Pain medicine (Malden, Mass.) · 2018
Identifying At-Risk Subgroups for Acute Postsurgical Pain: A Classification Tree Analysis.
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Wang Yang, Liu Zejun, Chen Shuanghong, Ye Xiaoxuan, Xie Wenyi, Hu Chunrong, Iezzi Tony, Jackson Todd
Аннотация
OBJECTIVE: 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).
DESIGN: The study design was prospective.
SETTING: This study was carried out at Chongqing No. 9 hospital, Chongqing, China.
SUBJECTS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
DESIGN: The study design was prospective.
SETTING: This study was carried out at Chongqing No. 9 hospital, Chongqing, China.
SUBJECTS: 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).
RESULTS: 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.
CONCLUSIONS: 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.