24-h ambulatory blood pressure variability and hypertensive nephropathy in Han Chinese hypertensive patients

High versus low measurement frequency during 24-h ambulatory blood pressure monitoring - a randomized crossover study

Ambulatory blood pressure monitoring (ABPM) may be stressful and associated with discomfort, possibly influenced by the number of cuff inflations. We compared a low frequency (LF-ABPM) regimen with one cuff inflation per hour, with a high frequency (HF-ABPM) regimen performed according to current guidelines using three cuff-inflations per hour during daytime and two cuff-inflations during night time. In a crossover study, patients underwent ABPMs with both frequencies, in a randomized order, within an interval of a few days. Patients reported pain (visual analogue scale from 0 to 10) and sleep disturbances after each ABPM. The primary endpoint was the difference in mean 24 h systolic BP (SBP) between HF-ABPM and LF-ABPM. A total of 171 patients were randomized, and data from 131 (age 58 ± 14 years, 47% females, 24% normotensive, 53% mildly hypertensive, and 22% moderately-severely hypertensive) completing both ABPMs were included in the analysis. Mean SBP was 137.5 mmHg (95% CI, 134.8;140.2) for HF-ABPM and 138.2 mmHg (95%CI, 135.2;141.1) for LF-ABPM. The 95% limits of agreement were -15.3 mmHg and +14.0 mmHg. Mean 24 h SBP difference between HF-ABPM and LF-ABPM was -0.7 mmHg (95%CI, -2.0;0.6). Coefficients of variation were similar for LF-ABPM and HF-ABPM. Pain scores (median with interquartile range), for HF-ABPM and LF-ABPM were 1.5 (0.6;3.0) and 1.3 (0.6;2.9) during daytime, and 1.3 (0.4:3.4) and 0.9 (0.4;2.0) during nighttime (P < 0.05 for both differences). We conclude that LF-ABPM and HF-ABPM values are in good agreement without any clinically relevant differences in BP. Furthermore, LF-ABPM causes a relatively modest reduction in procedure-related pain.

Cumulative blood pressure load and hypertensive nephropathy in Han Chinese hypertensive patients

The study aims to assess the relationship between cumulative blood pressure load (cBPL) and the risk of renal function decline in hypertensive patients and determine the blood pressure (BP) threshold required to prevent hypertensive nephropathy. A single-center prospective cohort study was conducted on hypertensive patients. The cBPL was defined as the proportion of area beyond variable BP cutoffs under ambulatory BP monitoring. Renal events were defined as > 25% (minor) or > 50% (major) decline of baseline estimated glomerular filtration rate (eGFR). Cox regression analysis was conducted between cBPL, other ambulatory BP parameters, and renal events. The results revealed a total of 436 Han Chinese hypertensive patients were eligible for enrollment. During an average follow-up period of 5.1 ± 3.3 years, a decline of > 25% and > 50% in eGFR was observed in 77 and eight participants, respectively. Cox regression analysis revealed that cSBPL140 (hazard ratio [HR], 1.102; 95% confidence interval [CI], 1.017-1.193; p = .017), cSBPL130 (HR, 1.076; 95% CI, 1.019-1.137; p = .008), and cSBPL120 (HR, 1.054; 95% CI, 1.010-1.099; p = .015) were independently associated with minor renal events. Similarly, cSBPL140 (HR, 1.228; 95% CI, 1.037-1.455; p = .017), cSBPL130 (HR, 1.189; 95% CI, 1.045-1.354; p = .009), and cSBPL120 (HR, 1.155; 95% CI, 1.039-1.285; p = .008) were independently associated with major renal events. In conclusion, cBPL is associated with renal function decline in hypertensive patients. Minimizing cBPL120 may decrease the risk of hypertensive nephropathy.

