoprotein cholesterol (HDL-C) ( P value <0.001, hazard ratio 0.662, 95% CI 0.526-0.832). This prediction model had a c-statistic of 0.803 (95% CI 0.786-0.819). Using survival analysis (Kaplan-Meier), higher blood pressure was associated with more cardiovascular events and mortality during follow-up.

CONCLUSION

Targeting population-specific predictors of hypertension can prevent its progression and inform healthcare professionals and policymakers to decrease the incidence, complications, and mortality related to hypertension.

Comprehensive Health Assessment Using Risk Prediction for Multiple Diseases Based on Health Checkup Data

Introduction

Tools developed to assess individuals' comprehensive health status would be beneficial for personalized prevention and treatment. This study aimed to develop a set of risk prediction models to estimate the risk for multiple diseases such as heart, blood vessel, brain, metabolic, liver, and kidney diseases using health checkup data only.

Methods

This is a retrospective study that used health checkup data combined with diagnostic information from electronic health records of Kurashiki Central Hospital in Okayama, Japan. All exposure factors were measured at the first health checkup visit, including demographic characteristics, laboratory test results, lifestyle questionnaires, medication use, and medical history. Primary outcomes were the diagnoses of 15 diseases during the follow-up period. Cox proportional hazard regression was applied to develop risk prediction models for heart, blood vessel, brain, metabolic, liver, and kidney diseases. Area under the curve with 4-year risk assessments were performed to evaluate the models.

Results

From January 2012 to September 2022, a total of 92,174 individuals aged 15-96 years underwent general health checkups. The area under the curve of the models in validation datasets was as follows: atrial fibrillation, 0.81; acute myocardial infarction, 0.81; heart failure, 0.76; cardiomyopathy, 0.72; angina pectoris, 0.70; atherosclerosis, 0.82; hypertension, 0.80; cerebral infarction, 0.77; intracerebral hemorrhage, 0.68; subarachnoid hemorrhage, 0.50; type-2 diabetes mellitus, 0.82; hyperlipidemia, 0.70; alcoholic liver disease, 0.91; liver fibrosis, 0.92; and chronic kidney disease, 0.80.

Conclusions

A set of prediction models to estimate multi-disease risk simultaneously from health checkup results may help to assess comprehensive individual health status and facilitate personalized prevention and early diagnosis.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis

OBJECTIVE

Our goal was to review the available literature on prognostic risk prediction for incident hypertension, synthesize performance, and provide suggestions for future work on the topic.

METHODS

A systematic search on PUBMED and Web of Science databases was conducted for studies on prognostic risk prediction models for incident hypertension in generally healthy individuals. Study-quality was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST) checklist. Three-level meta-analyses were used to obtain pooled AUC/C-statistic estimates. Heterogeneity was explored using study and cohort characteristics in meta-regressions.

RESULTS

From 5090 hits, we found 53 eligible studies, and included 47 in meta-analyses. Only four studies were assessed to have results with low risk of bias. Few models had been externally validated, with only the Framingham risk model validated more than thrice. The pooled AUC/C-statistics were 0.82 (0.77-0.86) for machine learning models and 0.78 (0.76-0.80) for traditional models, with high heterogeneity in both groups (I2 > 99%). Intra-class correlations within studies were 60% and 90%, respectively. Follow-up time (P = 0.0405) was significant for ML models and age (P = 0.0271) for traditional models in explaining heterogeneity. Validations of the Framingham risk model had high heterogeneity (I2 > 99%).

CONCLUSION

Overall, the quality of included studies was assessed as poor. AUC/C-statistic were mostly acceptable or good, and higher for ML models than traditional models. High heterogeneity implies large variability in the performance of new risk models. Further, large heterogeneity in validations of the Framingham risk model indicate variability in model performance on new populations. To enable researchers to assess hypertension risk models, we encourage adherence to existing guidelines for reporting and developing risk models, specifically reporting appropriate performance measures. Further, we recommend a stronger focus on validation of models by considering reasonable baseline models and performing external validations of existing models. Hence, developed risk models must be made available for external researchers.

Application of deep neural survival networks to the development of risk prediction models for diabetes mellitus, hypertension, and dyslipidemia

OBJECTIVES

: Although numerous risk prediction models have been proposed, few such models have been developed using neural network-based survival analysis. We developed risk prediction models for three cardiovascular disease risk factors (diabetes mellitus, hypertension, and dyslipidemia) among a working-age population in Japan using DeepSurv, a deep feed-forward neural network.

