Data Sheet 1_Prediction models for fear of cancer recurrence in adults with cancer: a systematic review.pdf

BackgroundNumerous studies have developed or validated prediction models to estimate the likelihood of Fear of Cancer Recurrence (FCR) among patients with cancer. The quality of these models and evaluations of their applicability to clinical practice and future research remain unclear. This study systematically evaluated the risk of bias and applicability of prediction models for FCR in oncology populations.
MethodsWe searched PubMed, Embase, Web of Science, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Data, and Chinese Biomedical Literature Database (CBM) from inception to August 1, 2025. Two reviewers independently screened studies and extracted data. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) checklist to assess risk of bias and applicability.
ResultsWe identified 702 records and ultimately included 7 studies encompassing 18 models. Most were published between 2022 and 2025. Sample sizes in the included studies ranged from 200 to 918, and the reported discrimination in model development cohorts ranged from 0.660 to 0.996. Applicability was judged to be good across all studies. However, all studies exhibited a high risk of bias, mainly due to suboptimal data sources, inadequate handling of predictors and missing data, and limited model validation. Among the included studies, the prevalence of FCR ranged from 47.7% to 63.4%. The most frequent predictors were age, social support, household/per-capita monthly income, occupation or employment status, and fatigue.
ConclusionsPrediction modeling for FCR in patients with cancer remains in an early stage, with both shared and heterogeneous predictors. Although overall model performance appears acceptable, most studies have methodological shortcomings, and few models have been validated. Future studies should design, conduct, and report models in accordance with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. In addition, studies with larger sample sizes and multicenter external validation are needed to improve model robustness.

背景：既往多项研究已开发或验证了用于评估癌症患者癌症复发恐惧（Fear of Cancer Recurrence, FCR）发生概率的预测模型，但此类模型的质量及其在临床实践与未来研究中的应用适用性仍尚不明确。本研究对肿瘤人群中针对FCR的预测模型的偏倚风险与应用适用性开展了系统评价。方法：本研究检索了PubMed、Embase、Web of Science、Cochrane图书馆、护理及相关健康文献累积索引（Cumulative Index to Nursing and Allied Health Literature, CINAHL）、中国知网（China National Knowledge Infrastructure, CNKI）、中国科技期刊数据库（VIP）、万方数据及中国生物医学文献数据库（China Biomedical Literature Database, CBM），检索时限从建库至2025年8月1日。由2名评价员独立完成文献筛选与数据提取工作，采用预测模型偏倚风险评估工具（Prediction model Risk Of Bias ASsessment Tool, PROBAST）清单对偏倚风险及应用适用性进行评价。结果：本研究共检索到702条文献记录，最终纳入7项研究，涵盖18个预测模型。其中大部分研究发表于2022至2025年。纳入研究的样本量范围为200至918例，模型开发队列中报告的区分度取值范围为0.660至0.996。所有研究的应用适用性均被评定为良好，但所有研究均存在较高的偏倚风险，主要源于数据来源欠优化、预测变量与缺失数据处理不充分，以及模型验证环节存在局限性。纳入研究中FCR的患病率范围为47.7%至63.4%。最常被纳入的预测变量包括年龄、社会支持、家庭/人均月收入、职业或就业状态以及疲劳症状。结论：针对癌症患者FCR的预测模型研究仍处于早期阶段，其预测变量既有共性也存在异质性。尽管整体模型性能尚可，但多数研究存在方法学缺陷，且仅有少数模型经过了验证。未来的研究应遵循《个体预后或诊断多变量预测模型透明报告指南》（Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis, TRIPOD）开展模型的设计、实施与报告工作。此外，还需开展大样本量及多中心外部验证研究，以提升模型的稳健性。