- Research
- Open Access
Evaluation of the applicability of deep breathing test in the diagnosis of hypertension with white-coat effect in Chinese patients in primary care
- Kam Sum Chan1Email authorView ORCID ID profile,
- Kit Ping Loretta Lai1,
- Pang Fai Chan1,
- Man Hei Matthew Luk1 and
- Vai Kiong David Chao1
- Received: 1 October 2018
- Accepted: 19 December 2018
- Published: 1 February 2019
Abstract
Purpose
The current gold standard for the diagnosis of white-coat effect is by the 24-h ambulatory blood pressure monitoring (ABPM) which may not be readily available in every primary care setting. Previous studies had shown that deep breathing, through modulating the baroreceptor reflex sensitivity to vagal stimulation over 30 to 60 s, was useful in detection of the white-coat effect. The aim of our study was to evaluate the diagnostic accuracy of the deep breathing test (DBT) as compared with the gold standard of ABPM in the diagnosis of hypertension with white-coat effect in Chinese patients in primary care.
Methods
This cross sectional study recruited 178 consecutive, eligible, consented, hypertensive patients receiving treatment at a local public primary care Hypertension Clinic.
The diagnostic accuracy of the DBT in all recruited patients, patients not taking beta-adrenergic blockers and patients with different clinic SBP cut-off before the DBT by means of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive and negative predictive values was evaluated.
Results
The results for the ROC curves for systolic and diastolic BP changes after the DBT were statistically insignificant. The ROC curve was statistically significant for SBP change in patients not taking beta-adrenergic blockers and with pre-DBT clinic SBP ≥ 165 mmHg (ROC curve area of 0.719, 95% CI 0.53 to 0.91, p = 0.04). The corresponding sensitivity and specificity of the DBT were 40.9 and 90.9% respectively if SBP drop was > 30 mmHg.
Conclusion
The DBT, even though could not be clinically applied to all patients, was proven to be a potential screening and diagnostic test for white-coat effect in Chinese hypertensive patients with a pre-test SBP of ≥165 mmHg and who were not taking beta-adrenergic blockers.
Trial registration
This study was approved by Kowloon East Cluster/ Kowloon Central Cluster Research Ethics Committee/Institutional Review Board of Hospital Authority of Hong Kong under the registration KC/KE-16-0084/ER-3.
Keywords
- White-coat effect
- Deep breathing test
- Hypertension
- Primary care
Introduction
Hypertension, being a major constituent to the global burden of non-communicable diseases [1], was reported to be prevalent in around 32% of the Hong Kong population in a local large cohort study in 2012 [2]. It is a leading risk factor of cardiovascular diseases which cause significant morbidity and mortality [3, 4]. However, only about half of the treated hypertensive patients achieved target blood pressures according to international studies [5, 6]. The reasons accounting for suboptimal blood pressure control included inadequate treatment, poor patient adherence, white-coat hypertension, undiagnosed secondary hypertension and true resistant hypertension [7, 8].
White-coat hypertension is defined as persistently raised office blood pressure in ambulatory normotensive patients [9]. It accounts for up to 20 to 30% of patients worldwide [10–12]. White-coat effect refers to a patient with home or ambulatory blood pressure within the hypertensive range but who exhibit a disproportionately raised clinic blood pressure reading [9]. It was reported to account for one in four of the treated hypertensive patients with suboptimal office blood pressure control [13, 14] and prevalent among 35–73% of patients with treated hypertension [12, 15–19]. Factors associated with white-coat hypertension including female sex, white ethnicity, increasing age, higher body mass index, higher clinic systolic blood pressure and declining renal function [12, 20, 21].
The detection of white-coat effect is important as failure to recognise the condition may lead to unnecessary treatment causing undesirable side effects for the patients and also inflating the cost of treatment [22–24]. Current gold standard for the diagnosis of white-coat effect is by the 24-h ambulatory blood pressure monitoring (ABPM) which can provide information about blood pressure during daily activities and sleep and thus better evaluation of white-coat effect than home blood pressure [9, 25, 26]. However, ABPM requires specific equipment and expertise to operate and may not be available in every primary care setting.
