Introduction
The differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT) is of great significance in clinical practice. WCH and SHT have a high prevalence in the general population; however, the target organ damaging effects of WCH and SHT differ from each other [1,2]. SHT is a hypertension subtype associated with target organ damage [3]. However, it is still controversial whether WCH is a benign clinical phenomenon or a clinical disease [4]. Notably, since the European Society of Hypertension practice guidelines (2014 edition) revised the diagnostic criteria for WCH and excluded isolated nocturnal hypertension with target organ damage from WCH, the cardiovascular and cerebrovascular injury effect of WCH has not been observed in many clinical studies [5,6]. For example, a large-scale clinical study including 115 708 samples in 2017 indicated no higher risk of cardiovascular disease in WCH than normotension [7]. WCH and SHT had elevated office blood pressure; however, cardiovascular and cerebrovascular injury effects significantly differed between the two groups [8]. A suitable discrimination between WCH and SHT in the clinical practice assists medical personnel in understanding a patient’s blood pressure status and formulating more appropriate treatment plans.
Despite the importance of clinical identification of WCH and SHT, their initial identification has always been challenging in clinical practice [9]. Currently, recognized methods include home blood pressure measurement and ambulatory blood pressure monitoring [10,11]. The method of measuring blood pressure at home by the general population is not standardized, and home self-test blood pressure cannot reflect nighttime blood pressure levels; its differential effect between WCH and SHT is lower than ambulatory blood pressure monitoring [12]. However, the clinical application of 24 h ambulatory blood pressure monitoring necessitates more time and expense on the part of the patient. If medical personnel can forecast WCH at the initial diagnosis, treatment can be carried out more rationally.
Some medical workers empirically identified WCH based on the clinician’s knowledge of the clinical characteristics of WCH. Previous research demonstrated that WCH is frequently high in women and clinically characterized by isolated systolic hypertension (ISH). In contrast, SHT is susceptible to dyslipidemia, renal dysfunction, and cardiovascular and cerebrovascular diseases (CCVD) [13–15]. Some medical practitioners evaluate WCH and SHT empirically based on known clinical criteria, but no scientific quantitative scoring method exists. The growing implementation of scoring models for differential diagnosis has provided the opportunity in recent years. The scoring model belongs to the category of risk prediction models. The primary advantage of our scoring model is its ability to combine several differential variables through the regression equation to improve the identification ability [16,17]. This study is aimed to develop a scoring model for differential diagnosis of WCH and SHT using least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate logistic regression analysis, and other statistical methods and to implement it in clinical practice.
Methods
Study participants
A total of 826 adults with elevated office blood pressure were recruited in Daping Hospital between April 2018 and June 2020. Participants were further screened using the following two exclusion criteria: (1) patients taking antihypertensive medications; (2) patients with renal insufficiency (serum creatinine, Scr ≥ 133 µmol/l) [18]. The study was evaluated and approved by the Ethics Committee of Daping Hospital, Army Medical University, and registered in the Chinese Clinical Trial Registry (registration number ChiCTR1800015507). All participants signed informed consent forms.
General clinical information
The age, sex, smoking and drinking histories, diabetes, and CCVD histories were obtained from the participants through questionnaires. The height and weight of the selected participants were measured, and their BMI was calculated by using the formula = weight (kg)/height2 (m) [19].
Blood pressure measurement
After the participants took a seat to rest in the clinic for 20 min, the blood pressure of the right brachial artery was measured three times using a mercury sphygmomanometer (Yuyue desktop vertical mercury sphygmomanometer, Jiangsu Yuyue Medical Equipment & Supply Co., Ltd., China) by experienced medical personnel. The average blood pressure measurement was taken as an office blood pressure measurement [20]. Using the ambulatory ECG blood pressure recorder (CB-2301-A, Zhongjian Keyi co., ltd., Wuxi, China), 24 h ambulatory blood pressure was monitored from 6:00 to 22:00 as daytime blood pressure and 22:00–6:00 as nighttime blood pressure. Daytime blood pressure measurement was conducted every 30 min, the number of efficient sphygmomanometers was above 80%, and the blood pressure measurements at night were performed after every hour.