METHODS

: Data were obtained from the Japan Epidemiology Collaboration on Occupational Health Study. A total of 51 258, 44 197, and 31 452 individuals were included in the development of risk models for diabetes mellitus, hypertension, and dyslipidemia, respectively; two-thirds of whom were used to develop prediction models, and the rest were used to validate the models. We compared the performances of DeepSurv-based models with those of prediction models based on the Cox proportional hazards model.

RESULTS

: The area under the receiver-operating characteristic curve was 0.878 [95% confidence interval (CI) = 0.864-0.892] for diabetes mellitus, 0.835 (95% CI = 0.826-0.845) for hypertension, and 0.826 (95% CI = 0.817-0.835) for dyslipidemia. Compared with the Cox proportional hazards-based models, the DeepSurv-based models had better reclassification performance [diabetes mellitus: net reclassification improvement (NRI) = 0.474, P  ≤ 0.001; hypertension: NRI = 0.194, P  ≤ 0.001; dyslipidemia: NRI = 0.397, P  ≤ 0.001] and discrimination performance [diabetes mellitus: integrated discrimination improvement (IDI) = 0.013, P  ≤ 0.001; hypertension: IDI = 0.007, P  ≤ 0.001; and dyslipidemia: IDI = 0.043, P  ≤ 0.001].

CONCLUSION

: This study suggests that DeepSurv has the potential to improve the performance of risk prediction models for cardiovascular disease risk factors.

Risk Assessment Score and Chi-Square Automatic Interaction Detection Algorithm for Hypertension Among Africans: Models From the SIREN Study

BACKGROUND

This study aimed to develop a risk-scoring model for hypertension among Africans.

METHODS

In this study, 4413 stroke-free controls were used to develop the risk-scoring model for hypertension. Logistic regression models were applied to 13 risk factors. We randomly split the dataset into training and testing data at a ratio of 80:20. Constant and standardized weights were assigned to factors significantly associated with hypertension in the regression model to develop a probability risk score on a scale of 0 to 1 using a logistic regression model. The model accuracy was assessed to estimate the cutoff score for discriminating hypertensives.

RESULTS

Mean age was 59.9±13.3 years, 56.0% were hypertensives, and 8 factors, including diabetes, age ≥65 years, higher waist circumference, (BMI) ≥30 kg/m2, lack of formal education, living in urban residence, family history of cardiovascular diseases, and dyslipidemia use were associated with hypertension. Cohen κ was maximal at ≥0.28, and a total probability risk score of ≥0.60 was adopted for both statistical weighting for risk quantification of hypertension in both datasets. The probability risk score presented a good performance-receiver operating characteristic: 64% (95% CI, 61.0-68.0), a sensitivity of 55.1%, specificity of 71.5%, positive predicted value of 70.9%, and negative predicted value of 55.8%, in the test dataset. Similarly, decision tree had a predictive accuracy of 67.7% (95% CI, 66.1-69.3) for the training set and 64.6% (95% CI, 61.0-68.0) for the testing dataset.

CONCLUSIONS

The novel risk-scoring model discriminated hypertensives with good accuracy and will be helpful in the early identification of community-based Africans vulnerable to hypertension for its primary prevention.

Exploring the Effects of Elevated Serum Uric Acid Levels on Hypertension: A Scoping Review of Hyperuricemia

Hypertension (HTN) is a global health concern due to its increasing prevalence and association with life-threatening complications. An intriguing area of investigation in HTN research is the relationship between HTN and hyperuricemia. In light of this, we conducted a review to summarize the relevant studies exploring the link between elevated serum uric acid (sUA) concentration and new-onset HTN. Through a comprehensive search of PubMed Central, MEDLINE, and PubMed databases, we identified 20 studies that met our inclusion criteria. The research encompassed various study designs, including cohort studies, cross-sectional studies, reviews, and clinical trials. Pathologically, the elevated sUA levels activate the renin-angiotensin system and also cause the formation of urate crystals, triggering inflammation in the kidneys. Additionally, direct effects on the endothelium contribute to inflammation, oxidative stress, nitric oxide depletion, and smooth muscle cell proliferation, ultimately leading to atherosclerosis. These diverse mechanisms collectively play a role in the pathogenesis of HTN. Interestingly, lowering sUA has been shown to reverse early-stage HTN dependent on uric acid. However, this effect is not observed in the uric acid-independent second stage of HTN. Various studies have demonstrated an independent and dose-dependent association between sUA levels and the prevalence of HTN across different populations and genders. The review highlights the potential role of uric acid-lowering drugs, like allopurinol, in the prevention and early-stage management of HTN. However, there is scarce research on the efficacy of other uric acid-lowering agents and combination therapies. We believe our review provides compelling evidence of the association between elevated sUA concentration and new-onset HTN. Identifying and managing hyperuricemia can provide a preventive approach to reducing the burden of HTN and its associated complications.

