Xinyan Wu,1,2 Tao Xu,2 Ping Cai,1 Siwen Shen,1 Mengchen Ji,1 Qing Liu,3 Xiaobo Wang,2 Mingqi Zhang,2 Shiming Wang,4,* Danfeng Gu1,* 

1Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214122, People’s Republic of China; 2Jiangnan University, Wuxi, Jiangsu, 214122, People’s Republic of China; 3Huadong Sanatorium, Wuxi, Jiangsu, 214122, People’s Republic of China; 4Wuxi Branch of Zhongda Hospital Southeast University, Wuxi, Jiangsu, 214122, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Danfeng Gu, Affiliated Hospital of Jiangnan University, 1000 hefeng Road, Binhu District, Wuxi, Jiangsu, People’s Republic of China, Email 1308594635@qq.com Shiming Wang, Wuxi Branch of Zhongda Hospital Southeast University, No. 1128, Dacheng Road, Xishan District, Wuxi, Jiangsu, People’s Republic of China, Email 281146056@qq.com

Background: Patients with lung cancer often experience high levels of pain and recurrent pain episodes after thoracoscopic surgery, compromising their adherence to rehabilitation exercises and quality of life. Pain catastrophizing is defined as an exaggerated and negative mindset towards actual or anticipated pain and is one of the significant factors influencing pain. To date, prior investigations on pain catastrophizing have predominantly employed variable-centered approaches, focusing on broader population levels while neglecting individual specificity. Latent profile analysis represents an individual-centered data analysis method that can accurately identify distinct phenotypic subgroups and deeply analyze the characteristics of each subgroup. This enables clinicians to identify high-risk patients early and provide precision interventions to reduce pain catastrophizing and ultimately alleviate postoperative pain burden. Stratification-guided interventions play a crucial role in treating patients with pain catastrophizing, aligning with the principles of precision medicine.
Methods: 409 patients were included in this cross-sectional study. Latent profile analysis (LPA) was employed to discern distinct classes of pain catastrophizing among these patients. Chi-square tests, ANOVA, and multinomial logistic regression were conducted to analyze the factors influencing the different pain catastrophizing profiles among patients following thoracoscopic lung cancer surgery.
Results: Three distinct latent classes of pain catastrophizing were identified, Class1, labeled “Mild Pain Catastrophizing-Rumination Type”(26.2% of participants), Class2, referred to as “Moderate Pain Catastrophizing-Ruminative Exaggerative Type”(33.2%), Class3, designated as “Severe Pain Catastrophizing-Ruminative Helpless Exaggerative Type”(40.6%). Unordered multinomial logistic regression analysis indicated that factors influencing mild pain catastrophizing included fatigue, perception of social support, marital status, primary caregiver, tumor , and clinical stage. In contrast, factors influencing moderate pain catastrophizing included fatigue, perception of social support, marital status, education level, primary caregiver, and clinical stage (P < 0.05).
Conclusion: Three distinct patterns of pain catastrophizing were identified in patients following lung cancer surgery, each exhibiting unique classification characteristics. Healthcare professionals should promptly identify individuals at high risk for pain catastrophizing and develop targeted interventions that address physiological, psychological, and social factors.

Keywords: thoracic surgery, video-assisted, pain catastrophizing, latent profile analysis