Simple predictive score for nocturnal hypertension and masked nocturnal hypertension using home blood pressure monitoring in clinical practice

Sleep and hypertension - up to date 2024

People spend one-third of their lives sleeping, and adequate, restful sleep is an essential component of a healthy life. Conversely, disruption of sleep has been found to cause various physical and mental health problems. Emerging research has shown that blood pressure (BP) during sleep is a stronger predictor of cardiovascular events than conventional office BP or daytime BP. Thus, management of both sleep health and nighttime BP during sleep is important for preventing cardiovascular events. However, recent studies demonstrated that nighttime BP is poorly controlled compared with office BP and daytime BP. This finding is understandable, given the challenges in monitoring BP during sleep and the multiplicity of factors related to nocturnal hypertension and BP variability. This review summarizes recent evidence and considers future perspectives for the management of sleep and hypertension.

Antihypertensive effect of esaxerenone and correlation between brachial and wrist home monitoring devices in patients with nocturnal hypertension: A post hoc analysis of the EARLY-NH study

Adequate management of nocturnal hypertension is crucial to reduce the risk of organ damage and cardiovascular events. The EARLY-NH study was a prospective, open-label, multicenter study conducted in Japanese patients with nocturnal hypertension who received esaxerenone treatment for 12 weeks. This post hoc analysis aimed to assess (1) the relationship between changes in morning home systolic blood pressure (SBP), bedtime home SBP, and nighttime home SBP based on changes in SBP and achievement rates of target SBP levels; and (2) the correlation between nighttime home SBP measurements using brachial and wrist home BP monitoring (HBPM) devices. This analysis evaluated 82 patients who completed the 12-week treatment period. Among those who achieved target morning home SBP (<135 mmHg) and target bedtime home SBP (<135 mmHg), the brachial HBPM device showed achievement rates of 63.6% and 56.4%, respectively, for target nighttime home SBP (<120 mmHg). The wrist device showed achievement rates of 66.7% and 63.4%, respectively, for the same targets. Significant correlations were observed between both devices for nighttime home SBP measurements at baseline (r = 0.790), Week 12 (r = 0.641), and change from baseline to Week 12 (r = 0.533) (all, p < .001). In this patient population, approximately 60% of individuals who reached target morning or bedtime home SBP levels <135 mmHg exhibited well-controlled nighttime home SBP. Although nighttime home SBP measurements obtained using both brachial and wrist HBPM devices displayed a significant correlation, the wrist device needs to be examined in more detail for clinical use.

Recent developments in machine learning modeling methods for hypertension treatment

Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.

Controversies in Hypertension III: Dipping, Nocturnal Hypertension, and the Morning Surge

A comprehensive approach to hypertension requires out-of-office determinations by home and/or ambulatory monitoring. The 4 phenotypes comparing office and out-of-office pressures in treated and untreated patients include normotension, hypertension, white-coat phenomena, and masked phenomena. Components of out-of-office pressure may be equally as important as mean values. Nighttime pressures are normally 10 - 20% lower than daytime (normal "dipping"). Abnormalities include dipping more than 20% (extreme dippers), less than 10 % (non-dippers), or rising above daytime (risers) and have been associated with elevated cardiovascular risk. Nighttime pressure may be elevated (nocturnal hypertension) in isolation or together with daytime hypertension. Isolated nocturnal hypertension theoretically changes white-coat hypertension to true hypertension and normotension to masked hypertension. Pressure normally peaks in the morning hours ("morning surge") when cardiovascular events are most common. Morning hypertension may result from residual nocturnal hypertension or an exaggerated surge and has been associated with enhanced cardiovascular risk, especially in Asian populations. Randomized trials are needed to determine whether altering therapy based solely on either abnormal dipping, isolated nocturnal hypertension, and/or an abnormal surge is justified.

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.

The role of blood pressure management in stroke prevention: current status and future prospects

INTRODUCTION

Stroke is the second-leading cause of death worldwide and the second-leading cause of disability-adjusted life-years. It is well known that hypertension is a significant risk factor for cardiovascular events, including stroke.

AREAS COVERED

Recent interventional trials have demonstrated the superiority of intensive blood pressure (BP) control for prevention of cardiovascular events compared to standard BP control. Notably, in the Strategy of Blood Pressure Intervention in Elderly Hypertensive Patients (STEP) trial, intensive BP control showed superiority in the prevention of stroke events in elderly hypertensive patients. Novel medications such as angiotensin receptor-neprilysin inhibitors and sodium glucose cotransporter 2 inhibitors have the potential to suppress various CVD events including stroke. Non-pharmacological antihypertensive therapies such as renal denervation have demonstrated BP-lowering effects and may be useful for stroke prevention. Additionally, new methods and systems of BP monitoring including various kinds of nighttime BP measurement devices, wearable devices, and methods using information and communication technology can be used to assess the pathophysiology of BP variability as a risk factor and an event trigger of stroke incidence.

EXPERT OPINION

Novel therapies and new technologies for BP evaluation strongly support the development of individualized anticipatory medicine, which should be useful for the prevention of stroke.

Seasonal Variation in Day-by-Day Home Blood Pressure Variability and Effect on Cardiovascular Disease Incidence

BACKGROUND

Although day-by-day home blood pressure (BP) variability (BPV) has been associated with cardiovascular disease (CVD) risk, it remains unclear whether this association differs from season to season. The present study aimed to assess seasonal variation in day-by-day home BP variability and its association with CVD risk.

METHODS

We analyzed the data from a nationwide, prospective observational study, the J-HOP study (Japan Morning Surge-Home Blood Pressure), in which 14 consecutive days of home BP monitoring were conducted. The values of SD (SD_{systolic BP [SBP]}), coefficient of variation_SBP, and average real variability_SBP of home SBP were used as indices of day-by-day home BPV.

RESULTS

Among 4231 participants (mean age, 64.9±10.9 years, 46.7% male, 91.5% hypertensives), all 3 day-by-day home BPV indices were lower in summer than winter after adjusting for confounding factors. In winter, SD_SBP, coefficient of variation_SBP, and average real variability_SBP were significantly associated with increased risk of CVD events (coronary artery disease, stroke, heart failure, and aortic dissection; adjusted hazard ratio [95%CI] per 1-SD of SD_SBP, 1.26 [1.02-1.54]; coefficient of variation_SBP, 1.24 [1.02-1.52]; average real variability_SBP, 1.44 [1.17-1.77]). These relationships were also observed in the analysis of quartiles of BPV parameters (adjusted hazard ratio [95%CI] compared to the first quartile, fourth quartile of SD_SBP 2.26 [1.06-4.85]; coefficient of variation_SBP 2.96 [1.43-6.15]; average real variability_SBP 2.73 [1.25-5.93]). In other seasons, however, there were no significant associations between day-by-day home BPV and CVD event risk.

CONCLUSIONS

Our findings indicate that day-by-day home BPV measured in winter is more strongly associated with future CVD incidence than that measured in other seasons.