Deep breathing was shown to be able to lower blood pressure by increasing the baroreceptor reflex sensitivity to vagal stimulation [27, 28]. Studies had shown that deep breathing over 30 to 60 s was useful in detection of the white-coat effect by measuring the difference in systolic blood pressure (SBP) after performing the deep-breathing test (DBT) [29–32]. In the Federico et al. study, the DBT resulted in a statistically significant difference in mean SBP drop of 17.8 and 10.9 mmHg (p < 0.001) among patients with or without white-coat effect respectively [29]. In the Marion et al. study, a 15% drop in SBP was found to be corresponded to a 96% specificity (95% CI 79.0–100.0) and 94% positive predictive value (95% CI 72.0–100.0) in the diagnosis of white-coat hypertension [30]. The Jose et al. study evaluated the diagnostic accuracy of DBT on identifying white-coat hypertension. The study adopted two criteria as a positive response to DBT: a 10 mmHg drop in diastolic blood pressure (DBP) or normalisation of SBP to < 140 mmHg and DBP < 90 mmHg. They found no significant difference (p < 0.26) between the hypertensive patients with or without white-coat effect when using the first criterion but was able to identify a significant difference (p < 0.003) using the second criterion when applied to patients with office SBP < 160/100 mmHg [31]. The application of DBP drop in predicting white-coat hypertension was only found useful in the Yoshihara et al. study [32].
Previous evidence also supported a positive correlation between office SBP and white-coat effect [20, 21]. In a Taiwanese study which compared the characteristics of patients with or without white-coat effect as defined by the gold standard 24 h ABPM, office SBP was found to be significantly correlated with white-coat effect (odd ratio 1.079, 95% CI 1.034–1.125, p < 0.001) [20]. In another large scale study involving over 2000 patients in Greece, a 1.0 mmHg increase in daytime SBP variability was correlated with an increase of 0.589 mmHg [95% CI 0.437–0.741] in the systolic white coat effect [21].
There was also evidence showing that beta-adrenergic blockers might potentiate the baroreflex, possibly through enhancing heart-rate variability and increasing the vagal tone while reducing the sympathetic beta-receptor stimulation and therefore might affect the DBT results [33, 34]. However, the effect of beta-adrenergic blockers on DBT was not considered in all the previous mentioned studies in detecting white-coat effect. To our knowledge, all of the current evidence of clinical application of DBT was derived from non-Chinese population. There was a lack of research data on the validity of DBT in detecting white-coat effect within the Chinese hypertensive population. The effects of different clinic SBP and the use of beta-adrenergic blockers on the application of DBT in Chinese were also lacking. This study therefore aimed to evaluate the applicability of DBT as an alternative diagnostic test of white-coat effect, which would be much less time and resource consuming as compared with the 24-h ABPM in Chinese hypertensive patients. Sub-analysis on the applicability of DBT in detecting white-coat effect in patients with different clinic SBP cut-off and patients not taking beta-adrenergic blockers would also be performed.
Methodology
Study design
This was a cross sectional study conducted in a public primary care clinic serving more than 10,000 hypertensive patients in year 2015. When patient failed to reach target clinic blood pressure during consecutive follow up visits, they would be asked to bring their home blood pressure machine for validation along with their home blood pressure record. If there was a significant discrepancy between the clinic blood pressure and home blood pressure as measured by a validated home blood pressure machine, they would be referred to the Hypertension Clinic for further evaluation by family medicine specialists. Consecutive patients on anti-hypertensives, attending the Hypertension Clinic and fulfilled the inclusion criteria i.e. with clinic SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg at latest two clinic visits during the study period were included. Only those who aged 18 years or above and agreed to give consent in participating in the study were recruited until the required sample size was reached. Non-Chinese patients, patients with suspected secondary hypertension or atrial fibrillation and pregnant patients were excluded. The study was conducted from 1st August 2016 to 30th September 2017. This study was approved by Kowloon East Cluster/ Kowloon Central Cluster Research Ethics Committee/Institutional Review Board of Hospital Authority of Hong Kong.