WCH and SHT were distinguished according to office blood pressure and 24 h ambulatory blood pressure. The European Hypertension Practice Guideline Standard (2014 Edition) was adopted, and the cutoff value for blood pressure elevation was set to daytime average ambulatory blood pressure ≥135/85 mmHg, nighttime average ambulatory blood pressure ≥120/70 mmHg, or 24 h average blood pressure ≥130/80 mmHg [21,22]. SHT was defined as elevated office blood pressure with elevated daytime, nighttime, or 24 h arterial blood pressure while WCH only demonstrated elevated office blood pressure [23]. ISH was diagnosed in subjects with elevated office SBP but normal office DBP [24].
Biochemical detection
The total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose, Scr, and uric acid were measured by BECKMAN AU5800 biochemical analyzer (Beckman Coulter Inc., Brea, California, USA) . Diabetes mellitus was defined as a previous definitive diagnosis of diabetes mellitus or a fasting blood glucose level of ≥7.0 mmol/l [25].
Division of training and validation sets
In the RStudio environment (version 1.3.959, RStudio Team, Boston, Massachusetts, USA), participants were divided into two groups, a training set and a validation set, with a ratio of 4:1. The statistical differences in age, sex, WCH prevalence rate, and other variables were compared between the training and validation sets. Variations between WCH and SHT patients were also investigated in the training and validation sets. The counting data is represented as the number of cases (%), and the Pearson chi-square test was used for statistical analysis. The measurement data were expressed as median (interquartile range), and Kruskal–Wallis rank-sum test was used for statistical analysis [26,27]. The above statistical analysis was performed in the SPSS computing environment (version 22.0, SPSS Inc., Chicago, Illinois, USA)
Model construction
The LASSO regression and univariate logistic regression were used to select the variables of the model in the RStudio environment. The study variables involved in the screening process included sex, age, BMI, smoking and drinking history, ISH, SBP, DBP, heart rate, diabetes mellitus, total cholesterol, triglyceride, HDL-C, LDL-C, Scr, uric acid, and CCVD. First, the LASSO regression model was implemented using 10-fold cross-validation with penalty parameter tuning based on minimum criteria and one standard error of the minimum criteria (the 1-SE criteria) in the training set. The LASSO regression model is suitable for the regression of high-dimensional and interactive data analysis. We further carried out univariate logistic regression analysis on the research variables according to the 1-SE criteria and included the research variables with statistical differences as the final model construction variables. Multivariate logistic regression analysis was used to construct the scoring model of WCH, and the corresponding nomogram was drawn [28]. The R language packets used included ‘readxl’, ‘glmnet’, ‘informationValue’, ‘rms’, and ‘pROC’, among others.
Discrimination test
First, the receiver operating curve (ROC) was applied to the training set to assess the discrimination degree of the scoring model in the training set, and the coefficient of determination (R2), the area under the ROC curve (AUC), and its 95% confidence interval (95% CI) were calculated. Finally, the established model was verified in the validation set, aiming to test the fitting degree of model [29].
Calibration degree test
First, the bootstrap method was used to randomly select samples 1000 times in the training set to verify the calibration degree of the model. The corresponding calibration curves were made, and the mean square error (MSE) and mean absolute error (MAE) values were calculated to evaluate the model. The lower the MAE and MSE, the better the stability of model [30]. Furthermore, the calibration degree of the model in the validation set was also checked by the same method.
Results
A total of 826 adults with elevated office blood pressure volunteered for this study, out of which 241 participants taking antihypertensive drugs and 32 participants with renal insufficiency were excluded. Finally, 553 adults were included in the study, 304 males and 249 females, with an average age of 63.6 years.
