Xia Li,1,* Ben-Kai Wei,2,* Fan Li,1 Huan-Huan Yan,1 Jun Shen1 

1Department of Breast Surgery, the First People’s Hospital of Lianyungang, The Affiliated Hospital of Xuzhou Medical University, Lianyungang, 222000, Jiangsu Province, People’s Republic of China; 2Department of General Surgery, the First People’s Hospital of Lianyungang, The Affiliated Hospital of Xuzhou Medical University, Lianyungang, 222000, Jiangsu Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jun Shen, Department of Breast Surgery, the First People’s Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, 222002, Jiangsu Province, People’s Republic of China, Tel +8618961325323, Email shenjun2257@126.com

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 post-treatment. Predictive modeling based on baseline characteristics is feasible although though further research is warranted.

Keywords: anxiety, breast cancer, prediction model, self-rating anxiety scale score, trajectory analysis