Procedures
All clinic blood pressure readings were obtained in sitting position with the measuring cuff at heart level, using the calibrated manual sphygmomanometers (UM-101, A&D instruments Ltd., Abingdon, Oxon, U.K) and appropriate cuff sizes. The patient, after resting for at least 5 min, would have his or her blood pressure measured at both arms. The second blood pressure, spaced 2 min apart would then be taken at the arm with higher measured blood pressure [26]. The highest blood pressure values would be used as the clinic blood pressure before performing the DBT. During the DBT, the patient had to take deep breathing cycles for sixty seconds, around one cycle every ten seconds. The patient would be instructed to simulate the respiratory pattern based on an application of tempo counter installed in a smartphone. Another blood pressure would be measured immediately after the DBT on the previously chosen arm. All blood pressure measurements and DBTs would be performed by the same trained nurse blinded to the 24-h ABPM results.
Subsequently, a monitor (TM-2430, A&D instruments Ltd., Abingdon, Oxon, U.K) would be installed to perform the 24-h ABPM with an appropriate cuff placed on the non-dominant arm and programmed to measure blood pressure every 30 min while patients were awaked and 60 min while patients were asleep. The awake and sleeping time would be programmed individually based on the history given by patients. Patients with ABPM reports showing more than 80% successful readings would be included and their results would be interpreted by family medicine specialists who were blinded to the DBT results. There was no internationally standardized definition on the interpretation of white-coat effect by using the 24-h ABPM. In this study, it would be defined as a decrease of 10 mmHg or more in mean daytime ambulatory SBP when compared with the clinic SBP [9].
Patients’ demographic data including age, gender, body mass index, smoking status, presence of diabetes and cardiovascular diseases and number and types of anti-hypertensives taken were retrieved from computerized record.
Sample size calculation
Previous studies showed that the sensitivity of using 10 mmHg difference as cut-off to define white-coat effect after DBT was 0.8 [29]. The prevalence of white-coat effect in hypertensive Chinese patients was lacking. Taking 35% as the most conservative estimated prevalence of white-coat effect in treated hypertensive patients in previous international studies [12, 15–19] and maximum marginal error of estimate as 0.10, the sample size needed was 176 [35].
Outcome
The main outcome was to evaluate the diagnostic accuracy of the DBT for white-coat effect by means of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive and negative predictive values in Chinese hypertensive patients. Sub-analysis on the effect of beta-adrenergic blockers and different clinic SBP cut-off on the diagnostic accuracy of the DBT would also be evaluated. Blood pressure responses to DBT in all patients and different sub-groups with or without white-coat effect would be presented in terms of mean blood pressure drop after the test.
Statistical analysis
Central tendencies and distributions of continuous variables were presented as means and standard deviations respectively. The means of continuous variables were compared with independent samples t test. Categorical variables were presented as proportions and percentages. They were compared with Chi-square test (with Yate’s correction for 2 × 2 comparisons) or Fisher’s exact test. Areas under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive and negative predictive values were calculated to assess the diagnostic accuracy of the DBT, with respective 95% confidence interval. Differences were considered statistically significant when p < 0.05. SPSS version 21 was used for statistical analysis.
