Identifying At-Risk Subgroups for Acute Postsurgical Pain: A Classification Tree Analysis.

Abstract

Acute postsurgical pain is common and has potentially negative long-term consequences for patients. In this study, we evaluated effects of presurgery sociodemographics, pain experiences, psychological influences, and surgery-related variables on acute postsurgical pain using logistic regression vs classification tree analysis (CTA). The study design was prospective. This study was carried out at Chongqing No. 9 hospital, Chongqing, China. Patients (175 women, 84 men) completed a self-report battery 24 hours before surgery (T1) and pain intensity ratings 48-72 hours after surgery (T2). An initial logistic regression analysis identified pain self-efficacy as the only presurgery predictor of postoperative pain intensity. Subsequently, a classification tree analysis (CTA) indicated that lower vs higher acute postoperative pain intensity levels were predicted not only by pain self-efficacy but also by its interaction with disease onset, pain catastrophizing, and body mass index. CTA results were replicated within a revised logistic regression model. Together, these findings underscored the potential utility of CTA as a means of identifying patient subgroups with higher and lower risk for severe acute postoperative pain based on interacting characteristics.