Sun Wenwen, Shen Jun, Sun Ru, Zhou Dan, Li Haihong
Department of Breast Surgery, the First People's Hospital of Lianyungang, the Affiliated Hospital of XuZhou Medical University, LianYunGang, Jiangsu, 222002, People's Republic of China.
Department of Nursing, the First People's Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, LianYunGang, Jiangsu, 222002, People's Republic of China.
Psychol Res Behav Manag. 2023 Sep 19;16:3883-3894. doi: 10.2147/PRBM.S425055. eCollection 2023.
In this study, we aim to establish and evaluate a predictive model for post-treatment anxiety state based on basic patient attributes and pre-treatment SAS scores, with the expectation that this model will guide clinical precision intervention.
Data were collected from 606 patients with breast cancer who underwent surgery at our hospital between January 1, 2015 and December 30, 2018 and 144 newly diagnosed patients with breast cancer who were admitted between June 1, 2019 and December 30, 2019, for a total of 750 patients with breast cancer. The relationship between SAS_A scores and prognosis was verified by analyzing patient baseline characteristics, follow-up data, pre-treatment self-rating anxiety scale (SAS) scores, and SAS_A scores in follow-up period after the end of treatment. A risk prediction model was developed in view of the SAS_A scores, which was then screened, validated, and simplified by scoring, with a nomogram plotted.
The SAS_A score can be utilized to differentiate prognosis. In K-M analysis, the high SAS_A score group had a significantly poorer progression-free survival rate than the low score group, p-value < 0.0001. Through model feature selection and clinical analysis, all variables were finally incorporated to establish a predictive model with a ROC AUC of 0.721 (0.637-0.805) for the validation set and external data, and an AUC of 0.810 (0.719-0.902) for external data, demonstrating good predictive performance. Calibration curves and probability distribution maps were constructed. DCA and CIC analyses demonstrated that model intervention could boost clinical benefits more effectively than intervention for all patients.
Using a predictive model to guide clinical management for anxiety in breast cancer patients is feasible, but additional research is required.
在本研究中，我们旨在基于患者基本属性和治疗前SAS评分建立并评估治疗后焦虑状态的预测模型，期望该模型能指导临床精准干预。
收集了2015年1月1日至2018年12月30日在我院接受手术的606例乳腺癌患者以及2019年6月1日至2019年12月30日收治的144例新诊断乳腺癌患者的数据，共计750例乳腺癌患者。通过分析患者基线特征、随访数据、治疗前自评焦虑量表（SAS）评分以及治疗结束后随访期的SAS_A评分，验证了SAS_A评分与预后的关系。鉴于SAS_A评分开发了风险预测模型，然后通过评分进行筛选、验证和简化，并绘制了列线图。
SAS_A评分可用于区分预后。在K-M分析中，高SAS_A评分组的无进展生存率显著低于低评分组，p值<0.0001。通过模型特征选择和临床分析，最终纳入所有变量建立了预测模型，验证集和外部数据的ROC AUC为0.721（0.637 - 0.805），外部数据的AUC为0.810（0.719 - 0.902），显示出良好的预测性能。构建了校准曲线和概率分布图。DCA和CIC分析表明，模型干预比针对所有患者的干预能更有效地提高临床效益。
使用预测模型指导乳腺癌患者焦虑的临床管理是可行的，但仍需进一步研究。