Results
Study population
Demographic data and clinical characteristics of patients with white-coat and without white-coat effect (N = 178)
Clinical characteristics | White-coat effect | Total no. | p value | |
|---|---|---|---|---|
Present (n = 122) | Absent (n = 56) | (%) | ||
Age (years) | ||||
Mean | 65.6 (SD 9.7) | 63.8 (SD 10.7) | 65.0 (SD 10) | 0.270 |
No. of patients < 40 | 1 | 1 | 2 (1.1) | |
40–49 | 8 | 4 | 12 (6.7) | |
50–59 | 23 | 14 | 37 (20.8) | |
60–69 | 48 | 19 | 67 (37.7) | 0.791 |
70–79 | 34 | 16 | 50 (28.1) | |
≥ 80 | 8 | 2 | 10 (5.6) | |
Sex | ||||
Male | 45 | 19 | 64 (36.0) | 0.703 |
Female | 77 | 37 | 114 (64.0) | |
Body mass index (kg/m2) | ||||
Mean | 25.3 (SD 3.7) | 25.6 (SD 3.8) | 0.618 | |
No. of patients < 23 | 28 | 17 | 45 (25.3) | |
23–24.9 (overweight) | 36 | 11 | 47 (26.4) | 0.317 |
≥ 25 (obesity) | 58 | 28 | 86 (48.3) | |
Smoking status | ||||
Non smoker | 101 | 43 | 144 (80.9) | 0.296 |
Ex-smoker | 19 | 13 | 32 (1.1) | |
Current smoker | 2 | 0 | 2 (18.0) | |
Presence of diabetes | ||||
Yes | 38 | 17 | 55 (30.9) | 0.916 |
No | 84 | 39 | 123 (69.1) | |
History of cardiovascular disease | ||||
Yes | 8 | 5 | 13 (7.3) | 0.757 |
No | 114 | 51 | 165 (92.7) | |
No. of anti-hypertensives taken | ||||
Monotherapy | 66 | 31 | 97 (54.5) | |
Two drugs | 33 | 16 | 49 (27.5) | 0.866 |
Three or more drugs | 23 | 9 | 32 (18.0) | |
Types of anti-hypertensives taken | ||||
Alpha-blockers | 9 | 7 | 16 (9.0) | 0.267 |
Angiotensin-converting enzyme inhibitors | 34 | 15 | 49 (27.5) | 0.881 |
Angiotensin receptor blockers | 17 | 9 | 26 (14.6) | 0.820 |
Beta-adrenergic blockers | 28 | 10 | 38 (21.3) | 0.555 |
Calcium channel blockers | 94 | 38 | 132 (74.2) | 0.193 |
Diuretics | 10 | 5 | 15 (8.4) | 1 |
Hydralazine | 8 | 5 | 14 (7.9) | 0.757 |
Methyldopa | 7 | 2 | 9 (5.1) | 0.722 |
Deep breathing test results
BP responses to the deep breathing test in all patients (N = 178)
Variables | Mean BP before DBT (mmHg) | Mean BP after DBT (mmHg) | p value |
|---|---|---|---|
Systolic BP | 153.6 (SD 17.4) | 138.7 (SD 16.9) | < 0.001 |
Diastolic BP | 77.1 (SD 10.5) | 72.8 (SD 10.7) | < 0.001 |
Deep breathing test results in all patients (N = 178)
BP response to the deep breathing test | White-coat effect | p value | |
Present (n = 122) | Absent (n = 56) | ||
Mean SBP drop (mmHg) | 15.5 (SD 12.0) | 13.3 (SD 11.7) | 0.247 |
Mean DBP drop (mmHg) | 4.4 (SD 5.2) | 3.8 (SD 4.3) | 0.436 |
Deep breathing test operating characteristics | Area under ROC curve | 95% confidence interval (CI) | p value |
SBP change | 0.52 | 0.43–0.61 | 0.611 |
DBP change | 0.53 | 0.44–0.62 | 0.552 |
Subgroup analysis with different pre-test SBP cut off
Sub-group analysis of deep breathing test results with different pre-test SBP cut-off
BP response to the deep breathing test | White-coat effect | p value | |
Present | Absent | ||
(i) SBP ≥ 145 mmHg (N = 118) | n = 78 | n = 40 | |
Mean SBP drop (mmHg) | 19.2 (SD 12.8) | 14.1 (SD 12.5) | *0.042 |
Mean DBP drop (mmHg) | 4.9 (SD 5.4) | 3.8 (SD 4.3) | 0.268 |
(ii) SBP ≥ 155 mmHg (N = 74) | n = 51 | n = 23 | |
Mean SBP drop (mmHg) | 20.8 (SD 14.5) | 15.1 (SD 13.0) | 0.107 |
Mean DBP drop (mmHg) | 4.8 (SD 6.3) | 4.3 (SD 4.2) | 0.739 |
(iii) SBP ≥ 165 mmHg (N = 43) | n = 30 | n = 13 | |
Mean SBP drop (mmHg) | 24.4 (SD 15.4) | 15.3 (SD 13.2) | 0.073 |
Mean DBP drop (mmHg) | 4.9 (SD 5.8) | 3.5 (SD 3.5) | 0.451 |