Training and validation sets
Participants were randomly divided into training and validation sets. There were 445 participants in the training and 108 in the validation sets. The prevalence rate of WCH was 35.1% in the training set, whereas in the validation set, it was 44.4%, which was not significantly different between the data sets (P = 0.070, Table 1). Other modeling parameters were also not statistically different between groups, except for gender and uric acid (P = 0.043 and 0.001, respectively, Table 1).
Table 1 -
Statistical analysis of the differences of research parameters between training set and validation set
| Parameters |
Training set |
Validation set |
P value |
| Individuals |
445 |
108 |
–
|
| WCH (%) |
156 (35.1) |
48 (44.4) |
0.070 |
| Female (%) |
191 (42.9) |
58 (53.7) |
0.043 |
| Age (years) |
64.0 (18.0) |
65.0 (16.0) |
0.734 |
| BMI (kg/m2) |
24.2 (4.4) |
23.8 (3.5) |
0.061 |
| Smoking (%) |
143 (32.1) |
31 (28.7) |
0.491 |
| Drinking (%) |
109 (24.5) |
27 (25.0) |
0.913 |
| Diabetes (%) |
69 (15.5) |
16 (14.8) |
0858 |
| ISH (%) |
220 (49.4) |
55 (50.9) |
0.781 |
| SBP |
144.0 (15.0) |
145.0 (12.0) |
0.347 |
| DBP |
90.0 (14.0) |
88.5 (15.0) |
0.894 |
| HR |
78.0 (18.0) |
77.5 (18.0) |
0.819 |
| TC (mmol/l) |
4.24 (1.37) |
4.16 (1.28) |
0.435 |
| TG (mmol/l) |
1.42 (1.05) |
1.27 (0.75) |
0.096 |
| HDL-C (mmol/l) |
1.09 (0.35) |
1.11 (0.37) |
0.563 |
| LDL-C (mmol/l) |
2.71 (1.01) |
2.58 (0.93) |
0.490 |
| Scr (μmol/l) |
66.4 (21.1) |
63.9 (22.7) |
0.217 |
| UA (μmol/l) |
337.2 (121.6) |
303.5 (98.9) |
0.001 |
| CCVD (%) |
154 (34.6) |
46 (42.6) |
0.121 |
CCVD, cardiovascular and cerebrovascular diseases; DBP, office diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; HR, heart rate; ISH, isolated systolic hypertension; LDL-C, low-density lipoprotein cholesterol; SBP, office systolic blood pressure; Scr, serum creatinine; TC, total cholesterol; TG, triglyceride; UA, serum uric acid; WCH, white coat hypertension.
In the training set, the prevalence of ISH was significantly higher in WCH patients than among SHT patients (P < 0.001, Table 2). SHT patients had significantly higher SBP, DBP, and triglyceride levels than the WCH group (P = 0.005, <0.001, and 0.021, respectively, Table 2). The prevalence rate of CCVD was significantly higher in SHT patients than in WCH patients (P = 0.037, Table 2).
