2021 European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement

Stergiou, George S.; Palatini, Paolo; Parati, Gianfranco; O’Brien, Eoin; Januszewicz, Andrzej; Lurbe, Empar; Persu, Alexandre; Mancia, Giuseppe; Kreutz, Reinhold

doi:10.1097/hjh.0000000000002843

Cited by 502 publications

(669 citation statements)

References 18 publications

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“…Currently, out-of-office BP monitoring, either ABPM or HBPM, is the gold standard to diagnose these patients (4-7). However, the use of out-of-office BP monitoring is usually limited for many reasons, such as the shortage of resources, great consumption of time, poor compliance, and poor adherence of patients (31,32). It is important to find a more efficient way to identify this particular patient group.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Hung

Shih

Wang

et al. 2021

Front. Cardiovasc. Med.

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Objective: This study aimed to develop machine learning-based prediction models to predict masked hypertension and masked uncontrolled hypertension using the clinical characteristics of patients at a single outpatient visit.Methods: Data were derived from two cohorts in Taiwan. The first cohort included 970 hypertensive patients recruited from six medical centers between 2004 and 2005, which were split into a training set (n = 679), a validation set (n = 146), and a test set (n = 145) for model development and internal validation. The second cohort included 416 hypertensive patients recruited from a single medical center between 2012 and 2020, which was used for external validation. We used 33 clinical characteristics as candidate variables to develop models based on logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost), and artificial neural network (ANN).Results: The four models featured high sensitivity and high negative predictive value (NPV) in internal validation (sensitivity = 0.914–1.000; NPV = 0.853–1.000) and external validation (sensitivity = 0.950–1.000; NPV = 0.875–1.000). The RF, XGboost, and ANN models showed much higher area under the receiver operating characteristic curve (AUC) (0.799–0.851 in internal validation, 0.672–0.837 in external validation) than the LR model. Among the models, the RF model, composed of 6 predictor variables, had the best overall performance in both internal and external validation (AUC = 0.851 and 0.837; sensitivity = 1.000 and 1.000; specificity = 0.609 and 0.580; NPV = 1.000 and 1.000; accuracy = 0.766 and 0.721, respectively).Conclusion: An effective machine learning-based predictive model that requires data from a single clinic visit may help to identify masked hypertension and masked uncontrolled hypertension.

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Section: Discussionmentioning

confidence: 99%

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Hung

Shih

Wang

et al. 2021

Front. Cardiovasc. Med.

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“…Arterial hypertension is defined by values of systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg according to the European Society of Hypertension guideline in 2021 [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Biomarkers of Uremic Cardiotoxicity

Stopić

Dragičević

Medić

et al. 2021

Toxins

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Cardiovascular (CV) morbidity and mortality increase along with the progression of chronic kidney disease (CKD). The potential novel biomarkers of cardiotoxicity have been tested with the aim of the early detection of patients at high CV risk, and among them are markers of inflammation, oxidative stress, acute renal injury, and microRNAs. The study analyzed biomarkers in non-dialysis-dependent (NDD; stage 3a–4 CKD) and dialysis-dependent (DD) CKD patients. The prospective cohort study included 87 patients who were followed for 18 months, during which period newly occurred CV events were recorded. Cox regression analysis confirmed serum albumin, urea, interventricular septum thickness diameter (IVST), the use of calcium antagonist, and erythropoiesis-stimulating agent to be significant predictors of CV outcome. No significant difference was observed in biomarkers of inflammation, oxidative stress, acute kidney injury (IL-18, CRP, ferritin, IMA, SOD, NGAL, and KIM-1), and miR-133a, in regards to the presence/absence of CV event, CV death, and left ventricular hypertrophy. Serum albumin, urea, IVST, and the use of calcium antagonist and erythropoiesis-stimulating agents were confirmed to be factors associated with CV events in CKD patients. Apart from traditional risk factors, new research is needed to define novel and reliable biomarkers of cardiotoxicity in CKD patients.

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“…Blood pressure was measured using a mercury sphygmomanometer (Accoson brand, made in England). This was done after the patient had sit for about 5 minutes and a standard technique, as described by the European Society of Hypertension, was strictly adhered to [38]. Blood pressure consisted of systolic blood pressure (SBP) and diastolic blood pressure (DBP).…”

Section: Methodsmentioning

confidence: 99%

Anthropometric indices as predictors of 10-year cardiovascular risk among Sub-Sahara Africans with type 2 diabetes

Azeez¹

2021

J Cardiovasc Med Cardiol

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Background: Body mass index, waist circumference, waist-hip ratio and waist-height ratio are simple clinical tools for determining obesity. Type 2 diabetes mellitus is often associated with multiple cardiovascular risk factors and increased cardiovascular death. The study was aimed at determining the relationship between these anthropometric indices and 10-year cardiovascular risk among sub-saharan Africans with type 2 diabetes mellitus. Methods: It was a cross-sectional study involving 67 adults (with 50.7% females) managed for type 2 diabetes mellitus in a referral hospital in Nigeria. Ethical approval was obtained at the institution review board and the participants also gave written consent. Anthropometric indices were determined using standard protocols. Fasting lipid profile, fasting plasma glucose, glycated haemoglobin and plasma creatinine were assayed using standard laboratory techniques. Atherogenic index of plasma, estimated glomerular filtration rate and the WHO-ISH cardiovascular risk score were also determined. Data was analyzed with SPSS version 22. Pearson correlation coefficient, Students’ t test, Chi square test, ROC curve analysis were performed as appropriate. Results: The mean age was 54.12±9.03 years. Obesity was found in 37.3%, 66.5%, 70.1% and 95.5% of the participants using BMI, WHR, WC and WHtR respectively. Intermediate/high cardiovascular risk was found in 38.2% and 24.2% of the males and females respectively. BMI and WC significantly correlated with blood pressure. There was no significant correlation between anthropometric indices and other cardiovascular risk factors studied. Using ROC curve analysis, BMI and WHtR had the highest AUC of 0.613 and 0.577 respectively. Conclusion: Among sub-sahara Africans with type 2 diabetes mellitus, there is a significant association between WC and BMI with the blood pressure. BMI and WHtR have the highest 10-year cardiovascular risk predictability among the anthropometric indices in this cohort of individuals. Larger and prospective studies are needed to validate these findings.

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2021 European Society of Hypertension practice guidelines for office and out-of-office blood pressure measurement

Cited by 502 publications

References 18 publications

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Biomarkers of Uremic Cardiotoxicity

Anthropometric indices as predictors of 10-year cardiovascular risk among Sub-Sahara Africans with type 2 diabetes

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