Prediction tools for postoperative pain

Abstract

Achieving adequate pain control after surgery has been emphasized by many. 5 Poor pain control may lead to increased postoperative opioid utilization, development of persistent surgical pain, and extended hospital stays. 2,4 In the cross-sectional study by Schnabel et al.,3 the authors used the PAIN-OUT registry containing 50,005 patients to elucidate 8 significant risk factors for severe acute postoperative pain (the numeric rating scale for pain $7) on postoperative day 1. Using a split-sample study design, they found patients with $3 risk factors were more likely to have severe postoperative pain, to spend more time in severe pain (.20%), and wished to have received more pain interventions. 3 The usefulness of their prediction model lies in its ability to risk-stratify patients in the preoperative setting, so personalized treatment strategies can be developed to improve pain outcomes. As such, prognostic variables included in the prediction model should be attainable in the preoperative setting. However, the prediction model in this study included 2 risk factors (feeling anxious or helpless due to pain) that are postoperative characteristics, limiting their utility in predicting pain control in the preoperative setting. Furthermore, the authors included countries with median worse pain intensity of the numeric rating scale $7 as one of the prognostic factors, limiting the external validity of the model to participating countries in the PAIN-OUT registry. The authors addressed these limitations by creating a simplified risk score containing 4 risk factors (younger age, female sex, preoperative opioid use, and preoperative chronic pain). However, the ability of this score to separate those who will have severe pain from those who will have adequate pain control was poor (C-statistic of 0.61). This, unfortunately, limits the utility of the score in the clinical setting.The reporting of their multivariable prediction model and risk score could be improved. The standard for reporting Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Perioperative Pain Management and Opioid Stewardship: A Practical Guide

“…Prospective identification of these challenges, including the application of standardized cognitive and psychosocial assessments, can allow for appropriate preoperative referral, patient optimization, and future study of risk mitigation strategies [15,18,52,75,78,80,88]. To this end, various predictive tools for postoperative pain are being explored [88][89][90][91]. Patient-centered education and expectation management during the pre-admission phase of care are effective strategies for improving postoperative pain control, limiting postoperative opioid use, decreasing complications and readmissions, and increasing postoperative function and quality of life [15,18,[92][93][94][95][96][97][98].…”

Predicting severe pain after major surgery: a secondary analysis of the Peri‐operative Quality Improvement Programme (PQIP) dataset

“…Whilst in keeping with the ranges reported in previous case series [1, 3] this represents a significant minority of patients, with potential implications for patient outcomes given the associations between postoperative pain and morbidity [2]. We focused on pre‐operative variables to increase the clinical utility of our model [10] with the ultimate aim that prediction would enable clinicians to take preventative rather than reactive approaches. Many of the key predictors were non‐modifiable patient and surgical aspects, for example female sex and younger age, both of which have been reported in previous studies [11, 12, 21]; a past medical history of diabetes; and thoracic surgery.…”

Validation of the Calgary Postoperative Pain after Spine Surgery Score for Poor Postoperative Pain Control after Spine Surgery

“…[4][5][6] The high incidence of poorly controlled pain could be explained by the lack of personalized treatment strategies, limitation in understanding of patient and surgical risk factors, and limited preoperative education about expected pain after surgery. 6 Prediction models for poor postoperative pain control have been developed for other surgical disciplines; [7][8][9] however, none are specific for spine surgery. 6 Further, the literature lacks risk prediction tools that facilitate risk stratification for poor postoperative pain control.…”

“…Prediction models for poor postoperative pain control have been developed for other surgical disciplines; 7-9 however, none are specific for spine surgery. 6 Further, the literature lacks risk prediction tools that facilitate risk stratification for poor postoperative pain control.…”