Department of Cardiovascular Medicine, Affiliated Fuzhou First Hospital of Fujian Medical University, Fuzhou, Fujian, China.
The Third Clinical Medical College, Fujian Medical University, Fuzhou, Fujian, China.
Medicine (Baltimore). 2023 Dec 22;102(51):e36659. doi: 10.1097/MD.0000000000036659.
The goal of our study was to create a nomogram to predict the risk of developing hypertension in patients with periodontitis. Our study used data from a total of 3196 subjects from the National Health and Nutrition Examination Survey 2009 to 2014 who had ever been diagnosed with periodontitis. The data set was randomly divided into a training set and a validation set according to a 7:3 ratio. The data from the training set was utilized to build the prediction model, while the validation set were used to validate the model. To identify the risk variables, stepwise regression was used to perform successive univariate and multivariate logistic regression analysis. The predictive ability of the nomogram model was evaluated using receiver operating characteristic curve. Calibration plots were used to assess the consistency of the prediction model. The clinical value of the model was evaluated using decision curve analysis and clinical impact curve. A nomogram for the risk of hypertension in subjects with periodontitis was constructed in accordance with the 8 predictors identified in this study. The areas under the receiver operating characteristic curve values for the training set and validation set were 0.922 (95% confidence interval: 0.911-0.933) and 0.918 (95% confidence interval: 0.900-0.935), respectively, indicating excellent discrimination. The decision curve analysis and clinical impact curve suggested that the model has significant clinical applications, and the calibration plots of the training set and validation set demonstrated good consistency. The nomogram can effectively predict the risk of hypertension in patients with periodontitis and help clinicians make better clinical decisions.
本研究旨在建立一个列线图模型，以预测患有牙周炎的患者发生高血压的风险。我们的研究使用了 2009 年至 2014 年期间曾被诊断患有牙周炎的 3196 名来自国家健康和营养检查调查（NHANES）的受试者的数据。数据集根据 7:3 的比例随机分为训练集和验证集。从训练集中的数据用于建立预测模型，而验证集则用于验证模型。为了确定风险变量，逐步回归用于进行连续的单变量和多变量逻辑回归分析。使用接收者操作特征曲线评估列线图模型的预测能力。校准图用于评估预测模型的一致性。使用决策曲线分析和临床影响曲线评估模型的临床价值。根据本研究中确定的 8 个预测因素，为患有牙周炎的受试者构建了高血压风险的列线图。训练集和验证集的受试者工作特征曲线下面积分别为 0.922（95%置信区间：0.911-0.933）和 0.918（95%置信区间：0.900-0.935），表明具有出色的区分能力。决策曲线分析和临床影响曲线表明该模型具有显著的临床应用价值，且训练集和验证集的校准图显示出良好的一致性。该列线图可以有效地预测患有牙周炎的患者发生高血压的风险，有助于临床医生做出更好的临床决策。
