Objectives 

This study aimed to establish a scoring model for the differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT).

Methods 

This study comprised 553 adults with elevated office blood pressure, normal renal function, and no antihypertensive medications. Through questionnaire investigation and biochemical detection, 17 parameters, such as gender and age, were acquired. WCH and SHT were distinguished by 24 h ambulatory blood pressure monitoring. The participants were randomly divided into a training set (445 cases) and a validation set (108 cases). The above parameters were screened using least absolute shrinkage and selection operator regression and univariate logistic regression analysis in the training set. Afterward, a scoring model was constructed through multivariate logistic regression analysis.

Results 

Finally, six parameters were selected, including isolated systolic hypertension, office systolic blood pressure, office diastolic blood pressure, triglyceride, serum creatinine, and cardiovascular and cerebrovascular diseases. Multivariate logistic regression was used to establish a scoring model. The R² and area under the ROC curve (AUC) of the scoring model in the training set were 0.163 and 0.705, respectively. In the validation set, the R² of the scoring model was 0.206, and AUC was 0.718. The calibration test results revealed that the scoring model had good stability in both the training and validation sets (mean square error = 0.001, mean absolute error = 0.014; mean square error = 0.001, mean absolute error = 0.025).

Conclusion 

A stable scoring model for distinguishing WCH was established, which can assist clinicians in identifying WCH at the first diagnosis.

中文翻译：

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白大衣高血压与持续性高血压鉴别诊断评分模型的建立

目标 

本研究旨在建立白大衣高血压（WCH）和持续性高血压（SHT）鉴别诊断的评分模型。

方法 

这项研究包括 553 名诊室血压升高、肾功能正常且未服用抗高血压药物的成年人。通过问卷调查和生化检测，获得性别、年龄等17个参数。通过24小时动态血压监测来区分WCH和SHT 。参与者被随机分为训练集（445 例）和验证集（108 例）。在训练集中使用最小绝对收缩和选择算子回归以及单变量逻辑回归分析来筛选上述参数。然后，通过多元逻辑回归分析构建评分模型。

结果 

最终筛选出单纯收缩期高血压、诊室收缩压、诊室舒张压、甘油三酯、血肌酐、心脑血管疾病等6个参数。采用多元逻辑回归建立评分模型。训练集中评分模型的R ²和ROC曲线下面积（AUC）分别为0.163和0.705。在验证集中，评分模型的R ²为0.206，AUC为0.718。校准测试结果表明，评分模型在训练集和验证集上均具有良好的稳定性（均方误差=0.001，平均绝对误差=0.014；均方误差=0.001，平均绝对误差=0.025）。

结论 

建立了区分WCH的稳定评分模型，可以帮助临床医生在首次诊断时识别WCH。