Deep breathing test operating characteristics | Area under ROC curve | 95% confidence interval (CI) | p value |
(i) SBP ≥ 145 mmHg (N = 118) | |||
SBP change | 0.60 | 0.49–0.70 | 0.087 |
DBP change | 0.55 | 0.44–0.66 | 0.365 |
(ii) SBP ≥ 155 mmHg (N = 74) | |||
SBP change | 0.61 | 0.48–0.74 | 0.130 |
DBP change | 0.51 | 0.37–0.65 | 0.912 |
(iii) SBP ≥ 165 mmHg (N = 43) | |||
SBP change | 0.68 | 0.51–0.84 | 0.070 |
DBP change | 0.57 | 0.39–0.74 | 0.500 |
Subgroup analysis with patients on beta-adrenergic blockers excluded
Sub-analysis of deep breathing test results with exclusion of patients on beta-adrenergicblockers
BP response to the deep breathing test | White-coat effect | p value | |
Present | Absent | ||
A. Patients not on beta-adrenergic blockers (N = 140) | n = 94 | n = 46 | |
Mean SBP drop (mmHg) | 15.7 (SD 11.5) | 12.8 (SD 11.0) | 0.149 |
Mean DBP drop (mmHg) | 4.5 (SD 5.1) | 3.9 (SD 4.3) | 0.465 |
B. Patients not on beta-adrenergic blockers and with different clinic SBP cut-off before DBT | |||
(i) SBP ≥ 145 mmHg (N = 92) | n = 58 | n = 34 | |
Mean SBP drop (mmHg) | 19.8 (SD 12.1) | 14.5 (SD 11.5) | *0.043 |
Mean DBP drop (mmHg) | 4.9 (SD 5.2) | 4.1 (SD 4.3) | 0.449 |
(ii) SBP ≥ 155 mmHg (N = 58) | n = 38 | n = 20 | |
Mean SBP drop (mmHg) | 22.0 (SD 13.4) | 15.1 (SD 13.7) | 0.068 |
Mean DBP drop (mmHg) | 4.9 (SD 6.0) | 4.6 (SD 4.4) | 0.847 |
(iii) SBP ≥ 165 mmHg (N = 33) | n = 22 | n = 11 | |
Mean SBP drop (mmHg) | 25.6 (SD 13.1) | 14.6 (SD 14.3) | *0.035 |
Mean DBP drop (mmHg) | 6.1 (SD 5.6) | 3.8 (SD 3.6) | 0.232 |
Deep breathing test operating characteristics | Area under ROC curve | 95% confidence interval (CI) | p value |
A. Patients not on beta-adrenergic blockers (N = 140) | |||
SBP change | 0.56 | 0.46–0.66 | 0.288 |
DBP change | 0.53 | 0.43–0.63 | 0.554 |
B. Patients not on beta-adrenergic blockers and with different clinic SBP cut-off before DBT | |||
(i) SBP ≥ 145 mmHg (N = 92) | |||
SBP change | 0.62 | 0.50–0.73 | 0.066 |
DBP change | 0.54 | 0.41–0.66 | 0.580 |
(ii) SBP ≥ 155 mmHg (N = 58) | |||
SBP change | 0.64 | 0.49–0.78 | 0.094 |
DBP change | 0.51 | 0.35–0.66 | 0.948 |
(iii) SBP ≥ 165 mmHg (N = 33) | |||
SBP change | 0.72 | 0.53–0.91 | *0.040 |
DBP change | 0.62 | 0.42–0.82 | 0.260 |
ROC curve in patients with SBP ≥165 mmHg showed that SBP change was a good diagnostic test for white-coat effect with the area under curve 0.72 (95% CI 0.53–0.91, p = 0.04)
Deep breathing test operating characteristics for SBP drop in patients not on beta-adrenergic blockers and with pre-test SBP ≥165 mmHg
Cut-off value | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) |
|---|---|---|---|---|
> 10 mmHg | 90.9 | 36.4 | 74 | 67 |
> 20 mmHg | 77.3 | 63.6 | 81 | 58 |
> 30 mmHg | 40.9 | 90.9 | 90 | 43 |
> 40 mmHg | 13.6 | 90.9 | 75 | 34 |
Discussion
Even though ABPM remains the gold standard in the diagnosis of white-coat effect in hypertension, it requires specific equipment and extra manpower that might not be readily available in every primary care physician’s office. A much simpler DBT would be invaluable to detect white-coat effect and avoid unnecessary over-treatment.
Our study showed an important finding that DBT was useful in detecting white-coat effect in a specific group of Chinese hypertensive patients i.e. patients with pre-test SBP ≥165 mmHg and who were not taking beta-adrenergic blockers. In this group of patients, the DBT showed high specificity (90.9%) if the post-test SBP drop was more than 30 mmHg. The relatively high PPV and low NPV implied that DBT was most useful to rule in the existence of white-coat effect.
