Hypertension Prediction Model Research Articles

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

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). 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. 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. 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.

Machine Learning Approach for Predicting Hypertension Based on Body Composition in South Korean Adults.

(1) Background: Various machine learning techniques were used to predict hypertension in Korean adults aged 20 and above, using a range of body composition indicators. Muscle and fat components of body composition are closely related to hypertension. The aim was to identify which body composition indicators are significant predictors of hypertension for each gender; (2) Methods: A model was developed to classify hypertension using six different machine learning techniques, utilizing age, BMI, and body composition indicators such as body fat mass, lean mass, and body water of 2906 Korean men and women; (3) Results: The elastic-net technique demonstrated the highest classification accuracy. In the hypertension prediction model, the most important variables for men were age, skeletal muscle mass (SMM), and body fat mass (BFM), in that order. For women, the significant variables were age and BFM. However, there was no difference between soft lean mass and SMM; (4) Conclusions: Hypertension affects not only BFM but also SMM in men, whereas in women, BFM has a stronger effect than SMM.

A Nomogram Prediction Model for Persistent Pulmonary Hypertension of the Newborn in Neonates Hospitalized for the First Time After Birth.

Persistent pulmonary hypertension of the newborn (PPHN) is one of the critical neonatal diseases associated with high morbidity and mortality. This study attempted to conduct a nomogram prediction model for performing early identification of PPHN and providing effective information for clinical practice. A total of 456 newborns who first admitted to the hospital after birth were included in the analysis, including 138 newborns with PPHN and 318 newborns without PPHN (as controls). The optimal predictive variables selection was performed based on LASSO (least absolute shrinkage and selection operator) regression and multivariate logistic regression. Using the selected variables, a nomogram prediction model was developed. To validate the model, the model was assessed using the receiver operating characteristic curve, calibration plot, and clinical impact curve. Six predictors, namely, gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pa o2 , were identified by LASSO and multivariate logistic regression analysis from the original 30 variables studied. The constructed model, using these predictors, exhibited favorable predictive ability for PPHN, with an area under the receiver operating characteristic of 0.897 (sensitivity = 0.876, specificity = 0.785) in the training set and 0.871 (sensitivity = 0.902, specificity = 0.695) in the validation set, and was well calibrated, as indicated by the PHosmer-Lemeshow test values of 0.233 and 0.876 for the training and validation sets, respectively. The model included gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pa o2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

ObjectivesHigh blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of...

Prediction model for hypertension in first decade after pre-eclampsia in initially normotensive women.

To develop a prediction model for the development of hypertension in the decade following pre-eclampsia in women who were normotensive shortly after pregnancy. Thiswas a longitudinal cohort study of formerly pre-eclamptic women attending a university hospital in The Netherlands between 1996 and 2019. We developed a prediction model for incident hypertension using multivariable logistic regression analysis. The model was validated internally using bootstrapping techniques. Of 259 women, 185 (71%) were normotensive at the first cardiovascular assessment, at a median of 10 (interquartile range (IQR), 6-24) months after a pre-eclamptic pregnancy, of whom 49 (26%) had developed hypertension by the second visit, at a median of 11 (IQR, 6-14) years postpartum. The prediction model, based on birth-weight centile, mean arterial pressure, total cholesterol, left ventricular mass index and left ventricular ejection fraction, had good-to-excellent discriminative ability, with an area under the receiver-operating-characteristics curve (AUC) of 0.82 (95% CI, 0.75-0.89) and an optimism-corrected AUC of 0.80. The sensitivity and specificity of our model to predict hypertension were 98% and 34%, respectively, and positive and negative predictive values were 35% and 98%, respectively. Based on five variables, we developed a good-to-excellent predictive tool to identify incident hypertension following pre-eclampsia in women who were normotensive shortly after pregnancy. After external validation, this model could have considerable clinical utility in tackling the cardiovascular legacy of pre-eclampsia. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Hypertension prediction using machine learning algorithm among Indonesian adults

&lt;div&gt;&lt;div&gt;&lt;p&gt;Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;

Novel biomarkers to predict chronic kidney disease and hypertension at 3 and 12 months post-cisplatin.

