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.

Keywords: Acute Pain; Latent Class Analyses (LCA); Random Intercept; Random Slope; Trajectory.

Figure 1.

Pain trajectories for individuals with similar mean pain scores after lower-extremity injury. Each line depicts a participant’s pain trajectory, and although each mean pain score is similar, the pain trajectory is different, demonstrating that patients with similar mean pain scores can have decreasing pain (negative trajectory), stable pain (flat trajectory), or increasing pain (positive trajectory). Reprinted from [11] with permission from Elsevier.

Figure 2.

Pain trajectories fitted by LMMs. Depicted in (A) are the trajectories for two individuals resulting from a random intercept LMM. Given the same data points for two individuals, the lines in (B) are the trajectories resulting from a random intercept and random slope model.

Figure 3.

Four hypothetical patient scenarios with 3 to 7 postoperative pain measurements demonstrating predicted random effects. Dotted line: an OLS regression fitted only to the subject’s pain assessments but ignoring all available covariate information. Solid line: a population-level estimate including covariates but ignoring the subject-specific trends. Dashed line: the model estimated weighted average of the individual-specific line and the population-averaged line (empirical Bayes).

Figure 4.

Pain trajectories of low back pain patients from four clusters were presented with a single knot per group. Reprinted from [39] under Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Figure 5.

Pain scores (7,762 encounters) of 5,418 hospitalized adult inpatients admitted with pain scores >4 with geometric smoothing (red solid lines) and fitted curve from polynomial regression model (blue dashed line). X-axis is the time since the initial pain measurement (days). Y-axis is the pain score (0–10 NRS). Reprinted from [5]. The Creative Commons license does not apply to this content. Use of the material in any format is prohibited without written permission from the publisher, Wolters Kluwer Health, Inc. Please contactpermissions@lww.comfor further information.

Figure 6.

Contrasting patterns of postoperative pain. (A) depicts the mean postoperative trajectory for all patients. (B) shows the mean trajectory for those patients classified as having decreased pain. (C) displays the mean trajectory for those patients who had stable pain over 6 days. (D) demonstrates the mean trajectory for those patients who had increasing pain over 6 days. Reprinted from [4] with permission from Elsevier.