Evidence map›Paper›PMID 42381042›Full record

ArticleBMC medical informatics and decision making2026

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

Jiang Yuan, Yinqing Yang, Yuan Li, Tumalisi Kayishaer, Jie Chu, Bingmei Zhu, Zhizhen Tao, Jinshu Han, Xinyu Zhao, Xiuying Hu and 2 more

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jiang YuanSchool of Public Health, Chengdu University of Traditional Chinese Medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu, 611137, People's Republic of China.
Yinqing YangSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People's Republic of China.
Yuan LiSchool of Public Health, Chengdu University of Traditional Chinese Medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu, 611137, People's Republic of China.
Tumalisi KayishaerSchool of Public Health, Chengdu University of Traditional Chinese Medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu, 611137, People's Republic of China.
Jie ChuSchool of Medical, University of Electronic Science and Technology of China, Cheng Du, 610054, People's Republic of China.
Bingmei ZhuDivision of Internal Medicine, Institute of Integrated Traditional Chinese and Western Medicine, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
Zhizhen TaoSchool of Public Health, Chengdu University of Traditional Chinese Medicine, No. 1166, Liutai Avenue, Wenjiang District, Chengdu, 611137, People's Republic of China.
Jinshu HanSchool of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People's Republic of China.
Xinyu ZhaoSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People's Republic of China.
Xiuying HuInnovation Center of Nursing Research, Nursing Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Cheng Du, 610041, People's Republic Of China. huxiuying@scu.edu.cn.
Chao TianDepartment of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Cheng Du, 610041, People's Republic of China. hxtc2000@163.com.
Yi YangSchool of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, People's Republic of China. thehanyang@163.com.

Funding

Nursing Key Laboratory of Sichuan Province HLKF2023(Z)-1
6 · The paper itself

Abstract

backgroundSleep disorders and anxiety-depression symptoms can significantly impair the quality of life and treatment adherence among breast cancer patients undergoing chemotherapy, while also increasing the risk of disease recurrence and mortality. Early identification of the progression trajectory and underlying causes of these symptoms is essential for providing personalized interventions for deteriorating cases.

objectiveThis study aims to analyze the trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients during chemotherapy using a group-based Multi-Trajectory Model (GBMTM) and to identify influencing factors through machine learning.

methodsProspective data from 150 breast cancer patients undergoing four cycles of chemotherapy were collected. A GBMTM was employed to identify distinct trajectories, with 70 predictive variables included in the analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select the optimal subset of variables. Seven machine learning models were compared, and the optimal model was chosen for result interpretation using SHAP (SHapley Additive exPlanations) analysis.

resultsAmong breast cancer patients undergoing chemotherapy, two distinct trajectories of sleep disorders and anxiety-depression symptoms were identified: the "stable group" (74.7%) and the "deteriorating group" (25.3%). SHAP analysis revealed that high levels of C-reactive protein, high uric acid, high hemoglobin, smoking, presence of invasive cancer with ductal carcinoma in situ, prior chemotherapy, being married, regular menstrual cycles in the past, and lack of family history were significant risk factors associated with the "deteriorating group."

conclusionOur findings indicate two distinct trajectories of sleep disorders and anxiety-depression symptoms in breast cancer patients undergoing chemotherapy, with the majority falling into the "stable group". The identified risk factors associated with the "deteriorating group" should be closely monitored to facilitate early intervention and improve patient outcomes.

trial registrationThe trial was registered on Chinese Clinical Trial Registry (Trial ID: ChiCTR2300077667 on 15 November 2023; available at https://www.chictr.org.cn/ ).

Indexed as

AnxietyBreast NeoplasmsDepressionMachine LearningSleep Wake DisordersAdultAgedFemaleHumansMiddle AgedPredictive Learning ModelsProspective StudiesAnxiety-depressionBreast cancerGroup-based Multi-Trajectory ModelMachine learningSleep disorders

Identifiers

PMID42381042
PMCPMC13587310

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.