Identifying at-risk subgroups for acute postsurgical pain: A classification tree analysis
Wang,Yang1; Liu,Zejun1; Chen,Shuanghong1; Ye,Xiaoxuan1; Xie,Wenyi2; Hu,Chunrong3; Iezzi,Tony4; Jackson,Todd1,5
Source PublicationPain Medicine (United States)
ISSN15264637 15262375
AbstractObjective. 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.
KeywordAcute postsurgical pain Classification tree analysis Pain self-efficacy Risk factors
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Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorJackson,Todd
Affiliation1.Key Laboratory of Cognition and PersonalitySouthwest University,Chongqing,China
2.Beibei Chinese Medicine Hospital,Chongqing,China
3.Department of Rheumatology and ImmunologyChongqing Number 9 Hospital,Chongqing,China
4.Department of PsychologyLondon Health Sciences Centre,London,Canada
5.Department of PsychologyUniversity of Macau,Taipa, Macau,999078,China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang,Yang,Liu,Zejun,Chen,Shuanghong,et al. Identifying at-risk subgroups for acute postsurgical pain: A classification tree analysis[J]. Pain Medicine (United States),2019,19(11):2283-2295.
APA Wang,Yang.,Liu,Zejun.,Chen,Shuanghong.,Ye,Xiaoxuan.,Xie,Wenyi.,...&Jackson,Todd.(2019).Identifying at-risk subgroups for acute postsurgical pain: A classification tree analysis.Pain Medicine (United States),19(11),2283-2295.
MLA Wang,Yang,et al."Identifying at-risk subgroups for acute postsurgical pain: A classification tree analysis".Pain Medicine (United States) 19.11(2019):2283-2295.
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