目的:了解消化道肿瘤患者对癌症复发的恐惧发生率,分析其影响因素,并进一步建立可视化风险预测模型。
方法:横断面研究。
方法:对当地医院收治的570例消化道肿瘤患者进行横断面调查,从2023年5月到2023年12月,采用方便的抽样方法。对影响因素进行单因素分析和logistic分析,利用R4.1.3软件构建消化道肿瘤患者害怕癌症复发的风险预测列线图模型。ROC曲线用于评估列线图模型的差异。使用校准曲线和Hosmer-Lemeshow拟合优度检验来评估模型的一致性。本研究使用TRIPOD检查表报告。
结果:在这项研究中,272例(47.7%)患者担心复发。消化道癌症患者复发恐惧柱状图的风险预测模型纳入了性别、治疗,消化道出血,疼痛,抑郁症和社会支持。C统计量为(.976),校准曲线表明,预测概率更符合实际发生概率,决策曲线表明,该预测模型具有较好的实用性。
结论:本研究构建的列线图预测模型是有效的,并有助于医疗保健专业人员根据其风险因素进行及时的干预和管理。
结论:列线图有助于计算消化道肿瘤患者FCR的风险概率,及时识别FCR患者,制定全面、个性化的对策,为消化道肿瘤患者提供良好的生活质量,延长生存周期。
■参与者为住院患者或接受随访的消化道癌症患者。首先,在调查和研究之前,成立了一个团队来讨论这个概念,研究目的,方法,意义,等。,并确定研究工具。第二,通过向患者合理解释研究,以寻求患者的知情同意并签署,患者独立填写问卷。对于文化程度低、无法填写问卷的患者,团队成员做出了客观的解释,以帮助他们选择合理的选择。
OBJECTIVE: To investigate the incidence of fear of cancer recurrence in patients with digestive tract cancers analyse its influencing factors, and further establish a visual risk prediction model.
METHODS: A cross-sectional study.
METHODS: A cross-sectional survey was conducted among 570 patients with digestive tract tumours admitted to a local hospital, from May 2023 to December 2023 by convenient sampling method. Univariate analysis and logistic analysis were performed on the influencing factors, and the risk prediction nomogram model of fear of cancer recurrence in patients with digestive tract cancer was constructed by using R 4.1.3 software. ROC curve was used to evaluate the differentiation of the nomogram model. The calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the consistency of the model. This study was reported using the TRIPOD checklist.
RESULTS: In this study, 272 (47.7%) patients developed fear of recurrence. The risk prediction model of recurrence fear column chart for digestive tract cancer patients incorporated six variables of gender, therapy, alimentary tract haemorrhage, pain, depression and social support. The C-statistic was (.976), and the calibration curve showed that the predicted probability was more in line with the actual probability of occurrence, and the decision curve showed that the predictive model had better practicality.
CONCLUSIONS: The column-line diagram prediction model constructed in this study is effective and facilitates timely intervention and management by healthcare professionals based on their risk factors.
CONCLUSIONS: Nomogram is helpful to calculate the risk probability of FCR in patients with digestive tract cancer, identify FCR patients in time, and formulate comprehensive and personalized countermeasures, to provide a good quality of life and prolong the survival cycle of patients with digestive tract cancer.
UNASSIGNED: Participants were hospitalized patients or patients with digestive tract cancer undergoing follow-up. First of all, before the investigation and research, a team is formed to discuss the concept, research purpose, method, significance, etc., and determine the research tools. Second, by reasonably explaining the study to patients to seek informed consent from the patient and sign it, patients filled in the questionnaire independently. For patients with low education levels who could not fill in the questionnaire, the team members made objective explanations to help them choose reasonable options.