A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population

Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta's Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Objective

The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.

Methods

A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.

Results

The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.

Conclusions

XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants

Objective

To develop an optimal screening model to identify the individuals with a high risk of hypertension in China by comparing tree-based machine learning models, such as classification and regression tree, random forest, adaboost with a decision tree, extreme gradient boosting decision tree, and other machine learning models like an artificial neural network, naive Bayes, and traditional logistic regression models.

Methods

A total of 4,287,407 adults participating in the national physical examination were included in the study. Features were selected using the least absolute shrinkage and selection operator regression. The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. The tree-based machine learning models, other machine learning models, and traditional logistic regression models were constructed to identify individuals with hypertension, respectively. Top features selected using the best algorithm and the corresponding variable importance score were visualized.

Results

A total of 24 variables were finally included for analyses after the least absolute shrinkage and selection operator regression model. The sample size of hypertensive patients in the training set was expanded from 689,025 to 2,312,160 using the borderline synthetic minority over-sampling technique algorithm. The extreme gradient boosting decision tree algorithm showed the best results (area under the receiver operating characteristic curve of non-laboratory: 0.893 and area under the receiver operating characteristic curve of semi-laboratory: 0.894). This study found that age, systolic blood pressure, waist circumference, diastolic blood pressure, albumin, drinking frequency, electrocardiogram, ethnicity (uyghur, hui, and other), body mass index, sex (female), exercise frequency, diabetes mellitus, and total bilirubin are important factors reflecting hypertension. Besides, some algorithms included in the semi-laboratory analyses showed less improvement in the predictive performance compared to the non-laboratory analyses.

Conclusion

Using multiple methods, a more significant prediction model can be built, which discovers risk factors and provides new insights into the prediction and prevention of hypertension.

Statistical modeling of health space based on metabolic stress and oxidative stress scores

Background

Health space (HS) is a statistical way of visualizing individual’s health status in multi-dimensional space. In this study, we propose a novel HS in two-dimensional space based on scores of metabolic stress and of oxidative stress.

Methods

These scores were derived from three statistical models: logistic regression model, logistic mixed effect model, and proportional odds model. HSs were developed using Korea National Health And Nutrition Examination Survey data with 32,140 samples. To evaluate and compare the performance of the HSs, we also developed the Health Space Index (HSI) which is a quantitative performance measure based on the approximate 95% confidence ellipses of HS.

Results

Through simulation studies, we confirmed that HS from the proportional odds model showed highest power in discriminating health status of individual (subject). Further validation studies were conducted using two independent cohort datasets: a health examination dataset from Ewha-Boramae cohort with 862 samples and a population-based cohort from the Korea association resource project with 3,199 samples.

Conclusions

These validation studies using two independent datasets successfully demonstrated the usefulness of the proposed HS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14081-0.

Nomogram Including Serum Ion Concentrations to Screen for New-Onset Hypertension in Rural Chinese Populations Over a Short-Term Follow-up

BACKGROUND

This study aimed to establish a clinically useful nomogram to evaluate the probability of hypertension onset in the Chinese population.

METHODS AND RESULTS

A prospective cohort study was conducted in 2012-2013 and followed up in 2015 to identify new-onset hypertension in 4,123 participants. The dataset was divided into development (n=2,748) and verification (n=1,375) cohorts. After screening risk factors by lasso regression, a multivariate Cox regression risk model and nomogram were established. Among the 4,123 participants, 818 (19.8%) developed hypertension. The model identified 10 risk factors: age, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, high pulse rate, history of diabetes, family history of hypertension and stroke, intake frequency of bean products, and intensity of physical labor. The C-indices of the model in the development and validation cohorts were 0.744 and 0.768, respectively. After the inclusion of serum calcium and magnesium concentrations, the C-indices in the development and validation cohorts were 0.764 and 0.791, respectively, with areas under the curve for the updated model of 0.907 and 0.917, respectively. The calibration curve showed that the nomogram accurately predicted the probability of hypertension. The updated nomogram was clinically beneficial across thresholds of 10-60%.

