A prospective observational study on trajectories and prognostic factors of mid back pain

Abstract

Background: Although mid back pain (MBP) is a common condition that causes significant disability, it has received little attention in research and knowledge about trajectories and prognosis of MBP is limited. The purpose of this study was to identify trajectories of MBP and baseline risk factors for an unfavorable outcome in MBP patients undergoing chiropractic treatment. Methods: This prospective-observational study analyzes outcome data of 90 adult MBP patients (mean age = 37.0 ± 14.6 years; 49 females) during one year (at baseline, after 1 week, 1 month, 3, 6 and 12 months) after start of chiropractic treatment. Patients completed an 11-point (0 to 10) numeric pain rating scale (NRS) at baseline and one week, one month, three, six and twelve months after treatment start and the Patient's Global Impression of Change (PGIC) questionnaire at all time points except baseline. To determine trajectories, clustering with the package kml (software R), a variant of k-means clustering adapted for longitudinal data, was performed using the NRS-data. The identified NRS-clusters and PGIC data after three months were tested for association with baseline variables using univariable logistic regression analyses, conditional inference trees and random forest plots. Results: Two distinct NRS-clusters indicating a favourable (rapid improvement within one month from moderate pain to persistent minor pain or recovery after one year, 80% of patients) and an unfavourable trajectory (persistent moderate to severe pain, 20% of patients) were identified. Chronic (> 3 months) pain duration at baseline significantly predicted that a patient was less likely to follow a favourable trajectory [OR = 0.16, 95% CI = 0.05-0.50, p = 0.002] and to report subjective improvement after twelve months [OR = 0.19, 95% CI = 0.07-0.51, p = 0.001], which was confirmed by the conditional inference tree and the random forest analyses. Conclusions: This prospective exploratory study identified two distinct MBP trajectories, representing a favourable and an unfavourable outcome over the course of one year after chiropractic treatment. Pain chronicity was the factor that influenced outcome measures using NRS or PGIC.

Factors Associated With Clinical Responses to Spinal Manipulation in Patients With Non-specific Thoracic Back Pain: A Prospective Cohort Study

“…Knecht et al investigated the various trajectories of mid-back pain and baseline risk factors for unfavorable outcomes for patients undergoing chiropractic treatment. Their results found that pain that lasts for more than 3 months before a treatment is associated with poor outcomes ( 11 ).…”

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

“…Many studies have identified clusters of pain trajectories among individuals living with chronic pain. Some have focused on participants with 1 chronic pain condition, such as osteoarthritis [ 15 , 36 - 39 , 52 - 58 ], low back pain [ 13 , 14 , 27 , 59 - 65 ], other back pain [ 25 , 32 , 49 , 66 ], neck or shoulder pain [ 33 , 61 , 67 , 68 ], leg pain [ 29 ], knee pain [ 69 ], or foot pain [ 70 ], whereas others have identified clusters among a broader population, such as those with musculoskeletal pain [ 26 , 31 , 47 , 71 , 72 ] or general pain [ 48 , 73 - 75 ]. Clusters in these studies were described by the severity of pain (eg, no pain , very low pain , mild pain , moderate pain , high pain , and severe pain ), the level of change in pain severity (eg, persistent , ongoing , episodic , worsening , recovering , and fluctuating ), or a combination of these features.…”

“…Similar to our study, some previous studies have used methods from the k-means and k-medoids family of algorithms. Knecht et al [ 25 ] used the KmL package to identify 2 clusters in responders and nonresponders groups. Weng et al [ 76 ] used a k-median algorithm to identify 4 clusters of pain severity: slightly rise , completely drop , sudden rise , and steady group .…”

“…Previous studies have used a range of methods to cluster pain severity, including k-means clustering [ 25 , 26 ], hierarchical cluster analysis [ 27 ], growth mixture modeling [ 28 - 31 ], latent class growth analysis [ 13 - 15 , 32 , 33 ], multilevel latent class analysis [ 34 ], and group-based trajectory analysis [ 35 - 40 ]. Different approaches have different strengths and kinds of assumptions; for example, some may assume that clusters are internally homogeneous, while others may assume that the data overall follow a particular (eg, linear) form, are continuous, or are similar.…”

The clinical course of neck pain: Are trajectory patterns stable over a 1‐year period?

“…Several studies show that most patients display an initial attenuation in symptoms and disability subsequent to the onset of an acute episode of spinal pain (Burns et al, 2020;Carroll et al, 2008;Vasseljen et al, 2013;Vos et al, 2008). Although this initial improvement is also found in patients already in a long-lasting course of pain, it seems to be larger and more rapid in patients presenting at the onset of an acute episode (Bot et al, 2005;Knecht et al, 2020;Langenfeld et al, 2015;Leaver et al, 2013;Peterson et al, 2012;Rubinstein et al, 2008). Previous research is mainly based on outcomes measured as mean values at single time points.…”