Table 2 -
Statistical analysis of the differences of research variables between
white coat hypertension patients and
sustained hypertension patients
| Parameters |
Training set |
P valuea
|
Validation set |
P valueb
|
| WCH |
SHT |
WCH |
SHT |
| Individuals |
156 |
289 |
– |
48 |
60 |
– |
| Female (%) |
70 (44.9) |
121 (41.9) |
0.541 |
32 (66.7) |
26 (43.3) |
0.016 |
| Age (years) |
64.0 (14.0) |
64.0 (18.0) |
0.716 |
65.0 (17.0) |
64.5 (15.0) |
0.805 |
| BMI (kg/m2) |
24.5 (4.5) |
24.2 (4.4) |
0.471 |
23.6 (3.8) |
23.9 (3.5) |
0.257 |
| Smoking (%) |
42 (26.9) |
101 (22.7) |
0.084 |
11 (22.9) |
20 (33.3) |
0.234 |
| Drinking (%) |
41 (24.5) |
68 (23.5) |
0.519 |
7 (14.6) |
20 (33.3) |
0.025 |
| Diabetes (%) |
21 (13.5) |
48 (16.6) |
0.381 |
6 (12.5) |
10 (16.7) |
0.545 |
| ISH (%) |
99 (63.5) |
121 (41.9) |
<0.001 |
30 (62.5) |
25 (41.7) |
0.031 |
| SBP |
142.5 (14.0) |
145.0 (17.0) |
0.005 |
142.5 (7.0) |
148.0 (18.0) |
0.001 |
| DBP |
83.0 (13.0) |
90.0 (12.0) |
<0.001 |
84.5 (13.0) |
91.0 (17.0) |
0.010 |
| HR |
78.0 (20.0) |
79.0 (15.0) |
0.398 |
76.0 (18.0) |
78.0 (18.0) |
0.204 |
| TC (mmol/l) |
4.23 (1.28) |
4.29 (1.43) |
0.277 |
4.16 (1.50) |
4.18 (1.11) |
0.936 |
| TG (mmol/l) |
1.52 (1.20) |
1.34 (0.83) |
0.021 |
1.26 (0.94) |
1.27 (0.74) |
0.483 |
| HDL-C (mmol/l) |
1.12 (0.35) |
1.08 (0.35) |
0.272 |
1.14 (0.38) |
1.09 (0.35) |
0.483 |
| LDL-C (mmol/l) |
2.70 (0.92) |
2.75 (1.07) |
0.293 |
2.59 (0.92) |
2.59 (1.02) |
0.850 |
| Scr (μmol/l) |
64.6 (19.3) |
67.1 (21.2) |
0.053 |
59.6 (20.0) |
67.3 (23.2) |
0.187 |
| UA (μmol/l) |
328.7 (112.6) |
346.8 (124.9) |
0.050 |
297.6 (87.4) |
313.1 (109.3) |
0.130 |
| CCVD (%) |
44 (28.2) |
110 (38.1) |
0.037 |
16 (33.3) |
30 (50.0) |
0.082 |
aP value, statistical analysis results of each parameter in the training set.
bP value, statistical analysis results of each parameter in the Validation set.
CCVD, cardiovascular and cerebrovascular diseases; DBP, office diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; HR, heart rate; ISH, isolated systolic hypertension; LDL-C, low-density lipoprotein cholesterol; SBP, office systolic blood pressure; Scr, serum creatinine; SHT, sustained hypertension; TC, total cholesterol; TG, triglyceride; UA, serum uric acid; WCH, white coat hypertension.
In the validation set, the prevalence of ISH was significantly higher in WCH patients than in SHT patients (P = 0.031, Table 2). At the same time, SBP and DBP were significantly higher in SHT patients (P = 0.001, P = 0.010, respectively, Table 2). Besides, the proportion of females in WCH patients was significantly higher than in SHT patients (P = 0.016, Table 2).
Variable screening
LASSO coefficient profiles of the 17 variables and 10-fold cross-validation for tuning parameter selection in the LASSO model were shown in Fig. 1. Six variables were selected by the LASSO regression algorithm, including ISH, SBP, DBP, triglyceride, Scr, and CCVD (Fig. 1). The corresponding screening variables in LASSO regression screening were shown in Fig. 1. Univariate logistic regression analysis in the training set revealed that ISH was significantly associated with a high risk of WCH [odds ratio (OR) = 2.411, 95% CI = 1.615–3.601, P < 0.001]. WCH depicted a significantly lower risk of CCVD than SHT (OR = 0.639, 95% CI = 0.419–0.975, P = 0.038). There were other clinical features of SHT, such as elevated SBP, DBP, triglyceride, and Scr (OR = 0.977, 95% CI = 0.964–0.992, P = 0.002; OR = 0.957, 95% CI = 0.939–0.974, P < 0.001; OR = 0.811, 95% CI = 0.687–0.957, P = 0.013; OR = 0.987, 95% CI = 0.975–0.999, P = 0.029, respectively). To identify WCH, the AUC of ISH, SBP, DBP, triglyceride, Scr, and CCVD were 0.608, 0.581, 0.652, 0.566, 0.555, and 0.549, respectively (Table 3).
