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.

IF 5.5 3区 医学 Q2 MEDICAL INFORMATICS
Jiang Yuan, Yinqing Yang, Yuan Li, Tumalisi Kayishaer, Jie Chu, Bingmei Zhu, Zhizhen Tao, Jinshu Han, Xinyu Zhao, Xiuying Hu, Chao Tian, Yi Yang
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Abstract

Background: Sleep 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.

Objective: This 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.

Methods: Prospective 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.

Results: Among 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."

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

女性乳腺癌化疗患者睡眠障碍和焦虑抑郁的轨迹分析:基于分组多轨迹模型和机器学习
背景:睡眠障碍和焦虑抑郁症状会显著影响乳腺癌化疗患者的生活质量和治疗依从性,同时也会增加疾病复发和死亡的风险。早期识别这些症状的发展轨迹和潜在原因对于为恶化的病例提供个性化干预至关重要。目的:采用基于分组的多轨迹模型(Multi-Trajectory Model, GBMTM)分析乳腺癌患者化疗期间睡眠障碍和焦虑抑郁症状的发展轨迹,并通过机器学习识别影响因素。方法:收集150例接受4个周期化疗的乳腺癌患者的前瞻性资料。采用GBMTM来识别不同的轨迹,分析中包括70个预测变量。使用最小绝对收缩和选择算子(LASSO)回归来选择变量的最优子集。比较了7种机器学习模型,并选择了最优模型,使用SHapley加性解释(SHapley Additive exPlanations)分析对结果进行解释。结果:在接受化疗的乳腺癌患者中,发现了两种不同的睡眠障碍和焦虑抑郁症状轨迹:“稳定组”(74.7%)和“恶化组”(25.3%)。SHAP分析显示,高水平的c反应蛋白、高尿酸、高血红蛋白、吸烟、浸润性癌(导管原位癌)、既往化疗、已婚、过去有规律的月经周期以及缺乏家族史是与“恶化组”相关的重要危险因素。结论:我们的研究结果表明,乳腺癌化疗患者的睡眠障碍和焦虑抑郁症状有两条明显的轨迹,大多数属于“稳定组”。应密切监测已确定的与“恶化组”相关的危险因素,以促进早期干预并改善患者预后。试验注册:该试验已于2023年11月15日在中国临床试验注册中心注册(试验ID: ChiCTR2300077667;可在https://www.chictr.org.cn/上获得)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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