Global Burden of Hypertension

Mills, Katherine T.; Bundy, Joshua D; Kelly, Tanika N.; Reed, John P.; Kearney, Patricia; Reynolds, Kristi; Jing, Chen; He, Jie

doi:10.1097/01.hjh.0000469726.59998.cc

Cited by 36 publications

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“…Hypertension is the leading preventable cause of premature death worldwide. According to Mills et al, in 2010 there were approximately 1.33 billion people with hypertension worldwide, accounting for 19.3% of the world's total population [1]. According to Kearney et al, by 2025 global hypertension patients will reach 1.56 billion [2].…”

Section: Introductionmentioning

confidence: 99%

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A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

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Section: Introductionmentioning

confidence: 99%

“…Diagnostics 2019, 9, 178 6 of 21 In this paper, there are several innovations in comparison with previous predictions of hypertension complications: (1) The study is aimed at the outcomes of hypertension, and the outcomes are complications that pose a major threat to human life. There has been no previous research on this subject.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

et al. 2019

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“…Systemic arterial hypertension (SAH) is a leading cause of cardiovascular diseases (CVD) reaching the prevalence of 30% in the world population [1]. As a multifactorial condition, to tackle this disease not only the environmental factors but also the emerging weight of genetic factors need to be considered for years to come, with application on genetic risk scores development [2], once genetics can be responsible for about 40% of the interindividual variance in blood pressure levels [3].…”

Section: Introductionmentioning

confidence: 99%

The combination of ACE I/D and ACE2 G8790A polymorphisms revels susceptibility to hypertension: A genetic association study in Brazilian patients

et al. 2019

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Background Systemic arterial hypertension (SAH) is a multifactorial condition that already affects one third of the worldwide population. The identification of candidate genes for hypertension is a challenge for the next years. Nevertheless, the small contribution of each individual genetic factor to the disease brings the necessity of evaluate genes in an integrative manner and taking into consideration the physiological interaction of functions. Angiotensin I–converting enzymes, ACE and ACE2, are key regulators of blood pressure that have counterbalance roles by acting on vasoactive peptides from Renin-Angiotensin-Aldosterone System (RAAS). Insertion/deletion (I/D) polymorphism of ACE gene and single nucleotide polymorphism G8790A of ACE2 gene have been associated with susceptibility to SAH, but the literature is controversial. We proposed to evaluate these two polymorphisms jointly exploring the combined effects of ACE and ACE2 genotypes on SAH susceptibility, an approach that have not been done yet for ACE and ACE2 polymorphisms. Methods and findings This genetic association study included 117 hypertensive (mean age 59.7 years) patients and 123 normotensive and diabetes-free controls (mean age 57.5 years). ACE and ACE2 polymorphisms were genotyped by SYBR Green real-time PCR and RFLP-PCR, respectively. Crude and adjusted odds ratio (OR) values were calculated to estimate the susceptibility to SAH development. It was obtained homogeneity regarding distribution by sex, age range, smoking, alcohol consumption and body mass index (BMI) between case and control groups. No-association was verified for each gene individually, but the combination of ACE and ACE2 polymorphisms on female gender revealed a significative association for DD/G_ carriers who had a 3-fold increased risk to SAH development (p = 0.03), with a stronger susceptibility on DD/GG carriers (7-fold increased risk, p = 0.01). The D allele of ACE showed association with altered levels of lipid profile variables on case group (VLDL-cholesterol, p = 0.01) and DD genotype in all individuals analysis (triglycerides, p = 0.01 and VLDL-cholesterol, p = 0.01). Conclusion These findings indicate that the combination of ACE and ACE2 polymorphisms effects may play a role in SAH predisposition been the DD/G_ genotype the susceptibility profile. This result allowed us to raise the hypothesis that an increased activity of ACE (prohypertensive effects) in conjunction with reduced ACE2 activity (antihypertensive effects) could be the underlining mechanism. The association of ACE D allele with lipid alterations indicate t...

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“…1,2 In 2010, 1.3 billion people in the world were suffering from hypertension, with an annual increase rate of 2.5%. 3 According to the World Health Organization (WHO) report published in 2013, the global prevalence of hypertension was 40%. 4 Annually, 9.4 million people die from either hypertension or its related complications worldwide.…”

Section: Introductionmentioning

confidence: 99%

<p>Non-Adherence to Prescribed Antihypertensives in Primary, Secondary and Tertiary Healthcare Settings in Islamabad, Pakistan: A Cross-Sectional Study</p>

Mahmood¹,

Jalal²,

Hadi³

et al. 2020

PPA

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Objective: This study aimed to assess the prevalence and predictors of non-adherence to antihypertensive medication among patients with hypertension attending various healthcare settings in Islamabad, Pakistan. Methods: A questionnaire-based cross-sectional study was conducted in selected healthcare facilities between September 2017 and December 2018. The study was conducted in primary, secondary and tertiary healthcare settings in Islamabad, Pakistan. Medication adherence was assessed using the eight-item Morisky Medication Adherence Scale (MMAS-8). Sociodemographic and clinical data of recruited patients were collected through a structured questionnaire. Binary logistic regression analysis was performed to determine covariates significantly associated with medication adherence and blood pressure control. Results: A total of 776 hypertensive patients were invited and 741 (95%) completed the questionnaire. The mean ± SD age of participants was 53.6±12.6 years; 284 patients (38.3%) had high adherence, 178 (24%) had moderate adherence and 279 (37.7%) were non-adherent to the prescribed antihypertensive therapy. Binary regression analysis revealed that old age (OR 1.783 [95% CI: 1.172-2.712]; P=0.013), being educated (OR 2.018 [95% CI: 1.240-3.284]; P=0.036), entitlement to free medical care (OR 1.369 [95% CI: 1.009-1.859]; P=0.044), treatment duration (OR 2.868 [95% CI: 1.913-4.299]; P=0.001), number of medications (OR 1.973 [95% CI: 1.560-2.495]; P=0.001), presence of any comorbidity (OR 2.658 [95% CI: 1.836-3.848]; P=0.001) and blood pressure control (OR 3.103 [95% CI: 2.268-4.247]; P=0.001) were significantly associated with good adherence. Similarly, age (OR 1.998 [95% CI: 1.313-3.040]; P=0.004), entitlement to free medical care (OR 1.498 [95% CI: 1.116-2.010]; P=0.007), treatment duration (OR 1.886 [95% CI: 1.143-3.113]; P=0.013), presence of any comorbidity (OR 1.552 [95% CI: 1.123-2.147]; P=0.008) and adherence level ; P=0.001) had significant association with controlled blood pressure. The following were the main reasons for non-adherence to prescribed antihypertensive medication: "don't feel need for regular use" (24.7%), "Carelessness" (13.4%) and "adverse effects" (11.2%). Conclusion: The prevalence of non-adherence to antihypertensive medications was high in the study population and poor medication adherence could potentially explain poor blood pressure control. Evidence-based targeted interventions on both medication adherence and blood pressure control should be introduced and implemented for better treatment outcomes.

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Global Burden of Hypertension

Cited by 36 publications

References 0 publications

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

The combination of ACE I/D and ACE2 G8790A polymorphisms revels susceptibility to hypertension: A genetic association study in Brazilian patients

<p>Non-Adherence to Prescribed Antihypertensives in Primary, Secondary and Tertiary Healthcare Settings in Islamabad, Pakistan: A Cross-Sectional Study</p>

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