Development and Validation of a Prediction Model for Chronic Post-Surgical Pain After Thoracic Surgery in Elderly Patients: A Retrospective Cohort Study

Xiao-Dan Wu,1 Fan-Fang Zeng,1 Xiao-Xuan Yu,1 Pan-Pan Yang,1 Jun-Peng Wu,1 Ping Xv,2 Hai-Tang Wang,1 You-Ming Pei1 1Department of Anesthesiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 2Department of Anesthesiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510280, People’s Republic of ChinaCorrespondence: Hai-Tang Wang; You-Ming Pei, Department of Anesthesiology, Nanfang Hospital, Southern medical university, 1838 Guangzhou North Road, Guangzhou, Guangdong, 510515, People’s Republic of China, Tel +86-18718911653 ; +86-15889942610, Fax +86 20-62787271 ; +86 20-62787271, Email wanghaitang8888@163.com; 71119099@qq.comPurpose: Chronic post-surgical pain (CPSP) is one of the adverse outcomes after surgery, especially in thoracotomy. However, the prevalence of CPSP in elderly adults (≥ 65 years), is still limited. Therefore, the present study was undertaken to establish and validate the prediction model of CPSP in those patients after thoracic surgery, including thoracotomy and video-assisted thoracoscopic surgery.Patients and Methods: This retrospective, observational single-center cohort study was conducted in Nanfang Hospital, Southern Medical University, which randomly and consecutively collected 577 elderly patients who underwent thoracic surgery between January 1, 2017, and December 31, 2020. According to the Akaike information criterion, the prediction model was built based on all the data and was validated by calibration with 500 bootstrap samples.Results: The mean age of participants was 69.09± 3.80 years old, and 63.1% were male. The prevalence of CPSP was 26.9%. Age more than 75 years, BMI, blood loss, longer length of hospital stays, and higher pre-operative neutrophil count were associated with CPSP. Except for these factors, we incorporated history of drinking to build up the prediction model. The areas under the curve (AUCs) of the prediction models were 0