Our study was one of the studies with larger sample size as compared to previous studies and probably the first to examine the deep breathing test’s applicability in Chinese patients. Furthermore, our study was the first to have taken into consideration the practicality of performing the DBT in actual clinical setting where the patients were more likely to be continued on their usual drug regime while performing the DBT. Neither the previous studies conducted by Federico et al. and Marion et al. took into account for the types of anti-hypertensives the patients were taking while they were performing the DBT, while in the Jose et al. study, the study subjects had anti-hypertensive withdrawn for 2–3 weeks before attempting the DBT, which might be less practical in a non-research setting.
Our study added information that beta-adrenergic blockers and different clinic SBP cut-off would affect the DBT performance. Since higher office SBP was positively correlated with white-coat effect, we postulated that patients with higher pre-test SBP would exhibit significant difference in BP responses after DBT between the white-coat and non white-coat groups. Though the mean SBP drop between the two groups did not show increasing significance as we moved towards higher SBP cut-off, it reached statistical significance across both groups with SBP ≥145 mmHg and ≥ 165 mmHg and patients not on beta-adrenergic blockers. The findings also supported the evidence that beta-adrenergic blockers could modify the baroreflex and hence affect the DBT results.
Previous studies showed conflicting evidence on whether the drop of SBP or DBP post-DBT could be used to evaluate the presence of white-coat effect [29–32]. Our study showed that DBP drop was not useful in detecting white coat effect which was compatible with most of the other published studies [29–31]. .For SBP drop, our study did not reach a significant value when all patient analysis was performed. Ethnicity might have partly accounted for this difference. Another reason we postulated was the way that DBT was performed, which differed among studies. In the Marion et al. study, the study subjects were asked to perform 3 or 4 cycles of deep breathing in 30 s while in the Federico et al. study, the number of deep breathing cycles was not specified. Only in the Jose et al. study were the patients asked to perform DBT according to the analog clock who simulated 0.1 Hz breathing cycle. The advantage of which was a more consistent and reproducible test result and also more conforming to the original idea behind synchronising the breathing cycle with the innate cardiovascular rhythms to modulate the baroreflex.
Limitation
Our study showed a significant result after exclusion of patients on beta-adrenergic blockers and those with pre-test SBP < 165 mmHg. However, the sample size by such exclusivity became smaller and hence affected the statistical accuracy to find a precise SBP cut-off for distinguishing between the white-coat and non white-coat effect. Further research is suggested to specifically investigate this sub-group with a larger sample size. Moreover, the study was conducted in only one single clinic which was specialized on assessing hypertensive patients with suspected white-coat effect. The clinic had exceptionally more patients with white-coat effect diagnosed (68.5%) and the results might not be able to be generalised to all primary care clinic settings in Hong Kong.
Throughout the study, there were several occasions where the post DBT blood pressures were higher than the pre-test values. As mentioned earlier, the device guided breathing cycle had its advantages, nonetheless, it also increased the anxiety in some of our patients during the attempt to synchronise their breathing rate to that of the counter on the mobile device. More training and attempts might be needed to allow the patients more adapt to the deep breathing exercise and produce a more pronounced blood pressure lowering effect. For those patients who showed a paradoxically increase in blood pressure after DBT, further evaluation with a standard ABPM should be performed.
Conclusion
The DBT could not be clinically applied to all Chinese patients with suspected white-coat effect. However, the study proved that it would be a potential screening and diagnostic test for white-coat effect under certain selection criteria; i.e. in patients with a pre-test SBP of ≥165 mmHg and who were not taking beta-adrenergic blockers. Future larger scale studies should be conducted to enhance the statistical accuracy and generalisability of the results.
Abbreviations
- ABPM:
-
Ambulatory blood pressure monitoring
- BP:
-
Blood pressure
- CI:
-
Confidence interval
- DBP:
-
Diastolic blood pressure
- DBT:
-
Deep breathing test
- NPV:
-
Negative predictive value
- PPV:
-
Positive predictive value
- ROC:
-
Receiver operating characteristic
- SBP:
-
Systolic blood pressure
Declarations
Acknowledgements
We would like to thank all nurses and doctors involved in the collection of the trial data.
