Development and Validation of a Predictive Model for Anxiety Trajectories in Patients with Breast Cancer: A Retrospective Study

被引:0
作者
Li, Xia [1 ]
Wei, Ben-Kai [2 ]
Li, Fan [1 ]
Yan, Huan-Huan [1 ]
Shen, Jun [1 ]
机构
[1] Xuzhou Med Univ, Peoples Hosp Lianyungang 1, Affiliated Hosp, Dept Breast Surg, Lianyungang 222000, Jiangsu, Peoples R China
[2] Xuzhou Med Univ, Peoples Hosp Lianyungang 1, Affiliated Hosp, Dept Gen Surg, Lianyungang 222000, Jiangsu, Peoples R China
关键词
anxiety; breast cancer; prediction model; self-rating anxiety scale score; trajectory analysis; QUALITY-OF-LIFE; DEPRESSION; INTERVENTION; PREVALENCE; DIAGNOSIS; WOMEN;
D O I
10.2147/PRBM.S501127
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Objective: This study aims to develop and validate a predictive model for short-term post-treatment anxiety trajectories in patients with breast cancer, utilizing baseline patient characteristics and initial anxiety scores to inform precise clinical interventions. Methods: Baseline characteristics were collected from 424 patients diagnosed with breast cancer who underwent surgical treatment at our hospital between January 1, 2021, and December 30, 2022. Anxiety levels were assessed using the Self-Rating Anxiety Scale (SAS) scores at admission and at 3-, 6-, 9-, and 12-months post-treatment. Distinct trajectories of SAS score changes were identified and categorized. Variables were screened, and multiple models were developed. The optimal model was identified through comparative analysis, and a nomogram was generated following model simplification. Results: We found three distinct trends in the trajectory of anxiety, but we grouped them into two broad categories: gradual reduction of anxiety and persistent anxiety. LM Model was established by logistic regression, and Model 1 and Model 2 were established by Random Forest (RF) and eXtreme Gradient Boosting (Xgboost) screening variables. The ROC curve areas in the validation set were 0.822 (0.757-0.887), 0.757 (0.680-0.834) and 0.781 (0.710-0.851), respectively. Model comparison, using Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI), identified the Lm model as optimal, which underwent further simplification and value assignment. Decision Curve Analysis (DCA) and Clinical Impact Curve (CIC) analyses confirmed the superiority of model-based interventions over general interventions. Conclusion: Distinct anxiety trajectories are observed in patients diagnosed with breast cancer during the first 12 months posttreatment. Predictive modeling based on baseline characteristics is feasible although though further research is warranted.
引用
收藏
页码:315 / 329
页数:15
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