Abstract
The prediction of acute postoperative pain would be of great clinical advantage, but results of studies investigating possible predictors are inconsistent. Here, we studied the role of a wide variety of previously suggested predictors in 74 patients undergoing breast surgery. Preoperatively, patients filled out the Pain Sensitivity Questionnaire (PSQ) and a set of psychological questionnaires (the Beck Depression Inventory [BDI], State-Trait Anxiety Inventory [STAI], and Pain Catastrophizing Scale [PCS]) and participated in an experimental pain testing session, including assessment of conditioned pain modulation (CPM), temporal summation, and responses to heat, pinprick, and pressure pain. Postoperatively, patients reported pain intensity. Stepwise linear regression analysis was used to test for prediction of maximal pain on postoperative day 1 in the whole cohort and in the subgroups of patients with and without pre-existing chronic pain. In the total group, linear regression identified only the expectation of postoperative pain intensity as significant predictor (F[1,65] = 6.5, P < 0.05), explaining 9% of the variance. In patients without pre-existing chronic pain, a smaller CPM effect predicted more postoperative pain, explaining 17% of the variance (F[1,48] = 9.9, P < 0.01). In patients with pre-existing chronic pain, higher PSQ and PCS scores predicted more postoperative pain, together explaining 54% of the variance (F[2,19] = 11.1, P < 0.001). In conclusion, prediction of acute postoperative pain in the whole group was limited. This might be due to differing predictors in specific subgroups of patients. Although CPM predicted pain in patients without pre-existing chronic pain, PSQ and PCS predicted pain in patients with pre-existing chronic pain.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISSN: | 0304-3959 |
Sprache: | Englisch |
Dokumenten ID: | 52328 |
Datum der Veröffentlichung auf Open Access LMU: | 14. Jun. 2018, 09:49 |
Letzte Änderungen: | 04. Nov. 2020, 13:31 |