Guo Junchen, Dai Yunyun, Jiang Sishan, Liu Junqingzhao, Xu Xianghua, Chen Yongyi
Department of Palliative Care, Hunan Cancer Hospital, No.283, Tongzipo Road, Yuelu District, Changsha, Hunan, 410006, China.
School of Nursing, University of Wollongong, Wollongong, NSW, 2522, Australia.
BMC Palliat Care. 2025 May 24;24(1):148. doi: 10.1186/s12904-025-01785-4.
Developing an accurate predictive model for palliative care phases is crucial for improving cancer patient management, enabling healthcare providers to identify those in need of specific care plans and streamlining decision-making process for patients and caregivers. This study aims to identify symptom and functional indicators from Palliative Care Outcomes Collaboration (PCOC) data and develop a predictive model capable of accurately categorizing palliative care phases in advanced cancer patients.
A retrospective cohort study design was adopted in this study. Data on PCOC information were collected and analyzed from patients admitted to a palliative care unit at a cancer hospital in China between April 2023 and December 2024. The Gradient Boosting Decision Tree in the machine learning algorithm to establish a palliative care phase prediction model and evaluated the prediction performance of this model.
A total of 9,787 assessments from 793 patients were included in the analysis of this study. Significant differences were identified among the four PCOC phases of care in terms of the symptom distress, palliative care problem severity, functional status and daily living activities. The machine learning model developed in this study achieved areas under the curve (AUCs) of 0.997, 0.996, 0.999, and 0.999 for predicting the stable, unstable, deteriorating, and terminal phases in the training group, respectively. In the testing group, the corresponding AUCs were 0.976, 0.965, 0.971, and 0.998.
The prediction model developed in this study based on the machine learning algorithm showed good performance, offering significant potential for facilitating timely interventions, enhancing symptom management, and optimizing palliative care resource allocation in advanced cancer patients in mainland China.
开发一个准确的姑息治疗阶段预测模型对于改善癌症患者管理至关重要，这能使医疗服务提供者识别出需要特定护理计划的患者，并简化患者及其照护者的决策过程。本研究旨在从姑息治疗结果协作组织（PCOC）的数据中识别症状和功能指标，并开发一个能够准确对晚期癌症患者的姑息治疗阶段进行分类的预测模型。
本研究采用回顾性队列研究设计。收集并分析了2023年4月至2024年12月期间在中国一家癌症医院姑息治疗科住院患者的PCOC信息数据。使用机器学习算法中的梯度提升决策树建立姑息治疗阶段预测模型，并评估该模型的预测性能。
本研究分析共纳入了793例患者的9787次评估。在症状困扰、姑息治疗问题严重程度、功能状态和日常生活活动方面，四个PCOC护理阶段之间存在显著差异。本研究开发的机器学习模型在训练组中预测稳定、不稳定、恶化和终末期阶段的曲线下面积（AUC）分别为0.997、0.996、0.999和0.999。在测试组中，相应的AUC分别为0.976、0.965、0.971和0.998。
本研究基于机器学习算法开发的预测模型表现良好，在促进中国大陆晚期癌症患者的及时干预、加强症状管理和优化姑息治疗资源分配方面具有巨大潜力。
