Predicting and Evaluating Pain after Surgery…newer Methods to the Rescue?

© 2023 Indian Journal of Anaesthesia | Published by Wolters Kluwer Medknow Managing post-surgical pain (PSP) is an integral part of anaesthesia practice; however, inspite of significant advancements in peri-operative medicine and the introduction of evidence-based recommendations for PSP management, existing data suggest that it is sub-optimally managed.[1-3] The clinical prevalence of acute PSP is as high as 80%, and one to 2/3 patients suffer moderate to severe pain. This hampers early recovery, lengthens hospital stay, increases opioid consumption, and leads to persistent post-surgical pain (PPSP).[3] PPSP is observed in 5–60% post-surgical patients irrespective of the type of surgery and can be of sufficient severity to affect the quality of life.[4] Also, as mentioned in a previous editorial of the Indian Journal of Anaesthesia (IJA), the pain management practices in our country are diverse.[5] All this leaves one wondering as to whether the current peri-operative pain management practices in our country are optimal. Are we really able to accurately evaluate post-surgical pain? How far have we been successful in predicting post-operative pain? Currently, risk prediction models including biomarkers are in the cyanosure of researchers and clinicians.[6] In this era of big data, events can be predicted. Can the same be performed successfully in pain practice?