Table 3 -
The univariate logistic regression analysis and discrimination test of the research variables involved in the model construction on distinguishing
white coat hypertension in the training set
| Parameters |
Univariate logistic regression |
AUC (95% CI) |
|
OR (95% CI) |
P value |
|
| ISH |
2.411 (1.615–3.601) |
<0.001 |
0.608 (0.553–0.663) |
| SBP |
0.977 (0.964–0.992) |
0.002 |
0.581 (0.526–0.635) |
| DBP |
0.957 (0.939–0.974) |
<0.001 |
0.652 (0.599–0.704) |
| TG |
0.811 (0.687–0.957) |
0.013 |
0.566 (0.512–0.621) |
| Scr |
0.987 (0.975–0.999) |
0.029 |
0.555 (0.500–0.611) |
| CCVD |
0.639 (0.419–0.975) |
0.038 |
0.549 (0.494–0.605) |
;)
Fig. 1: The result of LASSO regression analysis of variable screening in the training set. (a) The optimal parameter (lambda) selection in the LASSO model using cross-validation via 1-SE criteria. Cross-validation was performed 10 times to select the optimal parameters (lambda) of the LASSO model. The partial likelihood deviance is plotted against log (λ), where λ is the tuning parameter. Red dots indicate average deviance values for each model with a given λ, and partial likelihood deviance values are shown, with error bars representing standard error. The dotted vertical lines are plotted at the value selected using the 10-fold cross-validation and 1-SE criteria. The upper horizontal axis indicates the number of selected variables, while the lower horizontal axis represents log (λ). The left vertical dotted line indicates that 11 variables were selected by adopting the minimum penalty parameter standard. In contrast, the right vertical dotted line indicates that six variables were selected when one standard error (1-SE criteria) was adopted. (b) The LASSO coefficient profiles of the features. In the LASSO algorithm, with the change of lambda, the trajectory of each WCH-related characteristic coefficient is observed in the LASSO coefficient profile. Independent variable coefficients are expressed as the ordinates. The lower abscissa represents the Log lambda and the upper abscissa indicates the number of variables with non-zero coefficients in the model. LASSO, least absolute shrinkage and selection operator; SE, standard error; WCH, white coat hypertension.
Model construction and nomogram drawing
Multivariate logistic regression was used to construct the scoring model and draw the nomogram. In clinical applications, ISH and other variables can be scored individually, and the total score can be calculated by adding the individual scores. The likelihood of WCH in individuals with elevated office blood pressure can be known by comparing it with the nomogram (Fig. 2).
Fig. 2: The nomogram for differential diagnosis of WCH and SHT. To use the nomogram, an individual participant’s value is located on each variable axis, and a line is drawn upward to determine the number of points received for each variable value; add the points from all the variables, and a line is drawn from the total points axis to determine the possibility of WCH at the lower line of the nomogram. SHT, sustained hypertension; WCH, white coat hypertension.
Discrimination degree and calibration degree
The discrimination degree of the scoring model was statistically significant in both the training and validation sets (P < 0.001, <0.001, respectively). The R2 and AUC of the scoring model in the training set were 0.163 and 0.705 (95% CI = 0.656–0.754, Fig. 3a), respectively. In the validation set, the R2 and AUC of the scoring model were 0.206 and 0.718, respectively (95% CI = 0.621–0.814, Fig. 3b). The model had a certain discrimination degree in both the training and validation sets, and the discrimination degrees in the training and the validation sets were comparable.
Fig. 3: The discrimination test and calibration test of the scoring model. (a and b) The discrimination of the scoring model in the training and validation sets, respectively. (c and d) The calibration degree of the scoring model in the training and validation sets, respectively.
The calibration test results demonstrated that the scoring model was stable in both the training and validation sets (MSE = 0.001, MAE = 0.014, Fig. 3c; MSE = 0.001, MAE = 0.025, Fig. 3d, respectively).
