Yang Min, Zhang Huiqin, Yu Minglan, Xu Yunxuan, Xiang Bo, Yao Xiaopeng
School of Public Health, Southwest Medical University, No.1 Section 1, Xiang Lin Road, Longmatan District, Luzhou, 646000, P. R. China.
Institute of cardiovascular research, Southwest Medical University, No.1 Section 1, Xiang Lin Road, Longmatan District, Luzhou, 646000, P. R. China.
BMC Psychiatry. 2024 Dec 18;24(1):914. doi: 10.1186/s12888-024-06384-w.
Depression has emerged as a global public health concern with high incidence and disability rates, which are timely imperative to identify and intervene in clinical practice. The objective of this study was to explore the association between heart rate variability (HRV) and depression, with the aim of establishing and validating machine learning models for the auxiliary diagnosis of depression.
The data of 465 outpatients from the Affiliated Hospital of Southwest Medical University were selected for the study. The study population was then randomly divided into training and test sets in a 7:3 ratio. Logistic regression (LR), support vector machine (SVM), random forest (RF) and eXtreme gradient boosting (XGBoost) algorithm models were used to construct risk prediction models in the training set, and the model performance was verified in the test set. The four models were evaluated by the area under the receiver operating characteristic curve (ROC), calibration curve and the decision curve analysis (DCA). Furthermore, we employed the SHapley Additive exPlanations (SHAP) method to illustrate the effects of the features attributed to the model.
There were 237 people in the depressed group and 228 in the non-depressed group. In the training set (n = 325) and test set (n = 140), the area under of the curve(AUC) values of the XGBoost model are 0.92 [95% confidence interval (CI) 0.888,0.95] and 0.82 (95% CI 0.754,0.892)] respectively, which are higher than the other three models. The XGBoost model has excellent predictive efficacy and clinical utility. The SHAP method was ranked according to the importance of the degree of influence on the model, with age, heart rate, Standard deviation of the NN intervals (SDNN), two nonlinear parameters of HRV and sex considered to be the top 6 predictors.
We provided a feasibility study of HRV as a potential biomarker for depression. The proposed model based on HRV provides clinicians with a quantitative auxiliary diagnostic tool, which is assist to improving the accuracy and efficiency of depression diagnosis, and can also be utilized for the monitoring and prevention of depression.
抑郁症已成为全球公共卫生问题，发病率和致残率都很高，因此在临床实践中及时识别和干预至关重要。本研究的目的是探讨心率变异性（HRV）与抑郁症之间的关联，旨在建立并验证用于抑郁症辅助诊断的机器学习模型。
选取西南医科大学附属医院465例门诊患者的数据进行研究。然后将研究人群按照7:3的比例随机分为训练集和测试集。使用逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）和极端梯度提升（XGBoost）算法模型在训练集中构建风险预测模型，并在测试集中验证模型性能。通过受试者工作特征曲线（ROC）下面积、校准曲线和决策曲线分析（DCA）对这四种模型进行评估。此外，我们采用SHapley加法解释（SHAP）方法来说明模型特征的影响。
抑郁症组有237人，非抑郁症组有228人。在训练集（n = 325）和测试集（n = 140）中，XGBoost模型的曲线下面积（AUC）值分别为0.92 [95%置信区间（CI）0.888, 0.95]和0.82（95% CI 0.754, 0.892），高于其他三种模型。XGBoost模型具有出色的预测效能和临床实用性。SHAP方法根据对模型影响程度的重要性进行排序，年龄、心率、正常到正常间期标准差（SDNN）、HRV的两个非线性参数和性别被认为是前6个预测因素。
我们提供了一项关于HRV作为抑郁症潜在生物标志物的可行性研究。所提出的基于HRV的模型为临床医生提供了一种定量辅助诊断工具，有助于提高抑郁症诊断的准确性和效率，还可用于抑郁症的监测和预防。