CONCLUSIONS

The newly developed nomogram has good predictive ability and may effectively assess hypertension risk in high-risk rural areas in China.

Development and validation of a hypertension risk prediction model and construction of a risk score in a Canadian population

Identifying high-risk individuals for targeted intervention may prevent or delay hypertension onset. We developed a hypertension risk prediction model and subsequent risk sore among the Canadian population using measures readily available in a primary care setting. A Canadian cohort of 18,322 participants aged 35-69 years without hypertension at baseline was followed for hypertension incidence, and 625 new hypertension cases were reported. At a 2:1 ratio, the sample was randomly divided into derivation and validation sets. In the derivation sample, a Cox proportional hazard model was used to develop the model, and the model's performance was evaluated in the validation sample. Finally, a risk score table was created incorporating regression coefficients from the model. The multivariable Cox model identified age, body mass index, systolic blood pressure, diabetes, total physical activity time, and cardiovascular disease as significant risk factors (p < 0.05) of hypertension incidence. The variable sex was forced to enter the final model. Some interaction terms were identified as significant but were excluded due to their lack of incremental predictive capacity. Our model showed good discrimination (Harrel's C-statistic 0.77) and calibration (Grønnesby and Borgan test, [Formula: see text] statistic = 8.75, p = 0.07; calibration slope 1.006). A point-based score for the risks of developing hypertension was presented after 2-, 3-, 5-, and 6 years of observation. This simple, practical prediction score can reliably identify Canadian adults at high risk of developing incident hypertension in the primary care setting and facilitate discussions on modifying this risk most effectively.

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis

OBJECTIVE

We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.

METHODS

We systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist.

RESULTS

Of 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models.

CONCLUSION

We attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.

Prediction Models for the 5- and 10-Year Incidence of Home Morning Hypertension: The Ohasama Study

BACKGROUND

We aimed to develop risk prediction models for new-onset home morning hypertension.

METHODS

We followed up 978 participants without home hypertension in the general population of Ohasama, Japan (men: 30.1%, age: 53.3 years). The participants were divided into derivation (n = 489) and validation (n = 489) cohorts by their residential area. The C-statistics and calibration plots were assessed after the 5- or 10-year follow-up.

RESULTS

In the derivation cohort, sex, age, body mass index, smoking, office systolic blood pressure (SBP), and home SBP at baseline were selected as significant risk factors for new-onset home hypertension (≥135/85 mm Hg or the initiation of antihypertensive treatment) using the Cox model. In the validation cohort, Harrell's C-statistic for the 5-/10-year home hypertension was 0.7637 (0.7195-0.8100)/0.7308 (0.6932-0.7677), when we used the full model, which included the significant risk factors in the derivation cohort. The calibration test revealed good concordance between the observed and predicted 5-/10-year home hypertension probabilities (P ≥ 0.19); the regression slope of the observed probability on the predicted probability was 1.10/1.02, and the intercept was -0.04/0.06, respectively. A model without home SBP was also developed; for the 10-year home hypertension risk, the calibration test revealed a good concordance (P = 0.19) but Harrell's C-statistic was 0.6689 (0.6266-0.7067).

CONCLUSIONS

The full model revealed good ability to predict the 5- and 10-year home morning hypertension risk. Although the model without home SBP is acceptable, the low C-statistic implies that home BP should be measured to predict home morning hypertension precisely.

