Sun Di, Huang Tingting, Li Jiaojiao, Liu Meishuo, Zhang Xu, Cui Mengyao
School of Nursing, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Department of Head and Neck Surgery, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, Shenyang, China.
Psychooncology. 2025 Jul;34(7):e70236. doi: 10.1002/pon.70236.
Anticipatory grief is a significant emotional challenge for family caregivers of cancer patients, yet its early identification remains limited by subjective assessments and a lack of predictive tools. This study aimed to develop and validate a predictive model for anticipatory grief among family caregivers of cancer patients in China.
A multicenter cross-sectional study was conducted from February to October 2023, involving 642 family caregivers of lung and breast cancer patients from two tertiary hospitals in Liaoning Province, China. Latent Profile Analysis (LPA) classified caregivers into anticipatory grief risk categories based on the Anticipatory Grief Scale. LASSO-logistic regression was used to identify predictors and construct a predictive model, which was validated using discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis). A web-based nomogram was developed for practical application.
The mean anticipatory grief score was 72.44 ± 18.49, with LPA identifying three profiles: low (54.52%), moderate (30.53%), and high (14.95%) anticipatory grief. Seven predictors were identified: caregiver education level, monthly income, physical condition, caregiving duration, and patient cancer type, employment status, and time since diagnosis. The model showed good discrimination (AUC: 0.769 training, 0.671 validation), calibration (P = 0.095 training, P = 0.801 validation), and clinical utility (net benefit at 34%-62% threshold). The web-based tool is accessible at https://nomogrameofag.shinyapps.io/dynnomapp/.
This study developed a predictive model for anticipatory grief, identifying key risk factors and providing a practical tool for healthcare providers to identify high-risk caregivers. The findings support targeted interventions to enhance caregiver well-being and patient care quality, though future research should expand cancer types and incorporate qualitative insights for broader applicability.
预期性悲伤是癌症患者家庭照顾者面临的一项重大情感挑战，然而其早期识别仍受主观评估及缺乏预测工具的限制。本研究旨在开发并验证中国癌症患者家庭照顾者预期性悲伤的预测模型。
于2023年2月至10月进行了一项多中心横断面研究，纳入了来自中国辽宁省两家三级医院的642名肺癌和乳腺癌患者的家庭照顾者。潜在剖面分析（LPA）根据预期性悲伤量表将照顾者分为预期性悲伤风险类别。使用LASSO逻辑回归识别预测因素并构建预测模型，通过区分度（AUC）、校准度（Hosmer-Lemeshow检验）和临床实用性（决策曲线分析）对其进行验证。开发了一个基于网络的列线图以供实际应用。
预期性悲伤平均得分为72.44 ± 18.49，LPA识别出三种类型：低预期性悲伤（54.52%）、中度预期性悲伤（30.53%）和高预期性悲伤（14.95%）。识别出七个预测因素：照顾者教育水平、月收入、身体状况、照顾时长、患者癌症类型、就业状况以及确诊后的时间。该模型显示出良好的区分度（训练集AUC：0.769，验证集AUC：0.671）、校准度（训练集P = 0.095，验证集P = 0.801）和临床实用性（在34%-62%阈值下有净获益）。基于网络的工具可通过https://nomogrameofag.shinyapps.io/dynnomapp/访问。
本研究开发了预期性悲伤的预测模型，识别出关键风险因素，并为医疗保健提供者提供了一种识别高风险照顾者的实用工具。这些发现支持进行有针对性的干预，以提高照顾者的幸福感和患者护理质量，不过未来的研究应扩大癌症类型并纳入定性见解以实现更广泛的适用性。
