Construction of a nomogram identification model for the risk of bipolar depression
10.3969/j.issn.1000-6729.2025.07.01
- VernacularTitle:双相抑郁风险的列线图识别模型构建
- Author:
Yongyan DENG
1
;
Xiaoyi TIAN
;
Tingting ZHANG
;
Peilin XU
;
Jiana MUHAI
;
Liang ZHOU
;
Yueqin HUANG
;
Zhaorui LIU
Author Information
1. 北京大学第六医院,北京大学精神卫生研究所,国家卫生健康委员会精神卫生学重点实验室(北京大学),国家精神心理疾病临床医学研究中心(北京大学第六医院),北京 100083
- Publication Type:Journal Article
- Keywords:
bipolar disorder;
bipolar depression;
unipolar depression;
nomogram
- From:
Chinese Mental Health Journal
2025;39(7):577-584
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the differences in sociodemographic and clinical characteristics between pa-tients with unipolar depression bipolar depression and to establish a nomogram for identifying bipolar depression.Methods:Using data from the China Mental Disorders Cohort Study,the sociodemographic and clinical characteristics of 2 643 patients with unipolar depression and 250 patients with bipolar depression diagnosed accord-ing to the criteria of the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)were includ-ed to compare their sociodemographic and clinical characteristics.These characteristics included general demograph-ic information,disease-related information,clinical examination results,and the severity of the disease assessed with the Global Assessment of Functioning(GAF)and Hamilton Depression Rating Scale.Logistic regression analysis was employed to identify factors influencing bipolar depression,and a nomogram was constructed for its identifica-tion.Results:The risk factors for bipolar depression included being male(OR=1.48),being employed(OR=1.38),having non-melancholic features during episodes(OR=2.33),a Body Mass Index ranging from normal to obese(OR=2.48,2.49,4.65),psychotic features(OR=2.14),mixed episode(OR=9.36),comorbid physical diseases(OR=2.47),four or more depressive episodes(OR=1.67),earlier age of onset(OR=0.95),longer ill-ness duration(OR=1.03),and higher GAF scores(OR=1.02).The nomogram model achieved an AUC of 0.81(95％CI:0.78-0.84).The Hosmer-Lemeshow test result was x2=6.96(P＞0.05),indicating good model fit.The calibration curve showed good performance.The decision curve analysis revealed that the nomogram pro-vides significant clinical benefit when the risk of bipolar depression was within the range of 0 to 0.9.Conclusion:The nomogram established based on the identified sociodemographic and clinical factors can accurately assess the risk of bipolar depression,providing a useful tool for early identification and intervention.