A risk scoring system to predict the risk of new-onset hypertension among patients with type 2 diabetes

Hypertension (HTN), which frequently co-exists with diabetes mellitus, is the leading major cause of cardiovascular disease and death globally. This study aimed to develop and validate a risk scoring system considering the effects of glycemic and blood pressure (BP) variabilities to predict HTN incidence in patients with type 2 diabetes. This research is a retrospective cohort study that included 3416 patients with type 2 diabetes without HTN and who were enrolled in a managed care program in 2001-2015. The patients were followed up until April 2016, new-onset HTN event, or death. HTN was defined as diastolic BP (DBP) ≥ 90 mm Hg, systolic BP (SBP) ≥ 140 mm Hg, or the initiation of antihypertensive medication. Cox proportional hazard regression model was used to develop the risk scoring system for HTN. Of the patients, 1738 experienced new-onset HTN during an average follow-up period of 3.40 years. Age, sex, physical activity, body mass index, type of DM treatment, family history of HTN, baseline SBP and DBP, variabilities of fasting plasma glucose, SBP, and DBP and macroalbuminuria were significant variables for the prediction of new-onset HTN. Using these predictors, the prediction models for 1-, 3-, and 5-year periods demonstrated good discrimination, with AUC values of 0.70-0.76. Our HTN scoring system for patients with type 2 DM, which involves innovative predictors of glycemic and BP variabilities, has good classification accuracy and identifies risk factors available in clinical settings for prevention of the progression to new-onset HTN.

Development of a risk prediction model for incident hypertension in Japanese individuals: the Hisayama Study

The identification of individuals at high risk of developing hypertension can be of great value to improve the efficiency of primary prevention strategies for hypertension. The objective of this study was to develop a risk prediction model for incident hypertension based on prospective longitudinal data from a general Japanese population. A total of 982 subjects aged 40-59 years without hypertension at baseline were followed up for 10 years (2002-12) for the incidence of hypertension. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg, or the use of antihypertensive agents. The risk prediction model was developed using a Cox proportional hazards model. A simple risk scoring system was also established based on the developed model. During the follow-up period (median 10 years, interquartile range 5-10 years), 302 subjects (120 men and 182 women) developed new-onset hypertension. The risk prediction model for hypertension consisted of age, sex, SBP, DBP, use of glucose-lowering agents, body mass index (BMI), parental history of hypertension, moderate-to-high alcohol intake, and the interaction between age and BMI. The developed model demonstrated good discrimination (Harrell's C statistic=0.812 [95% confidence interval, 0.791-0.834]; optimism-corrected C statistic based on 200 bootstrap samples=0.804) and calibration (Greenwood-Nam-D'Agostino χ² statistic=12.2). This risk prediction model is a useful guide for estimating an individual's absolute risk for hypertension and could facilitate the management of Japanese individuals at high risk of developing hypertension in the future.

Development of the prediction model for hypertension in patients with idiopathic inflammatory myopathies

Cardiac involvement is an important cause of morbidity and mortality in patients with idiopathic inflammatory myopathies (IIMs). Hypertension, an important cardiovascular risk factor for the general population, has a crucial role in heart involvement. However, few studies have focused on the hypertension associated with IIMs. This study aimed to develop and assess the prediction model for incident hypertension in patients with IIMs. A retrospective cohort study was performed on 362 patients with IIMs, of whom 54 (14.9%) were given a diagnosis of new-onset hypertension from January 2008 to December 2018. The predictors of hypertension in IIMs were selected by least absolute shrinkage and selection operator (LASSO) regression, multivariable logistic regression, and clinically relevance, and then these predictors were used to draw the nomogram. Discrimination, calibration and clinical usefulness of the model were evaluated using the C-index, calibration plot, and decision curve analysis, respectively. The predicting model was validated by the bootstrapping validation. The nomogram mainly included predictors such as age, diabetes mellitus, triglyceride, low-density lipoprotein-cholesterol (LDL-C), antinuclear antibodies (ANA), and smoking. This prediction model demonstrated good discrimination with a C-index of 0.754 (95%CI, 0.684 to 0.824) and good calibration. The C-index of internal validation was 0.728, and decision curve analysis demonstrated that this nomogram was clinically useful. Clinicians can use this prediction model to assess the risk of hypertension in IIMs patients, and early preventive measures should be taken to reduce the incidence of hypertension in high-risk patients.

Validation of the Framingham hypertension risk score in a middle eastern population: Tehran lipid and glucose study (TLGS)

Background

The Framingham hypertension risk score is a well-known and simple model for predicting hypertension in adults. In the current study, we aimed to assess the predictive ability of this model in a Middle Eastern population.