Discussion
Clinically identifying WCH during initial diagnosis is currently problematic. Currently, some clinicians empirically distinguish WCH from SHT by combining WCH with the high incidence of ISH and minimal damage to target organs and SHT with an increased incidence in men and smokers [14,31]. However, it is impossible to scientifically and quantitatively assess the risk of WCH. Presently, a scientific scoring model for differential diagnosis of WCH and SHT is unavailable. In this study, a scoring model for differential diagnosis of WCH and SHT was developed.
LASSO regression analysis was initially used to screen research variables in this modeling process. Presently, LASSO regression is a relatively accurate model variable screening method. In contrast to other variable screening methods, such as ridge regression, LASSO regression can screen variables and adjust complexity, effectively avoiding model overfitting [32]. In this study, 17 research variables, like gender and age, were included in the LASSO regression model. All the candidate variables were clinically readily accessible, including basic information such as sex, age, office blood pressure, and biochemical test results such as blood lipid and renal function.
A total of six variables contributing to the model construction were screened by LASSO regression, namely ISH, SBP, DBP, triglyceride, Scr, and CCVD; these reflected the clinical characteristics of an individual’s blood pressure, blood lipids, renal function, and target organ injury. For a long time, ISH has been believed to be closely related to WCH. In 2019, Feitosa et al. [14] verified again that WCH has the clinical feature of increasing ISH proportion at different age groups . The LASSO regression analysis also suggested that ISH is positively correlated with WCH. ARTEMIS research involving 27 countries around the world found that SHT is more prevalent than WCH in patients with hyperlipidemia and renal insufficiency [33]. In this study, triglyceride and Scr were also positively correlated with SHT.
European Hypertension Practice Guide (2018 Edition) suggested that individuals with grade 1 hypertension or elevated office blood pressure without obvious target organ damage should be suspected of having WCH, and ambulatory blood pressure monitoring should be performed to confirm the diagnosis [34]. In other words, the likelihood of WCH is assessed based on blood pressure level and target organ damage. Additionally, the scoring model is an improvement over the European hypertension guidelines. Finally, we may rely not only on office blood pressure and CCVD information but also on the combination of additional parameters. First, the scoring model reflected the clinical characteristics of a high proportion of ISH in WCH and the clinical characteristics of relatively high triglyceride and Scr in SHT.
This study used ISH, SBP, DBP, triglyceride, Scr, and CCVD to develop a differential diagnosis model in the training set. The results revealed that these variables alone could not discriminate WCH, and their AUC values were 0.608, 0.581, 0.652, 0.566, 0.555, and 0.549, respectively. The development of the differential diagnosis model can improve the discrimination ability of WCH. The AUC of the model applied to the training, and the validation sets were 0.705 and 0.718, respectively, which was significantly improved compared to the ability of univariate WCH identification. Moreover, the AUC values of the training and validation sets were relatively similar, indicating that the model is fairly robust [35]. Further calibration test results demonstrated that the model is stable in both the training and validation sets.
This study developed a scoring model for differential diagnosis of WCH and SHT in individuals with normal renal function. The scoring model had a good degree of stability and discrimination. It scientifically and quantitatively evaluated the possibility of WCH at the initial diagnosis, which is useful for identifying WCH in clinical practice.
Summary
What is known
- The differential diagnosis of WCH and SHT is of great significance.
- The initial identification of WCH and SHT has always been a difficult challenge in clinical practice.
What this study adds
- This study developed a scoring model for differential diagnosis between WCH and SHT.
- This scoring model depicted a high degree of stability and discrimination.
AUC, areas under the ROC curve; CCVD, cardiovascular and cerebrovascular diseases; CI, confidence interval; DBP, office diastolic blood pressure; ISH, isolated systolic hypertension; OR, odds ratio; SBP, office systolic blood pressure; Scr, serum creatinine; TG, triglyceride.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 81860643).
Conflicts of interest
There are no conflicts of interest.
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