Department of Cardiovascular Medicine, Affiliated Fuzhou First Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Fujian Medical University, The Third Clinical Medical College, Fuzhou, Fujian, China.
PLoS One. 2023 Apr 5;18(4):e0284113. doi: 10.1371/journal.pone.0284113. eCollection 2023.
Depression is of increasing concern as its prevalence increases. Our study's objective was to create and evaluate a nomogram to predict the likelihood that hypertension patients may experience depression. 13293 people with hypertension who were under 20 years old were chosen from the National Health and Nutrition Examination Survey (NHANES) database between 2007 and 2018 for this study. The training and validation sets were split up into the dataset at random in a 7:3 ratio. To find independent predictors, univariate and multivariate logistic regression were employed on the training set. Using information from the validation set, nomogram was subsequently created and internally validated. The effectiveness of the nomogram is assessed using calibration curve and receiver operator characteristic (ROC) curve. Combining univariate logistic regression analysis and multifactor logistic regression analysis, the results showed that age, sex, race, marital, education level, sleep time on workdays, poverty to income ratio, smoking, alcohol consumption, sedentary time and heart failure status were risk factors for hypertensive patients suffering from depression and were included in the nomogram model, and ROC analysis showed that the AUC of the training set was 0.757 (0.797-0.586), with a sensitivity of 0.586; the AUC of the test set was 0.724 (0.712-0.626), with a sensitivity of 0.626, which was a good fit. Decision curve analysis further confirms the value of nomogram for clinical application. In the civilian non-institutionalized population of the United States, our study suggests a nomogram that can aid in predicting the likelihood of depression in hypertension patients and aiding in the selection of the most effective treatments.
随着抑郁症患病率的增加，其受到的关注日益增加。本研究旨在创建和评估一个列线图，以预测高血压患者发生抑郁的可能性。本研究从 2007 年至 2018 年的国家健康和营养检查调查（NHANES）数据库中选择了 13293 名年龄在 20 岁以下的高血压患者。通过随机将数据集分为训练集和验证集，将数据集分为 7:3 的比例。为了找到独立的预测因素，在训练集中使用单变量和多变量逻辑回归。使用验证集的信息，随后创建并内部验证了列线图。使用校准曲线和接收器工作特征（ROC）曲线评估列线图的有效性。通过单变量逻辑回归分析和多因素逻辑回归分析相结合，结果表明，年龄、性别、种族、婚姻状况、教育程度、工作日睡眠时间、贫困与收入比、吸烟、饮酒、久坐时间和心力衰竭状态是高血压患者患抑郁症的危险因素，并被纳入列线图模型，ROC 分析显示，训练集的 AUC 为 0.757（0.797-0.586），灵敏度为 0.586；验证集的 AUC 为 0.724（0.712-0.626），灵敏度为 0.626，拟合度较好。决策曲线分析进一步证实了列线图在临床应用中的价值。在美国的平民非机构化人群中，我们的研究提出了一个列线图，可以帮助预测高血压患者患抑郁症的可能性，并帮助选择最有效的治疗方法。
