儿童癌症幸存者会经历相互关联的症状，其模式可以通过网络分析来阐明。然而，当前的症状网络是根据平均幸存者构建的，没有考虑个体异质性。我们建议通过模拟使用带有协变量的伊辛模型来评估个人症状网络估计，并估计成年儿童癌症幸存者的个人症状网络。我们采用带有协变量的伊辛模型来构建网络，通过逻辑回归来估计二元症状之间的关联。模拟实验评估了该方法在构建个人症状网络方面的稳健性。真实世界的数据说明包括来自圣裘德终身队列研究 (SJLIFE) 的 1708 名成年儿童癌症幸存者，这是一项回顾性队列研究，通过前瞻性随访来描述儿童癌症幸存者的病因和后期影响。患者10个领域的基线症状（心脏、肺部、感觉、恶心、运动、疼痛、记忆、疲劳、焦虑、抑郁）和个人特征（年龄、性别、种族/民族、受教育程度、个人收入和婚姻状况）使用调查进行自我报告。治疗变量（任何化疗或放射治疗）是从医疗记录中获得的。使用 Ising 模型估计 10 个领域的个人症状网络，结合个体特征和治疗数据。模拟证实了 Ising 模型与协变量在构建个人症状网络方面的稳健性。现实世界的数据分析确定年龄、性别、种族/民族、教育、婚姻状况和治疗（任何化疗和放疗）是影响症状同时发生的主要因素。年龄较大的儿童癌症幸存者表现出更强的心脏疲劳关联。少数种族/族裔的幸存者有更强的疼痛-疲劳关联。受过大学以上教育的女性幸存者表现出更强的疼痛与焦虑关联。接受辐射的未婚幸存者的运动和记忆问题之间存在更强的关联。带有协变量的伊辛模型可以准确估计个人症状网络。儿童癌症幸存者的症状共现模式存在个体异质性。估计的个人症状网络提供了对相互关联的症状体验的见解。© 2024 Zhou 等人。

Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors.We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data.Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems.The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.© 2024 Zhou et al.