卵巢癌患者癌症相关性认知功能障碍预测模型构建

Construction of a predictive model for cancer-related cognitive dysfunction among ovarian cancer patients

目的构建卵巢癌患者癌症相关性认知功能障碍(CRCI)预测模型。方法选择2022年4月—2023年12月本院妇科收治的100例卵巢癌患者作为研究对象,根据患者癌症功能评估—认知功能量表的得分情况,将患者分为CRCI组(43例)和无CRCI组(57例)两组。收集患者的年龄、文化水平、既往慢性疾病史、月经状态、绝经时间、临床分期的相关资料,使用癌症治疗功能评估—疲乏量表(FACIT-F)、匹兹堡睡眠质量指数量表(PSQI)、焦虑自评量表(SAS)对患者疲乏、睡眠质量、焦虑情况进行调查,通过单因素分析和Logistic回归分析筛选出卵巢癌患者出现CRCI的相关影响因素,并建立风险预测模型。结果单因素分析及Logistic回归分析结果显示,年龄、文化水平、月经状态、绝经时间、疲乏情况、睡眠质量、焦虑情况均是卵巢癌患者发生CRCI的独立危险因素(P<0.05),根据上述因素构建的模型敏感度为93.0%,特异性为87.7%,曲线下面积为0.931,约登指数为0.807。Hosemer-Lemeshow拟合优度检验,χ^(2)=2.042,P=0.360,校准曲线的平均绝对误差为0.039,校正曲线总体趋势趋近于理想曲线。结论卵巢癌患者CRCI发生率处于较高水平,依据年龄、文化水平、月经状态、绝经时间、疲乏情况、睡眠质量、焦虑情况构建的预测模型具有良好预测性,可为识别卵巢癌患者CRCI发生情况提供参考。

Objective To construct a prediction model for cancer-related cognitive dysfunction(CRCI)among ovarian cancer patients.Methods One hundred patients with ovarian cancer admitted to the Department of Gynaecology at First People's Hospital of Jiujiang from April 2022 to December 2023 were selected as subjects for this study.Patients were divided into CRCI and non-CRCI groups based on their scores on the Functional Assessment of Cancer Therapy-Cognitive Function Scale.Data on patient age,education level,history of chronic diseases,menstrual status,time since menopause,and clinical stage were collected.The fatigue,sleep quality,and anxiety levels of the patients were assessed using the Functional Assessment of Cancer Therapy-Fatigue Scale(FACIT-F),Pittsburgh Sleep Quality Index(PSQI),and Self-Rating Anxiety Scale(SAS),respectively.Univariate analysis and logistic regression analysis were conducted to identify factors associated with CRCI in ovarian cancer patients and to establish a risk prediction model.Results Univariate and logistic regression analyses revealed that age,education level,menstrual status,time since menopause,fatigue,sleep quality,and anxiety are independent risk factors for CRCI in ovarian cancer patients(P<0.05).The predictive model,based on these factors,demonstrated a sensitivity of 93.0%,specificity of 87.7%,area under the curve of 0.931,and Youden's index of 0.807.The Hosmer-Lemeshow goodness-of-fit test resulted in a chi-square value of 2.042,P=0.360,with a mean absolute error of the calibration curve at 0.039,indicating that the calibration curve's overall trend was close to the ideal curve.Conclusions The incidence of CRCI in ovarian cancer patients is high.The predictive model,constructed based on age,education level,menstrual status,time since menopause,fatigue,sleep quality,and anxiety,exhibits good predictive performance and can serve as a reference for identifying the occurrence of CRCI in ovarian cancer patients.