e22003 Background: Urine kidney injury biomarkers measured during cisplatin therapy may help identify patients at risk for adverse long-term kidney outcomes. In children treated for cancer, we examined associations of urine tubular injury biomarkers collected during two cisplatin cycles, with chronic kidney disease (CKD) and hypertension at 3 and 12 months post-cisplatin. Methods: We analyzed data from the Applying Biomarkers to Minimize Long-Term Effects of Childhood/Adolescent Cancer Treatment (ABLE) Nephrotoxicity Study: a twelve-center prospective cohort of 159 children receiving cisplatin. Urine tubular injury biomarkers (by ELISA: neutrophil gelatinase-associated lipocalin [NGAL]; kidney injury molecule-1 [KIM-1]; tissue inhibitor of metalloproteinase-2 [TIMP-2]; insulin-like growth factor-binding protein-7 [IGFBP-7]) were measured at three timepoints (pre-infusion; 12-24 hours post-infusion; discharge after cisplatin cycle) during an early cisplatin cycle (EarlyCisP: first or second cycle of therapy) and a later cycle (LateCisP: last or second-to-last cycle). Area under the curve (AUC) for biomarkers to predict CKD (estimated glomerular filtration rate below normal or urine albumin/creatinine above normal for age, per international definition) and hypertension (3 blood pressures; per American Academy of Pediatrics guidelines) at 3 and 12 months post-cisplatin was calculated. AUC change from adding biomarkers to a published clinical prediction model for 3-month outcomes was evaluated (DeLong method). Results: 156 patients were available for analysis (mainly solid tumors). 3-month (median 90 days) outcomes were 52/118 (44%) CKD; 17/125 (14%) hypertension. 12-month (median 335 days) outcomes were 47/118 (40%) CKD; 15/125 (12%) hypertension. AUCs for all biomarkers to predict 3 and 12-month CKD and hypertension were poor to modest (best AUC = 0.70, 95% CI 0.60-0.80 for pre-infusion EarlyCisP TIMP-2*IGFBP-7 to predict 3-month hypertension). Biomarkers did not improve the clinical prediction model for 3-month CKD (AUC change not statistically significant with vs. without biomarkers). Adding pre-infusion EarlyCisP NGAL and TIMP-2*IGFBP-7 to the clinical prediction model for 3-month hypertension led to AUC change from 0.81 (0.72-0.91) to 0.89 (0.83-0.95) (p &lt; 0.05). Conclusions: Tubular injury biomarkers we studied were individually not strong predictors of 3 or 12-month post-cisplatin kidney outcomes. Adding biomarkers to existing clinical prediction models may help predict post-therapy hypertension development or identify higher kidney-risk patients.

Systematic review and meta-analysis protocol for development and validation of a prediction model for gestational hypertension in Africa.

Examining the development and validation of predictive models for gestational hypertension, evaluating the validity of the methodology, and investigating predictors typically employed in such models. Systematic review and meta-analysis protocol. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guideline will be used to carry out the study procedure. Using the key phrases "Gestational hypertension," "prediction, risk prediction," and "validation," a full systematic search will be conducted in PubMed/MEDLINE, Hinari, Cochrane Library, and Google Scholar. The methodological quality of the included studies will be evaluated using the prediction model risk of bias assessment tool. The CHARMS (checklist for critical evaluation and data extraction for systematic reviews of prediction modeling research) will be used to extract the data, and STATA 16 will be used to analyze it. The degree of study heterogeneity will be assessed using Cochrane I2 statistics. A subgroup analysis will be performed to reduce the variance between primary studies. To examine the impact of individual studies on the pooled estimates, a sensitivity analysis will be performed. The funnel plot test and Egger's statistical test will be used to assess the small study effect. The presence of a modest study effect is shown by Egger's test (p-value 0.05), which will be handled by nonparametric trim and fill analysis using the random-effects model. The protocol has been registered in the PROSPERO-International Prospective Register of systematic reviews, with the registration number CRD42022314601.

Association Between Ambulatory Blood Pressure and Risk of Home Hypertension in a Normotensive Population: The Ohasama Study.

