Liu Yongcai, Zhou Jieru, Huang Yijuan, Yao Xin, Zhang Xiaoyu, Cai Jian, Jiang Haihong, Chen Wei, Li Haiyan
Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
The Second School of Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Asia Pac J Oncol Nurs. 2025 Dec 6;12:100741. doi: 10.1016/j.apjon.2025.100741. eCollection 2025 Dec.
To identify the longitudinal heterogeneous trajectories of frailty in older patients with prostate cancer 6 months after radical prostatectomy and to explore the predictors of different trajectories.
A longitudinal design was conducted, and a total of 248 patients were recruited at the urology department in a tertiary-grade A hospital between June 2024 and March 2025. Data on predictive variables were collected at baseline using General information questionnaire, Geriatric Nutritional Risk Index, Health Literacy Management Scale, Kessler Psychological Distress Scale, and Medical Outcome Study Social Support Survey. Frailty assessments were subsequently performed at four time points using Tilburg Frailty Indicator: hospital discharge, and 1-, 3-, and 6 months postoperatively. Growth Mixture Modeling was employed to examine frailty trajectories over time. Multiple logistic regression and decision tree models were used to explore the predictors of heterogeneous trajectories of frailty.
Three distinct trajectories of frailty were identified in old patients with prostate cancer: frailty rapid improvement group (54.4%), frailty progressive deterioration group (14.5%), and frailty persistent high group (31.1%). Multiple logistic regression analysis identified health literacy, nutritional status, psychological distress, and the presence of two or more comorbidities as independent predictors of frailty trajectories. The decision tree model further highlighted health literacy as the most influential predictor, followed by psychological distress, nutritional status, and social support.
Frailty trajectories in older prostate cancer patients exhibit substantial heterogeneity. These findings provide insights into the predictors of frailty in the process of diagnosis and treatment, and can be used in the clinical identification and monitoring of high-risk patients. Health care providers should develop personalized tailored to these predictive variables to mitigate frailty progression.
确定老年前列腺癌患者根治性前列腺切除术后6个月虚弱的纵向异质轨迹,并探索不同轨迹的预测因素。
采用纵向设计,于2024年6月至2025年3月在一家三级甲等医院泌尿外科招募了248例患者。使用一般信息问卷、老年营养风险指数、健康素养管理量表、凯斯勒心理困扰量表和医学结局研究社会支持调查在基线时收集预测变量的数据。随后使用蒂尔堡虚弱指标在四个时间点进行虚弱评估:出院时、术后1个月、3个月和6个月。采用生长混合模型来检查随时间变化的虚弱轨迹。使用多元逻辑回归和决策树模型来探索虚弱异质轨迹的预测因素。
在老年前列腺癌患者中确定了三种不同的虚弱轨迹:虚弱快速改善组(54.4%)、虚弱进行性恶化组(14.5%)和虚弱持续高位组(31.1%)。多元逻辑回归分析确定健康素养、营养状况、心理困扰以及存在两种或更多种合并症是虚弱轨迹的独立预测因素。决策树模型进一步强调健康素养是最有影响力的预测因素,其次是心理困扰、营养状况和社会支持。
老年前列腺癌患者的虚弱轨迹表现出显著的异质性。这些发现为诊断和治疗过程中虚弱的预测因素提供了见解,可用于临床识别和监测高危患者。医疗保健提供者应根据这些预测变量制定个性化方案,以减轻虚弱进展。