Predicting and evaluating pain after surgery…Newer methods to the rescue?

Abstract

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? CURRENT PERI-OPERATIVE PAIN PRACTICE PATTERNS Most of the existing literature agrees that currently used multi-modal strategies to prevent or manage pain seem insufficient, mostly because of a lack of focus on organised approaches to identify and assure patient-specific pain management.[2] The current PSP management strategy in most of the hospitals includes a fixed prescription with additional analgesic according to the World health Organization (WHO) analgesic ladder to reduce subjective pain scores. The WHO analgesic ladder which is basically developed for cancer pain tends to worsen with time and relies heavily on opioids. Analgesic administration as per pain scores on the numerical rating scale (NRS) and the visual analogue scale (VAS), a widely used subjective pain assessment tool, may lead to over-use of opioids because patients may interpret pain levels differently. Hence, rather than utilising the WHO analgesic ladder and NRS/VAS to guide pain practices, the application of surgery-specific analgesic strategies in the form of regional analgesia closer to the site of operation, psychological and physical therapy, patient-controlled analgesia, and multi-modal oral opioid-sparing immediate release formulation is advocated.[4] Besides these, high-quality PSP management is a complex task requiring multiple health care personnel and facilities along with evidence-based guidelines. Patient satisfaction is not solely dependent upon the intensity of pain, and quality indicators do play a role in patient satisfaction. Some patients paradoxically report a high level of satisfaction with the pain control strategy despite severe pain, and this might be because of greater participation of the patient in his/her treatment plan, adequate pain management facilities, and a caring environment.[3] Nevertheless, good patient comfort, satisfaction, quality of recovery, post-operative functional outcomes, and quality of life are currently the goals that drive peri-operative health care.[7–9] This means that in addition to pain, the quality of recovery too needs to be assessed. In a randomised controlled trial being published in this issue of the IJA, the effect of bolus intravenous lignocaine (1.5 mg/kg at induction followed by an intravenous infusion 1 mg/kg/h for 24 hours) on post-operative pain relief and the quality of recovery has been evaluated by assessing the Quality of Recovery 15 (QoR15) score on post-operative day 1 and at suture removal in patients undergoing surgery for breast cancer. The authors found that the quality of recovery was better with intravenous lignocaine for pain management and have attributed this to better analgesia and lower opioid consumption produced by lignocaine.[10] This clearly endorses the use of non-opioids in multi-modal post-operative pain management.[11] Nevertheless, the use of non-opioids in pain management is no longer novel and is now a favourite strategy of both researchers and clinicians.[12] CURRENT POST-OPERATIVE PAIN ASSESSMENT TOOLS Effective pain management depends on accurate pain assessment. There are various frequently used pain assessment tools such as VAS, NRS, verbal rating scale (VRS), and faces pain scale (FPS). Most of them are uni-dimensional and self-reported and assess only the pain intensity.[13] Some patients may find difficulty in describing the complexity of their pain by a single numerical value or descriptive words. The subjectivity of the pain assessment tools is thus a limitation, and the search for objective tools goes on. In an article being published in this issue of the IJA, changes in pupillary diameter were used to objectively evaluate post-operative pain. The authors found that the mean pupillary diameter showed a significant incremental trend with an increasing VAS pain score and changes in pupillary diameter correlated well with pain scores on the VAS.[14] The acute pain trajectory is a more precise tool for pain measurement which can evaluate the effect of pain control dynamically and accurately, where initial pain intensity is characterised through the intercept, whereas the resolution of pain is presented by the slope.[15] The acute pain trajectory is advantageous in individualised post-operative pain treatment because it focuses more on rapid pain resolution, rather than a reduction in pain intensity. It has been noted since long that the uni-dimensional pain assessment tools are helpful in relieving suffering and reducing the magnitude of surgical stress at the cost of opioid overuse but do not promote functional recovery.