We investigated the association between ambulatory blood pressure (BP) and the risk of home hypertension in a normotensive population and whether considering ambulatory BP improves the 10-year prediction model for home hypertension risk, which was developed in the previous Ohasama Study. In this prospective study, we followed up with 410 participants (83.2% women; age, 53.6 years) without a home and ambulatory hypertension in the general population of Ohasama, Japan. The Cox model was used to assess the hazard ratios (HRs) for home hypertension (home BP ≥ 135/≥85 mmHg or the initiation of antihypertensive treatment) and model improvement. During a mean 14.2-year follow-up, 225 home hypertension incidences occurred. The HR (95% confidence interval) for home hypertension incidence per 1-SD higher (=6.76 mmHg) 24-hour systolic BP (SBP) was 1.59 (1.33 to 1.90), after adjustments for possible confounding factors, including baseline home SBP. Harrell's C-statistics increased from 0.72 to 0.73 (P = 0.11) when 24-hour SBP was added to the basic 10-year home hypertension prediction model, which includes sex, age, body mass index, smoking status, office SBP, and baseline home SBP. Continuous net reclassification improvement (0.53, P < 0.0001) and integrated discrimination improvement (0.028, P = 0.0014) revealed improvement in the model. A total of 24-hour SBP could be an independent predictor of future home hypertension. Home BP and 24-hour BP can longitudinally influence each other in the long term.

Prediction model for hypertension after preeclampsia in initially normotensive women; the Queen of Hearts cohort

Abstract Background Timely detection and treatment of hypertension prevents cardiovascular diseases. Formerly preeclamptic women are at increased risk for hypertension, but targeted and structural follow-up of these women is lacking. Purpose To develop a prediction model for the development of hypertension in the decade following preeclampsia in women who were initially normotensive shortly after pregnancy. Methods In this longitudinal cohort study, formerly preeclamptic women were invited twice for a cardiovascular assessment. The first visit took place at a median interval of 10 months after delivery and a second visit 10 years later as part of the Queen of Hearts cohort study on early detection of heart failure among young women with a history of preeclampsia (ClinicalTrials.gov Identifier NCT02347540). Normotensive women at the first visit were divided in two groups whether or not they had developed hypertension during visit 2. Hypertension was defined as SBP ≥130 mmHg and/or DBP ≥80 mmhg and/or use of antihypertensive medication. We developed a prediction model using multivariable logistic regression analysis. The model was internally validated with bootstrapping techniques. Results Of the 259 women, 185 (71%) were normotensive at first visit of which 49 (26%) had developed hypertension at the second visit. The prediction model, based on birth weight centile, mean arterial pressure, total cholesterol, left ventricular mass index and left ventricular ejection fraction, had a good to excellent discriminative ability of AUC-ROC-curve 0.82 (95% CI 0.75–0.89) with an optimism corrected AUC of 0.80. Sensitivity and specificity of our model to predict yearly 10% risk to develop hypertension was 98% and 65% respectively and positive- and negative-predictive values were 50% and 99% respectively. Conclusions Based on five easily available variables, we developed a good-to-excellent performing predictive tool to identify incident hypertension following preeclampsia in women that were normotensive shortly after pregnancy. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): This work was partially supported by funding of de Nederlandse Hartstichting (Dutch Heart Foundation, grant number: 2013T084, Queen of Hearts study)

Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning

Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.

Prediction Model for Hypertension and Diabetes Mellitus Using Korean Public Health Examination Data (2002-2017).

