摘要
目的构建基于临床特征及血液指标的单双相抑郁鉴别诊断模型。方法分别纳入单相抑郁和双相抑郁受试者,回顾性收集受试者的临床数据及血常规、血生化检测指标。将数据随机分为训练集和测试集。基于不同的特征组合在训练集中采用极端梯度提升法(eXtreme gradient boosting,XGBoost)进行分类模型训练,并在测试集上验证模型的性能,采用受试者操作特征(receiver operating characteristic,ROC)曲线下面积(area under curve,AUC)、敏感性、特异性和准确率进行模型效能评价,使用SHapley加法移植计算所选特征对单双相抑郁鉴别诊断的重要性。结果本研究共纳入1160例受试者,其中918例为单相抑郁,242例为双相抑郁。构建单双相抑郁分类模型,其中基于临床特征、血常规指标和生化指标的XGBoost模型的效能最佳,其AUC为0.889,敏感性为0.831,特异性为0.839,准确率为0.863。该模型中的主要特征包括病程、发病年龄、血清白蛋白、低密度脂蛋白、血钾浓度、白细胞计数、血小板/淋巴细胞比值和单核细胞。结论病程特征和血液学生物标志物等容易在临床中获得的指标可对单相抑郁与双相抑郁的鉴别诊断提供重要支持。
Objective This study aims to build a differential diagnosis model for unipolar and bipolar depression based on clinical features and blood indicators.Methods According to inclusion and exclusion criteria,participants with unipolar and bipolar depression were included,and clinical data and blood test indicators of the participants were extracted.The data were randomly divided into a training set and a testing set.Classification models were trained on the training set using extreme gradient boosting based on different feature combinations,and the performance of the models was validated on the testing set.Receiver operating characteristic(ROC),area under curve(AUC),sensitivity,specificity and accuracy were used to evaluate model performance.The SHapley additive explanations(SHAP)method was used to calculate the importance of selected features for the differential diagnosis of unipolar and bipolar depression.Results In the unipolar and bipolar depression classification model,the XGBoost model performs the best,with an AUC of 0.889,sensitivity of 0.831,specificity of 0.839,and accuracy of 0.863.The main features in this model include duration of illness,age of onset,albumin,low-density lipoprotein,blood potassium concentration,white blood cell count,platelet/lymphocyte ratio,and monocytes.Conclusion Duration of illness and hematological biomarkers,which are easily obtainable in clinical settings,can provide important support for the differential diagnosis of unipolar and bipolar depression.
作者
曾金坤
曹敏讷
刘丹丹
邓伟
ZENG Jinkun;CAO Minne;LIU Dandan;DENG Wei(Hangzhou Seventh People's Hospital,Affiliated Mental Health Center,School of Medicine,Zhejiang University,Hangzhou 310013,China)
出处
《中国神经精神疾病杂志》
CAS
CSCD
北大核心
2024年第3期129-134,共6页
Chinese Journal of Nervous and Mental Diseases
基金
科技创新2030项目(编号:2021ZD0200600)
杭州市生物医药和健康产业扶持科技专项(编号:2022WJC067)。
关键词
单相抑郁
双相抑郁
生物标志物
鉴别诊断
机器学习
Unipolar depression
Bipolar depression
Biomarkers
Differential diagnosis
Machine learning