Funding
No funding was received since the trial was conducted under routine clinic circumstances.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Authors’ contributions
Dr. KSC was the main party overlooking the running of the trial and acquisition of data, drafting and writing of the manuscript. Dr. KPLL and Dr. PFC were the supervisors of the trial, and were involved in the conception and design of the trial and also revising the manuscript. Dr. MHML was responsible in the data analysis of trial. Dr. VKDC was involved in the revision of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study was approved by Kowloon East Cluster/ Kowloon Central Cluster Research Ethics Committee/Institutional Review Board of Hospital Authority of Hong Kong under the registration KC/KE-16-0084/ER-3. All study subjects gave written consent before recruitment. There was no reported adverse event from trial.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
References
- Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2224–60.View ArticleGoogle Scholar
- Leung G, Ni M, Wong P, Lee P, Chan B, Stewart S, et al. Cohort profile: FAMILY cohort. Int J Epidemiol. 2015;46(2):e1.View ArticleGoogle Scholar
- Collins R, Peto R, MacMahon S, et al. Blood pressure, stroke, and coronary heart disease. Part 2, short-term reductions in blood pressure: overview of randomised drug trials in their epidemiological context. Lancet. 1990;335:827–38.View ArticleGoogle Scholar
- The Hypertension Detection and Follow-up Program Cooperative Group. Division of heart and vascular diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md. Five-year findings of the hypertension detection and follow-up program: I. reduction in mortality of persons with high blood pressure, including mild hypertension. JAMA. 1979;242(23):2562.View ArticleGoogle Scholar
- Falaschetti E, Chaudhury M, Mindell J, Poulter N. Continued improvement in hypertension management in England: results from the Health Survey for England 2006. Hypertension. 2009;53(3):480.View ArticleGoogle Scholar
- Hajjar I, Kotchen TA. Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988–2000. JAMA. 2003;290:199–206.View ArticleGoogle Scholar
- Lloyd-Jones DM, Evans JC, Larson MG, O'Donnell CJ, Rocella EJ, Levy D. Differential control of systolic and diastolic blood pressure: factors associated with lack of blood pressure control in the community. Hypertension. 2000;36:594–9.View ArticleGoogle Scholar
- Bunker J, Callister W, Chang C, Sever P. How common is true resistant hypertension? J Hum Hypertens. 2010;25(2):137–40.View ArticleGoogle Scholar
- National Institute of Health and Clinical Excellence. Hypertension: clinical management of primary hypertension in adults. London: NICE; 2011. NICE clinical guideline 127Google Scholar
- O’Brien E, Coats A, Owens P, Petrie J, Padfield PL, Littler WA, de Swiet M, Mee F. Use and interpretation of ambulatory blood pressure monitoring: recommendations of the British hypertension society. BMJ. 2000;320:1128–34.View ArticleGoogle Scholar
- Streitel KL, Graham JE, Pickering TG, Gerin W. Explaining gender differences in the white coat effect. Blood Press Monit. 2011;16:1–6.View ArticleGoogle Scholar
- MacDonald MB, Laing GP, Wilson MP, Wilson TW. Prevalence and predictors of white-coat response in patients with treated hypertension. CMAJ. 1999;161:265–9.PubMedPubMed CentralGoogle Scholar
- de la Sierra A, Segura J, Banegas J, Gorostidi M, de la Cruz J, Armario P, et al. Clinical features of 8295 patients with resistant hypertension classified on the basis of ambulatory blood pressure monitoring. Hypertension. 2011;57(5):898–902.View ArticleGoogle Scholar
- Brown M, Buddle M. Martin a. is resistant hypertension really resistant? Am J Hypertens. 2001;14(12):1263.View ArticleGoogle Scholar
- Myers MG. The white coat effect in treated hypertension. Blood Press Monit. 1996;1:247–9.PubMedGoogle Scholar
