AAAPT: Assessment of the Acute Pain Trajectory

Abstract

Objective Define and contrast acute pain trajectories vs. the aggregate pain measurements, summarize appropriate linear and nonlinear statistical analyses for pain trajectories at the patient level, and present methods to classify individual pain trajectories. Clinical applications of acute pain trajectories are also discussed. Setting In 2016, an expert panel involving the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION), American Pain Society (APS), and American Academy of Pain Medicine (AAPM) established an initiative to create a pain taxonomy, named the ACTTION-APS-AAPM Pain Taxonomy (AAAPT), for the multidimensional classification of acute pain. The AAAPT panel commissioned the present report to provide further details on analysis of the individual acute pain trajectory as an important component of comprehensive pain assessment. Methods Linear mixed models and nonlinear models (e.g., regression splines and polynomial models) can be applied to analyze the acute pain trajectory. Alternatively, methods for classifying individual pain trajectories (e.g., using the 50% confidence interval of the random slope approach or using latent class analyses) can be applied in the clinical context to identify different trajectories of resolving pain (e.g., rapid reduction or slow reduction) or persisting pain. Each approach has advantages and disadvantages that may guide selection. Assessment of the acute pain trajectory may guide treatment and tailoring to anticipated symptom recovery. The acute pain trajectory can also serve as a treatment outcome measure, informing further management. Conclusions Application of trajectory approaches to acute pain assessments enables more comprehensive measurement of acute pain, which forms the cornerstone of accurate classification and treatment of pain.

Time Effect on Acute Postoperative Pain After Total Knee Replacement Surgery

“…Notably, our study is one of the first to use multilevel analyses in this postoperative population. Our data demonstrate the advantages of a multilevel analysis, which describes the clinical scenario in detail and takes individual differences into account 16 . Furthermore, we replicated our earlier findings regarding the individual differences in APP between patients 28 Figure 3.…”

“…The inclusion of a feedback system in the application would enable patients to become actively involved in their treatment by learning from their own data 8,31 . Bayman et al advocate the application of trajectory approaches to acute pain assessments to enable more comprehensive measurements of acute pain, forming the cornerstone of the accurate classification and treatment of pain 16 . We demonstrated significant differences in baseline pain and pain course over time, supporting the development of more personalized medicine.…”

“…Multilevel data analysis, due to its nested data structure, wherein moments are embedded within participants, allows one to model random intercepts and slopes to assess various baseline levels and dynamics of individual participants over time. Multilevel analysis comprises the comparison and evaluation of individual pain trajectories, unlike averaging pain data, which carries the risk of masking the rate, direction of change, and duration of pain in an individual patient 16,17 …”

Early Postoperative Pain Trajectories after Posterolateral and Axillary Approaches to Thoracic Surgery: A Prospective Monocentric Observational Study

“…Defining acute postoperative pain as a trajectory rather than as one or more simple point estimates of intensity increases the information yield of pain assessment and improves measurement precision [ 8 , 16 ]. Thus, it must be underlined that a high percentage of patients experience pain during the early postoperative period despite the analgesic techniques put in place and followed-up by nurses who specialize in pain management.…”

Profiles of adult people in a Spanish sample with chronic pain: Cluster analysis

“…The most frequently used technique of analysis for these purposes is cluster analysis. In recent years, it has been widely used in the area of health sciences to classify and characterize people with different pathologies on the basis of symptoms or common characteristics (Bayman et al, 2021).…”