Hypertension and diabetes mellitus are major chronic diseases that are important factors in the management of cardiovascular disease. In order to prevent the occurrence of chronic diseases, proper health management through periodic health check-ups is necessary. The purpose of this study is to determine the incidence of hypertension and diabetes mellitus according to the health check-up, and to develop a predictive model for hypertension and diabetes according to the health check-up. We used the National Health Insurance Corporation database of Korea and checked whether hypertension or diabetes occurred from that date according to the number of health check-ups over the past 10 years. Compared to those who underwent five health check-ups, those who participated in the first screening had hypertension (OR = 2.18, 95% CI = 2.14–2.22), diabetes mellitus (OR = 1.33, 95% CI = 1.30–1.35) and both diseases (OR = 2.46, 95% CI = 2.39–2.53); individuals who underwent 10 screenings had hypertension (OR = 0.86, 95% CI = 0.83–0.88), diabetes mellitus (OR = 0.83, 95% CI = 0.81–0.85) and both diseases (OR = 0.83, 95% CI = 0.79–0.87). Individuals who attended fewer than five screenings compared with individuals who attended five or more screenings had hypertension (OR = 1.61, 95% CI = 1.59–1.62; AUC = 0.66), diabetes mellitus (OR = 1.21, 95% CI = 1.20–1.22; AUC = 0.59) and both diseases (OR = 1.75, 95% CI = 1.72–1.78, AUC = 0.63). The machine learning-based prediction model using XGBoost showed higher performance in all datasets than the conventional logistic regression model in predicting hypertension (accuracy, 0.828 vs. 0.628; F1-score, 0.800 vs. 0.633; AUC, 828 vs. 0.630), diabetes mellitus (accuracy, 0.707 vs. 0.575; F1-score, 0.663 vs. 0.576; AUC, 0.710 vs. 0.575) and both diseases (accuracy, 0.950 vs. 0.612; F1-score, 0.950 vs. 0.614; AUC, 0.952 vs. 0.612). It was found that health check-up had a great influence on the occurrence of hypertension and diabetes, and screening frequency was more important than other factors in the variable importances.

Machine Learning-Based Prediction of Masked Hypertension Among Children With Chronic Kidney Disease.

Ambulatory blood pressure monitoring (ABPM) is routinely performed in children with chronic kidney disease to identify masked hypertension, a risk factor for accelerated chronic kidney disease progression. However, ABPM is burdensome, and developing an accurate prediction of masked hypertension may allow using ABPM selectively rather than routinely. To create a prediction model for masked hypertension using clinic blood pressure (BP) and other clinical characteristics, we analyzed 809 ABPM studies with nonhypertensive clinic BP among the participants of the Chronic Kidney Disease in Children study. Masked hypertension was identified in 170 (21.0%) observations. We created prediction models for masked hypertension via gradient boosting, random forests, and logistic regression using 109 candidate predictors and evaluated its performance using bootstrap validation. The models showed C statistics from 0.660 (95% CI, 0.595-0.707) to 0.732 (95% CI, 0.695-0.786) and Brier scores from 0.148 (95% CI, 0.141-0.154) to 0.167 (95% CI, 0.152-0.183). Using the possible thresholds identified from this model, we stratified the dataset by clinic systolic/diastolic BP percentiles. The prevalence of masked hypertension was the lowest (4.8%) when clinic systolic/diastolic BP were both <20th percentile, and relatively low (9.0%) with clinic systolic BP<20th and diastolic BP<80th percentiles. Above these thresholds, the prevalence was higher with no discernable pattern. ABPM could be used selectively in those with low clinic BP, for example, systolic BP<20th and diastolic BP<80th percentiles, although careful assessment is warranted as masked hypertension was not completely absent even in this subgroup. Above these clinic BP levels, routine ABPM remains recommended.

Body mass index is superior to other body adiposity indexes in predicting incident hypertension in a highly admixed sample after 10-year follow-up: The Baependi Heart Study.

Hypertension is the leading cause of overall mortality in low‐ and middle‐income countries. In Brazil, there is paucity of data on the determinants of incident hypertension and related risk factors. We aimed to determine the incidence of hypertension in a sample from the Brazilian population and investigate possible relationships with body adiposity indexes. We assessed risk factors associated with cardiovascular disease, including adiposity body indexes and biochemical analysis, in a sample from the Baependi Heart Study before and after a 10‐year follow‐up. Hypertension was defined by the presence of systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg or the use of antihypertensive drugs. From an initial sample of 1693 participants, 498 (56% women; mean age 38 ± 13 years) were eligible to be included. The overall hypertension incidence was 24.3% (22.3% in men and 25.6% in women). Persons who developed hypertension had higher prevalence of obesity, higher levels for blood pressure, higher frequency of dyslipidemia, and higher body adiposity indexes at baseline. The best prediction model for incident hypertension includes age, sex, HDL‐c, SBP, and Body Mass Index (BMI) [AUC = 0.823, OR = 1.58 (95% CI 1.23‐2.04)]. BMI was superior in its predictive capacity when compared to Body Adiposity Index (BAI), Body Roundness Index (BRI), and Visceral Adiposity Index (VAI). Incident hypertension in a sample from the Brazilian population was 24.3% after 10‐year follow‐up and BMI, albeit the simpler index to be calculated, is the best anthropometric index to predict incident hypertension.