- Little P, Barnett J, Barnsley L, Marjoram J, Fitzgerald Berron A, Mant D. Comparison of agreement between different measures of blood pressure in primary care and daytime ambulatory blood pressure. BMJ. 2002;325:254–9.View ArticleGoogle Scholar
- Myers MG, Reeves RA. White coat phenomenon in patients receiving antihypertensive therapy. Am J Hypertens. 1991;4:844–9.View ArticleGoogle Scholar
- Liana FL, Mateus DS, Priscila SL, et al. White coat effect and masked uncontrolled hypertension in treated hypertensive-diabetic patients: prevalence and target organ damage. J Diabetes. 2015;7(5):699–707.View ArticleGoogle Scholar
- Pierdomenico SD, Mezzetti A, Lapenna D, Guglielmi MD, Mancini M, Salvatore L, et al. ‘White-coat’ hypertension in patients with newly diagnosed hypertension: evaluation of prevalence by ambulatory monitoring and impact on cost of health care. Eur Heart J. 1995;16:692–7.View ArticleGoogle Scholar
- Huang CC, Wu TC, Lin SJ, Chen JW, Leu HB. Clinical predictors of significant white-coat effect in non-diabetic hypertensive patients. Acta Cardiol Sin. 2010;26:151–6.Google Scholar
- Manios E, Koroboki E, Tsivgoulis G, Spengos K, Spiliopoulou I, Brodie F, et al. Factors influencing White-coat effect. Am J Hypertens. 2008;21(2):153–8.View ArticleGoogle Scholar
- Redon J, Campos C, Narciso ML, Rodicio JL, Pascual JM, Ruilope LM. Prognostic value of ambulatory blood pressure monitoring in refractory hypertension: a prospective study. Hypertension. 1998;31(2):712–8.View ArticleGoogle Scholar
- White WB. Ambulatory blood pressure as a predictor of target organ disease and outcome in the hypertensive patient. Blood Press Monit. 1999;4(3–4):181–4.PubMedGoogle Scholar
- Parati G, Ulian L, Santucciu C, Omboni S, Mancia G. Difference between clinic and daytime blood pressure is not a measure of the white-coat effect. Hypertension. 1998;31:1185–9.View ArticleGoogle Scholar
- Mancia G, Fagard R, Narkiewicz K, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013;34(28):2159–219.View ArticleGoogle Scholar
- Staessen JA, Asmar R, De Buyzere M, Imai Y, Parati G, Shimada K, Stergiou G, Redón J, Verdecchia P. Participants of the 2001 consensus conference on ambulatory blood pressure monitoring. Task force II: blood pressure measurement and cardiovascular outcome. Blood Press Monit. 2001;6:355–70.View ArticleGoogle Scholar
- Mori H, Yamamoto H, Kuwashima M, Saito S, Ukai H, Hirao K, et al. How does deep breathing affect office blood pressure and pulse rate? Hypertens Res. 2005;28:499–504.View ArticleGoogle Scholar
- Bernardi L, Porta C, Spicuzza L, Bellwon J, Spadacini G, Frey AW, et al. Slow breathing increases arterial baroreflex sensitivity in patients with chronic heart failure. Circulation. 2002;105:143–5.View ArticleGoogle Scholar
- Augustovski FA, Calvo CB, Deprati M, Waisman G. The deep-breath test as a diagnostic maneuver for white-coat effect in hypertensive patients. J Am Board Fam Pract. 2004;17:184–9.View ArticleGoogle Scholar
- Tomičić M, Petric D, Rumboldt M, Carević V, Rumboldt Z. Deep breathing: a simple test for white coat effect detection in primary care. Blood Press. 2015;24(3):158–63.View ArticleGoogle Scholar
- Thalenberg JM, Povoa RM, Bombig MT, Sa GA, Atallah AN, Luna Filho B. Slow breathing test increases the suspicion of white-coat hypertension in the office. Arq Bras Cardiol. 2008;91:243–9.View ArticleGoogle Scholar
- Yoshihara K, Fukui T, Osawa H, Ishii Y, Morita H, Yamashiro S, et al. Deep breathing test (DBT) in predicting white coat hypertension. Fukuoka Igaku Zasshi. 1993;84:395–401.PubMedGoogle Scholar
- Parati G, Mutti E, Frattola A, Castiglioni P, di Rienzo M, Mancia G. Beta-adrenergic blocking treatment and 24-hour baroreflex sensitivity in essential hypertensive patients. Hypertension. 1994;23(6 Pt 2):992–6.View ArticleGoogle Scholar
- Niemelä M, Airaksinen K, Huikuri H. Effect of Beta-blockade on heart rate variability in patients with coronary artery disease. J Am Coll Cardiol. 1994;23(6):1370.View ArticleGoogle Scholar
- Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Info. 2014;48:193–204.View ArticleGoogle Scholar