Comparison of Ambulatory and Home Blood Pressure Variability for Cardiovascular Prognosis and Biomarkers

BACKGROUND

Although blood pressure variability (BPV) is reported to be associated with cardiovascular prognoses, it has not been established whether ambulatory BPV (ABPV; ie, short-term 24-hour BPV) or home BPV (HBPV; day-to-day BPV) is a superior clinical marker.

METHODS

We analyzed the associations of ABPV and HBPV with cardiovascular prognoses and biomarkers in 1314 hypertensive outpatients who underwent both home and ambulatory BP measurements in the J-HOP study (Japan Morning-Surge Home Blood Pressure). BPV was evaluated by the SD, coefficient of variation, and average real variability of the patients' 24-hour ambulatory and home systolic BP values.

RESULTS

During the median 7.0-year follow-up, 109 cardiovascular events occurred. All SD, coefficient of variation, and average real variability values of the HBPV were significantly associated with cardiovascular risk even after adjusting by average 24-hour ambulatory systolic BP and each ABPV value: 1 SD of hazard ratio (95% CI) for the SD, 1.36 (1.14-1.63); coefficient of variation, 1.38 (1.16-1.66); and average real variability 1.29 (1.10-1.51) of HBPV. The ABPV parameters did not exhibit comparable relationships. The cardiovascular risk spline curves showed a trend toward increased risks with increasing HBPV parameters. There were no differences between ABPV and HBPV in the relationships with B-type natriuretic peptide and the urine albumin-creatine ratio.

CONCLUSIONS

In this comparative analysis of ambulatory and home BP monitoring values in individuals with hypertension, ABPV was not significantly associated with cardiovascular prognosis adjusted by average BP level, and HBPV was suggested to have modest superiority in predicting cardiovascular prognosis compared with ABPV.

Short- to long-term blood pressure variability: Current evidence and new evaluations

Increased blood pressure (BP) variability and the BP surge have been reported to be associated with increased cardiovascular risk independently of BP levels and can also be a trigger of cardiovascular events. There are multiple types of BP variation: beat-to-beat variations related to breathing and the autonomic nervous system, diurnal BP variation and nocturnal dipping related to sleep and physical activity over a 24-hr period, day-to-day BP variability with anomalous readings within a several-day period, visit-to-visit BP variability between outpatient visits, and seasonal variations. BP variability is also associated with the progression to hypertension from prehypertension and the progression of chronic kidney disease and cognitive impairments. Our research group proposed the "resonance hypothesis of blood pressure surge" as a new etiological hypothesis of BP variability and surges; i.e., the concept that when the time phases of surges and hypertension-inducing environmental influences coincide, resonance occurs and is amplified into a larger "dynamic surge" that triggers the onset of cardiovascular disease. New devices to assess BP variability as well as new therapeutic interventions to reduce BP variability are being developed. Although there are still issues to be addressed (including measurement accuracy), cuffless devices and information and communication technology (ICT)-based BP monitoring devices have been developed and validated. These new devices will be useful for the individualized optimal management of BP. However, evidence regarding the usefulness of therapeutic interventions to control BP variability is still lacking.

Prediction of white-coat hypertension and white-coat uncontrolled hypertension using machine learning algorithm

Aims

The detection of white-coat hypertension/white-coat uncontrolled hypertension (WCH/WUCH) with out-of-office blood pressure (BP) monitoring is time- and resource-consuming. We aim to develop a machine learning (ML)-derived prediction model based on the characteristics of patients from a single outpatient visit.

Methods and results

Data from two cohorts in Taiwan were used. Cohort one (970 patients) was used for development and internal validation, and cohort two (464 patients) was used for external validation. WCH/WUCH was defined as an office BP of ≥140/90 mmHg and daytime ambulatory BP of <135/85 mmHg in treatment-naïve or treated individuals. Logistic regression, random forest (RF), eXtreme Gradient Boosting, and artificial neural network models were trained using 26 patient parameters. We used SHapley Additive exPlanations values to provide explanations for the risk factors. All models achieved great area under the receiver operating characteristic curve (AUROC), specificity, and negative predictive value in both validations (AUROC = 0.754-0.891; specificity = 0.682-0.910; negative predictive value = 0.831-0.968). The RF model was the best performing (AUROC = 0.884; sensitivity = 0.619; specificity = 0.887; negative predictive value = 0.872; accuracy = 0.819). The five most influential features of the RF model were office diastolic BP, office systolic BP, current smoker, estimated glomerular filtration rate, and fasting glucose level.