PREDICTION OF GESTATIONAL DIABETES AND HYPERTENSION BASED ON PREGNANCY EXAMINATION DATA

Gestational diabetes mellitus and hypertension are two common pregnancy complications, which seriously threaten the life safety of pregnant women and adversely affect the growth and development of the fetus. Therefore, it is of great significance to detect and prevent hypertension and diabetes at an early stage of pregnancy. Each pregnant woman will undergo multiple tests at different gestational weeks. This progress produces lots of pregnancy examination data. These data can reflect the dynamic changes of pregnant women’s health indicators during pregnancy. This study aims to establish gestational diabetes and hypertension prediction model with a machine learning method based on real pregnancy examination data from the hospital. We use Logistic Regression, XGBoost, LightGBM, and Neural Network Model based on LSTM to do the prediction, respectively, and compare the performance. We check the prediction accuracy at different stages of pregnancy. We found that with pregnancy examination data at all gestational weeks, the predictive AUCs for diabetes and hypertension can reach 0.92 and 0.87, respectively. At 16th gestational week, the AUCs are 0.68 for diabetes and 0.70 for hypertension. We extract the checking items which are most important and get a simplified model with a modest reduction in predictive accuracy. This study demonstrates that based on several routine pregnancy examination items we can establish a machine learning model to detect and predict gestational diabetes and hypertension. This can be used as a diagnostic aid and is conducive to early prevention and treatment.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS).

The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies. The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model. The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population. This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

Development and Validation of a Prediction Model for Gestational Hypertension in Mezam Division

Objective: Our study aimed to develop and validate a prediction model for identifying women at increased risk of developing gestational hypertension (GH) in Mezam division, Northwest Region (NWR) of Cameroon.

Development and Validation of a Risk Score Prediction Model for Incident Hypertension among HIV Patients on Antiretroviral Therapy in Fako Division: A 5-Years Retrospective Cohort

A 5-years retrospective cohort study was used to review the records or patient files of 1640 HIV patients enrolled between the years 2013-2015. These files were reviewed in 3 HIV treatment Centers (HTCs) which measured a recorded blood pressure routinely.Data was entered in Excel 2010 and analyzed using stata 13. Log rank test and cox proportional hazard regression were used to identify independent risk factors.

Development of the Hypertension Index Model in General Adult Using the Korea National Health and Nutritional Examination Survey and the Korean Genome and Epidemiology Study.

Hypertension, a risk factor for cardiovascular disease and all-cause mortality, has been increasing. Along with emphasizing awareness and control of hypertension, predicting the incidence of hypertension is important. Several studies have previously reported prediction models of hypertension. However, among the previous models for predicting hypertension, few models reflect various risk factors for hypertension. We constructed a sex-specific prediction model using Korean datasets, which included socioeconomic status, medical history, lifestyle-related variables, anthropometric status, and laboratory indices. We utilized the data from the Korea National Health and Nutrition Examination Survey from 2011 to 2015 to derive a hypertension prediction model. Participants aged 40 years or older. We constructed a sex-specific hypertension classification model using logistic regression and features obtained by literature review and statistical analysis. We constructed a sex-specific hypertension classification model including approximately 20 variables. We estimated its performance using the Korea National Health and Nutrition Examination Survey dataset from 2016 to 2018 (AUC = 0.847 in men, AUC = 0.901 in women). The performance of our hypertension model was considered significant based on the cumulative incidence calculated from a longitudinal dataset, the Korean Genome and Epidemiology Study dataset. We developed this hypertension prediction model using features that could be collected in a clinical office without difficulty. Individualized results may alert a person at high risk to modify unhealthy lifestyles.

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.