[4] Despite this insight, the routine use of pain scoring tools that promote functional recovery have not been validated and widely adopted. Various evidence-based functional pain assessment tools such as the Clinically Aligned Pain Assessment (CAPA) tool, the Activity-Based Checks (ABCs) of pain scale, the 8-point assessment tool recommended by the American Pain Society, and the 3-point functional activity score developed by Scott and McDonald are already available.[4,16] These functional tools assess comfort, mood, movement, and sleep along with changes in pain, pain control, and side effects.[17] PRE-OPERATIVE PREDICTORS FOR ACUTE AND CHRONIC POST-SURGICAL PAIN There is evidence that improving the management of PSP strongly depends on the timely identification, quantification, and prevention of PSP and its predictors. Many multi-centric studies identified patient characteristics (young age, female gender, obesity), pre-operative psychology (anxiety, depression), pre-existing pain status [severity, duration, functional disability, and morphine milligram equivalent (MME) consumption], wound size >10 cm, and type of surgery as predictors of both acute and chronic PSP.[1,2,18,19] PPSP is strongly correlated with a high acute PSP trajectory severity.[20] With the growing use of technology in medical science, the health-related data available and stored in clinical systems, mobile applications and wearables, artificial intelligence (AI), or machine learning (ML) tools are a boon with the availability of big data sets, fast analysis and predictive algorithms, and software that helps the pain physician not only in better understanding, accurately predicting, and managing PSP but also in pain research.[21–25] The sensitivity of facial expression-based AI to detect NRS pain score ≥4/10 and ≥7/10 has been found to be better than subjective pain assessment using NRS by nurses (89.7% and 77.5% versus 44.9% and 17.0%, respectively).[25] Painchek is a smartphone-integrated automation and AI-based technology based on facial recognition and expression in patients who are unable to communicate during the evaluation of the intensity of pain. AI can be beneficial for classifying, predicting, diagnosing, and managing pain based on personalised patterns that can be missed by subjective methods.[26,27] DEVELOPMENT OF BETTER PREDICTION AND EVALUATION MODELS As pain is significantly subjective, the gold standard for pain assessment has remained patient-reported or self-reported till now.[28] Self-reporting only measures the intensity and grossly misses the psychological and cognitive domains of pain. Although visual addition to self-reporting has been researched and clinically used, it has not thoroughly addressed the limitations. Therefore, there is a continued need for further research in these aspects.[29] Such research might improve and adapt the existing pain assessment tools or develop and validate sensor-based AI for pain assessment. Whereas a few self-reporting pain assessment tools, that is, geriatric pain measure, brief pain inventory, composite pain index, and so on, have multi-dimensions beyond the intensity assessment, there is ample scope to improve, shorten, and adapt them for different age groups, gender, languages, cultures, and ethnicity.[30] Technologies to sense, map (neuroimaging), and analyse pain objectively by evaluating the biological, environmental, and neuronal pathways are being developed. The pain monitoring device, PMD-200 TM (Medasense Biometric Ltd. Ramat Gan, Israel), uses Nociception Level (NOL) TM technology to measure response to the pain stimulus and displays it as a numerical scale. The technology is used intra-operatively and has been shown to reduce post-operative pain, the need for inhalational agents, and early extubation.[31,32] However, the efficacy and reliability are still a matter of further research, especially in the post-anaesthesia care unit and ward where the patient is awake. Ongoing research (NCT03276260) might enlighten us, but the same will require validation from different patient sub-groups and centres across the globe. There are some other nociception-based monitoring devices and technologies as well, that is, Surgical Pleth Index (GE Healthcare, Helsinki, Finland),[33] Analgesia Nociception Index, recently renamed as High-frequency Heart Rate Variability Index (Mdoloris Medical Systems, Loos, France), and so on.[34] These are being tested in anaesthetised patients and need to be evaluated in awake patients when emotions and spatial factors contribute to the pain. Meanwhile, as innovative research continues to take place, it is certain that in the days to come, the anaesthesiologist will be well equipped to accurately predict and evaluate not only intra-operative and post-operative pain but also functional outcomes and the quality of recovery as well.