Conclusion

Our prediction models achieved good performance, underlining the feasibility of applying ML models to outpatient populations for the diagnosis of WCH and WUCH. Further validation with other prospective data sets should be considered in the future.

Short-term blood pressure variability and outcomes in non-dialysis chronic kidney disease

Background

Blood pressure variability (BPV) is associated with cardiovascular and all-cause mortality, and has been demonstrated in dialysis patients, but has been poorly studied and remains controversial in non-dialysis chronic kidney disease (CKD) patients. We investigated the effect of short-term BPV on prognosis in this population.

Methods

A total of 245 stage 1–4 CKD patients with 24-h ambulatory blood pressure recordings were recruited. BPV was evaluated by standard deviation, coefficient of variation, and variation independent of the mean, respectively. All subjects were followed up to the composite end-point event or until January 15, 2020. Patients were divided into two groups based on 24-h median variation independent of the mean, and demographics, laboratory indicators and echocardiogram results were compared. Logistic regression was used to analyze the risk factors for increased BPV. Multivariate Cox regression and Kaplan-Meier survival analysis were used to explore the relationship between BPV and renal prognosis and major cardiovascular events.

Results

The mean age was 42.07 ± 12.66 years, with 141 males (57.55%). Multivariate Logistic regression analysis showed that high BMI (OR 1.110, P = 0.017), hyperkalemia (OR 2.227, P = 0.040), increased left ventricular end-diastolic diameter (OR 1.103, P = 0.010) and hypertension (OR 2.525, P = 0.002) were independent risk factors for high BPV. Kaplan-Meier survival analysis showed that renal and cardiovascular outcomes were better in the low BPV group than in the high BPV group (P = 0.006; P = 0.002). After adjusting for age, sex and traditional kidney related risk factors, BPV were not independently associated with renal outcomes. High BPV (HR 4.662, P = 0.017) was the main independent risk factor for major cardiovascular events in CKD.

Conclusions

In non-dialysis CKD, short-term BPV was associated with major cardiovascular disease but not renal progression. BMI, hypertension, potassium balance, and left ventricular end-diastolic diameter influenced short-term BPV.

Relations between white coat effect of blood pressure and arterial stiffness

The aim of this study was to analyze the relationship between brachial-ankle pulse wave velocity (b-a PWV) and white coat effect (WCE), that is the difference between the elevated office blood pressure (BP) and the lower mean daytime pressure of ambulatory BP, in a mixed population of normotention, untreated sustained hypertension, sustained controlled hypertension, sustained uncontrolled hypertension, white coat hypertension, white coat uncontrolled hypertension. A total of 444 patients with WCE for systolic BP (54.1% female, age 61.86 ± 13.3 years) were enrolled in the study. Patients were separated into low WCE (<9.5 mm Hg) and high WCE (≥9.5 mm Hg) according to the median of WCE. The subjects with a high WCE showed a greater degree of arterial stiffness than those with a low WCE for systolic BP values (P < .05). The b-a PWV were 17.2 ± 3.3 m/s and 18.4 ± 3.4 m/s in low WCE and high WCE, respectively. The b-a PWV increased with the increase of WCE, showing a positive correlation between them (P > .05 for non-linearity). The significant association between the high WCE and the b-a PWV was confirmed by the results of multiple regression analysis after adjusting for confounding factors (β = .78, 95% Cl .25-1.31, P = . 004). Similar results were observed in subgroups. In conclusion, WCE is significantly associated with arterial stiffness. More research is needed to determine the WCE and target organ damage.

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.