Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care

doi:10.1155/2021/9201235

科研成果详情

题名	Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care
作者	Hu, Cheng 1; Li, Qian 2; Shou, Ji 3; Zhang, Feng-xian 1; Li, Xia 4,5; Wu, Min 2; Xu, Meng-jing 1; Xu, Li 1
发表日期	2021-07-19
发表期刊	BIOMED RESEARCH INTERNATIONAL 影响因子和分区
语种	英语
原始文献类型	Article
其他关键词	SLEEP QUALITY INDEX ; CANCER-PATIENTS ; BREAST-CANCER ; OF-LIFE ; SYMPTOMS ; INVENTORY ; ANXIETY ; SCALE ; RELIABILITY ; MORTALITY
摘要	Objectives. Depression is highly prevalent in non-Hodgkin's lymphoma (NHL) patients undergoing chemotherapy. The social stress associated with malignancy induces neurovascular pathology promoting clinical levels of depressive symptomatology. The purpose of this study was to establish an effective depressive symptomatology risk prediction model to those patients. Methods. This study included 238 NHL patients receiving chemotherapy, 80 of whom developed depressive symptomatology. Different types of variables (sociodemographic, medical, and psychosocial) were entered in the models. Three prediction models (support vector machine-recursive feature elimination model, random forest model, and nomogram prediction model based on logistic regression analysis) were compared in order to select the one with the best predictive power. The selected model was then evaluated using calibration plots, ROC curves, and C-index. The clinical utility of the nomogram was assessed by the decision curve analysis (DCA). Results. The nomogram prediction has the most efficient predictive ability when 10 predictors are included (AUC=0.938). A nomogram prediction model was constructed based on the logistic regression analysis with the best predictive accuracy. Sex, age, medical insurance, marital status, education level, per capita monthly household income, pathological stage, SSRS, PSQI, and QLQ-C30 were included in the nomogram. The C-index was 0.944, the AUC value was 0.972, and the calibration curve also showed the good predictive ability of the nomogram. The DCA curve suggested that the nomogram had a strong clinical utility. Conclusions. We constructed a depressive symptomatology risk prediction model for NHL chemotherapy patients with good predictive power and clinical utility.
出版者	HINDAWI LTD
出版地	LONDON
ISSN	2314-6133
EISSN	2314-6141
卷号	2021 页码:9201235
DOI	10.1155/2021/9201235
页数	12
WOS类目	Biotechnology & Applied Microbiology ; Medicine, Research & Experimental
WOS研究方向	Biotechnology & Applied Microbiology ; Research & Experimental Medicine
WOS记录号	WOS:000683449200003
收录类别	SSCI ; SCIE ; PUBMED ; SCOPUS
URL	查看原文
PubMed ID	34337060
PMC记录号	PMC8313321
SCOPUSEID	2-s2.0-85112640848
通讯作者地址	[Li, Qian]Third Hosp Quzhou, Psychiat Dept, Quzhou, Zhejiang, Peoples R China.
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/3012
专题	附属第二医院_康复科第二临床医学院，附属第二医院、育英儿童医院
通讯作者	Li, Qian
作者单位	1.Third Hosp Quzhou, Qual Control Dept, Quzhou, Zhejiang, Peoples R China; 2.Third Hosp Quzhou, Psychiat Dept, Quzhou, Zhejiang, Peoples R China; 3.Third Hosp Quzhou, Nursing Dept, Quzhou, Zhejiang, Peoples R China; 4.Wenzhou Med Univ, Rehabil Dept, Affiliated Hosp 2, Wenzhou, Peoples R China; 5.Wenzhou Med Univ, Yuying Childrens Hosp, Wenzhou, Peoples R China
推荐引用方式 GB/T 7714	Hu, Cheng,Li, Qian,Shou, Ji,et al. Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care[J]. BIOMED RESEARCH INTERNATIONAL,2021,2021:9201235.
APA	Hu, Cheng., Li, Qian., Shou, Ji., Zhang, Feng-xian., Li, Xia., ... & Xu, Li. (2021). Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care. BIOMED RESEARCH INTERNATIONAL, 2021, 9201235.
MLA	Hu, Cheng,et al."Constructing a Predictive Model of Depression in Chemotherapy Patients with Non-Hodgkin's Lymphoma to Improve Medical Staffs' Psychiatric Care".BIOMED RESEARCH INTERNATIONAL 2021(2021):9201235.