Methods

We studied 5423 participants, aged 20–69 years, without hypertension, who participated in two consecutive examination cycles of the Tehran Lipid and Glucose Study (TLGS). We assessed discrimination based on Harrell’s concordance statistic (c-index) and calibration (graphical comparison of predicted vs. observed). We evaluated the original, recalibrated (for intercept and slope), and revised (for beta coefficients) models.

Results

Over the 3-year follow-up period, 319 participants developed hypertension. The Framingham hypertension risk score performed well in discriminating between individuals who developed hypertension and those who did not (c-index = 0.81, 95% CI: 0.79–0.83). Initially, there was a systematic underestimation of the original risk score (events predicted), which was readily corrected by a simple model revision.

Conclusions

The revised Framingham hypertension risk score can be used as a screening tool in public health and clinical practice to facilitate the targeting of preventive interventions in high-risk Middle Eastern people.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10760-6.

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques

Hypertension 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.

Joint Effects of Body Mass Index and Waist Circumference on the Incidence of Hypertension in a Community-Based Chinese Population

OBJECTIVE

We aimed to investigate the relationships of body mass index (BMI), waist circumference (WC), and obesity defined using a combination of both indexes, with the incidence of hypertension in a Chinese community-based population.

METHODS

A total of 1,927 Chinese participants (57.2 ± 8.9 years old) with normal blood pressure at baseline were recruited from the Shijingshan community in Beijing. Incident hypertension was defined as blood pressure ≥140/90 mm Hg, self-reported hypertension, or the use of any antihypertensive medication at the follow-up visit.

RESULTS

During 2.3 years of follow-up, 19.1% (n = 97) of the men and 13.6% (n = 158) of the women developed incident hypertension. The adjusted odds ratios (ORs) (95% confidence intervals [CIs]) for obesity (BMI ≥30) were 3.49 (1.59-7.66) and 2.60 (1.48-4.55) for men and women, respectively. A 1-point increase in BMI was associated with 8% (OR = 1.08, 95% CI: 1.00-1.17) and 10% (OR = 1.10, 95% CI: 1.05-1.16) increases in the incidence of hypertension in men and women, respectively. Abdominal obesity (WC ≥90 cm in men and ≥85 cm in women) was positively associated with incident hypertension in both men (adjusted OR = 1.79, 95% CI: 1.10-2.91) and women (adjusted OR = 1.61, 95% CI: 1.09-2.40). A 1-cm increase in WC was associated with 4% (adjusted OR = 1.04, 95% CI: 1.01-1.07) and 4% (adjusted OR = 1.04, 95% CI: 1.02-1.07) increases in the incidence of hypertension in men and women, respectively. The combination of abnormal BMI and WC has the highest risk for hypertension in both men (adjusted OR = 3.10, 95% CI: 1.48-6.50) and women (adjusted OR = 2.51, 95% CI: 1.43-4.40).

CONCLUSIONS

This study shows that BMI, WC, and an index that combined the two are independently associated with incident hypertension in a Chinese community-based population.

The Autonomic Regulation of Circulation and Adverse Events in Hypertensive Patients during Follow-Up Study

PURPOSE

Comprehensive study of autonomic regulation assessed during follow-up could provide new detailed information about the risks stratification for hypertensive patients. Therefore, we investigated the associations of these indices with death, stroke, and revascularization during the follow-up observation of 55 patients.

METHODS

All patients were with target organ damage, and 27 of them had associated clinical conditions (ACC). Mean age of patients with and without ACC was 62.6 ± 4.2 and 51.9 ± 9.9 (mean ± SD) years, respectively. Follow-up was from 66 to 95 months. At entry, autonomic regulation was assessed by the tilt test, Valsalva maneuver, hand-grip test, and cold-stress vasoconstriction. Hemodynamic parameters were measured by continuous blood pressure monitoring, occlusion plethysmography, and electrocardiography. Re-examination of patients was carried out by questioning and physical and laboratory examination.

RESULTS

We found that fatal outcomes were associated with a lower Valsalva index (1.34 ± 0.16 vs. 1.69 ± 0.37, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%, P < 0.05) and depressed cold vasoconstriction (0.20 ± 0.02 vs. 0.39 ± 0.16%.

CONCLUSIONS

This study shows that such autonomic regulation indices as Valsalva index, blood pressure dynamics in the tilt test, cold-stress vasomotor reactivity, and BPV are important for prognosis of hypertension course.