Contribution of single office aortic systolic blood pressure measurements to the detection of masked hypertension: data from two separate cohorts

Diagnostic agreement of masked uncontrolled hypertension detected by ambulatory blood pressure and home blood pressure measured by an all-in-one BP monitoring device: The HI-JAMP study

Masked hypertension is defined by office blood pressure (BP) in the controlled-BP range while out-of-office BP measured by ambulatory BP monitoring (ABPM) and home BP monitoring (HBPM) is in the uncontrolled range. However, diagnosis of masked hypertension may differ if assessed by different out-of-office BP indices. This study aims to investigate the diagnostic agreement of masked uncontrolled hypertension (MUHT) detected by ABPM indices (ABPM-MUHT) and HBPM indices (HBPM-MUHT) using the same all-in-one device (TM2441; A&D Company). The present study enrolled a total of 2322 treated hypertensive patients (males 53.2%, average age 69.2 ± 11.5 years) from the Home-Activity ICT-based Japan Ambulatory Blood Pressure Monitoring Prospective (HI-JAMP) Study, who consecutively underwent office BP monitoring, 24-h ABPM (at 30-min intervals), and 5-day HBPM (twice each morning and evening) using the same device. When out-of-office BP control status was assessed only by 24-h average SBP or by the average of morning and evening SBP, the diagnostic agreement of MUHT detected by ABPM and HBPM was 29.7% among the 445 patients with any type of MUHT. When out-of-office BP indices in each time-window were simultaneously assessed, the diagnostic agreement increased to 40-45.7%. Our results indicated the importance of assessing BPs at various times of day, especially morning hours, for perfect hypertension management. Diagnosis of masked hypertension only by an averaged BP index, without considering specific time-windows, might underestimate cardiovascular risk.

Nomogram based on clinical features at a single outpatient visit to predict masked hypertension and masked uncontrolled hypertension: A study of diagnostic accuracy

Patients with masked hypertension (MH) and masked uncontrolled hypertension (MUCH) are easily overlooked, and both cause target organ damage. We propose a prediction model for MH and MUCH patients based on clinical features at a single outpatient visit. Data collection was planned before the index test and reference standard were after. Thus, we retrospectively collect analyzed 804 subjects who underwent ambulatory blood pressure monitoring (ABPM) at Renmin Hospital of Wuhan University. These patients were divided into normotension/controlled hypertension group (n = 121), MH/MUCH (n = 347), and sustained hypertension (SH)/sustained uncontrolled hypertension group (SUCH) (n = 302) for baseline characteristic analysis. Models were constructed by logistic regression, a nomogram was visualized, and internal validation by bootstrapping. All groups were performed according to the definition proposed by the Chinese Hypertension Association. Compared with normotension/controlled hypertension, patients with MH/MUCH had higher office blood pressure (BP) and were more likely to have poor liver and kidney function, metabolic disorder and myocardial damage. By analysis, [office systolic blood pressure (OSBP)] (P = .004) and [office diastolic blood pressure (ODBP)] (P = .007) were independent predictors of MH and MUCH. By logistic regression backward stepping method, office BP, body mass index (BMI), total cholesterol (Tch), high-density lipoprotein cholesterol (HDL-C), and left ventricular mass index are contained in this model [area under curve (AUC) = 0.755] and its mean absolute error is 0.015. Therefore, the prediction model established by the clinical characteristics or relevant data obtained from a single outpatient clinic can accurately predict MH and MUCH.

Prospects for developing and implementing remote blood pressure monitoring in patients under dispensary follow-up

Recently, the use of telemedicine technologies (TMT) in the healthcare has gained great importance. TMT is one of the ways to increase the healthcare availability, including in patients with high blood pressure (BP). Office BP measurement and 24-hour BP monitoring are not accurate enough to study natural or induced BP changes over long periods of time. For the selection of antihypertensive drugs and the diagnosis of hypertension (HTN) in patients with an emotionally unstable personality type, as well as in the differential diagnosis of normotension, preHTN, BP selfmonitoring comes first. The use of BP self-monitoring for the diagnosis, selection of therapy, assessment of adherence and effectiveness of treatment of HTN is more effective with remote, socalled telemetric, dynamic BP monitoring. The article presents world experience in the effective use of dynamic remote BP monitoring using TMT.

 

Annual reports on hypertension research 2020

In 2020, 199 papers were published in Hypertension Research. Many excellent papers have contributed to progress in research on hypertension. Here, our editorial members have summarized eleven topics from published work and discussed current topics in depth. We hope you enjoy our special feature, Annual Reports on Hypertension Research.

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

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.