Preoperative prediction of severe postoperative pain

Abstract

We developed and validated a prediction rule for the occurrence of early postoperative severe pain in surgical inpatients, using predictors that can be easily documented in a preoperative setting. A cohort of surgical inpatients (n=1416) undergoing various procedures except cardiac surgery and intracranial neurosurgery in a University Hospital were studied. Preoperatively the following predictors were collected: age, gender, type of scheduled surgery, expected incision size, blood pressure, heart rate, Quetelet index, the presence and severity of preoperative pain, health-related quality of life the (SF-36), Spielberger's State-Trait Anxiety Inventory (STAI) and the Amsterdam Preoperative Anxiety and Information Scale (APAIS). The outcome was the presence of severe postoperative pain (defined as Numeric Rating Scale > or =8) within the first hour postoperatively. Multivariate logistic regression in combination with bootstrapping techniques (as a method for internal validation) was used to derive a stable prediction model. Independent predictors of severe postoperative pain were younger age, female gender, level of preoperative pain, incision size and type of surgery. The area under the receiver operator characteristic (ROC) curve was 0.71 (95% CI: 0.68-0.74). Adding APAIS scores (measures of preoperative anxiety and need for information), but not STAI, provided a slightly better model (ROC area 0.73). The reliability of this extended model was good (Hosmer and Lemeshow test p-value 0.78). We have demonstrated that severe postoperative pain early after awakening from general anesthesia can be predicted with a scoring rule, using a small set of variables that can be easily obtained from all patients at the preoperative visit. Before this internally validated preoperative prediction rule can be applied in clinical practice to support anticipatory pain management, external validation in other clinical settings is necessary.

Acute pain after total hip arthroplasty does not predict the development of chronic postsurgical pain 6 months later

“…Factors such as age [9,22], female gender 1221, and opioid consumption have been identified as predictors of the transition to CPSP [4]. Our results failed to demonstrate such associations (Table 1).…”

“…The most consistent finding in the literature thus far has been that the presence of current pain (either preoperative or acute postsurgical pain) predicts future pain [4,[9][10][11][12]. Other factors such as younger age [3,14], female gender [9,15], the presence of anxiety [9,16], pain catastrophizing [17], and other psychological variables such as emotional numbing [18] have been shown to predict the development ofpostsurgical pain after a variety of surgical procedures [4].…”

“…before surgery/after surgery/or not at all), differences were not found in the acute perioperative period with respect to opioid consumption, pain at rest, or movement-evoked pain after total hip arthroplasty [19]. All patients also received a clinically relevant, preventive, multimodal regimen of acetaminophen, celecoxib (Cox-2 selective inhibitor), and spinal anesthesia [9]. The aim of the current paper was to test whether maximal movement-evoked acute pain scores would predict the development of CPSP in the sample of patients who had undergone total hip arthroplasty.…”

“…The relatively infrequent use of opioid analgesics in our cohort is likely attributable to the adjunctive medications given, which decreased the pain intensity. Studies have reported that female gender [9] and younger age [9,40) predicted intense acute postoperative pain, and nationwide surveys consistently find that the incidence of chronic neuropathic pain is significantly higher in women than men [4143]. Several smaller studies of CPSP have also identified younger age as a risk factor for the transition from acute pain to pain chronicity [14,22].…”

Baseline Morphine Consumption May Explain Between-Study Heterogeneity in Meta-analyses of Adjuvant Analgesics and Improve Precision and Accuracy of Effect Estimates

“…Other bias included baseline characteristic imbalances which have been associated with influencing pain (for example gender and pre-operative pain) 29 or industry sponsorship. 30 …”