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Characterising the clinical course of back pain by mean pain scores over time may not adequately reflect the complexity of the clinical course of acute low back pain. We analysed pain scores over 12 weeks for 1,585 acute low back pain patients presenting to primary care to identify distinct pain trajectory groups, and baseline patient characteristics associated with membership of each cluster. This was a secondary analysis of the PACE trial that evaluated paracetamol for acute low back pain. Latent class growth analysis determined a five cluster model which comprised 567 (35.8%) patients who recovered by week 2 (cluster 1, rapid pain recovery); 543 (34.3%) patients who recovered by week 12 (cluster 2, pain recovery by week 12); 222 (14.0%) patients whose pain reduced but did not recover (cluster 3, incomplete pain recovery); 167 (10.5%) patients whose pain initially decreased but then increased by week 12 (cluster 4, fluctuating pain); and 86 (5.4%) patients who experienced high-level pain for the whole 12 weeks (cluster 5, persistent high pain). Patients with longer pain duration were more likely to experience delayed or non-recovery. Belief in greater risk of persistence was associated with non-recovery, but not delayed recovery. Higher pain intensity, longer duration and workers' compensation were associated with persistent high pain, while older age and increased number of episodes was associated with fluctuating pain. Identification of discrete pain trajectory groups offers the potential to better manage acute low back pain.
Research Paper
Trajectories of acute low back pain: a latent class
growth analysis
Aron S. Downie
*, Mark J. Hancock
, Magdalena Rzewuska
, Christopher M. Williams
Chung-Wei Christine Lin
, Christopher G. Maher
Characterising the clinical course of back pain by mean pain scores over time may not adequately reflect the complexity of the clinical
course of acute low back pain. We analysed pain scores over 12 weeks for 1585 patients with acute low back pain presenting to
primary care to identify distinct pain trajectory groups and baseline patient characteristics associated with membership ofeach cluster.
This was a secondary analysis of the PACE trial that evaluated paracetamol for acute low back pain. Latent class growth analysis
determined a 5 cluster model, which comprised 567 (35.8%) patients who recovered by week 2 (cluster 1, rapid pain recovery); 543
(34.3%) patients who recovered by week 12 (cluster 2, pain recovery by week 12);222 (14.0%) patients whosepain reduced but did not
recover (cluster 3, incomplete pain recovery); 167 (10.5%) patients whose pain initially decreased but then increased by week 12
(cluster 4, fluctuating pain); and 86 (5.4%) patients who experienced high-level pain for the whole 12 weeks (cluster 5, persistent high
pain). Patients with longer pain duration were more likely to experience delayed recovery or nonrecovery. Belief in greater risk of
persistence was associated with nonrecovery, but not delayed recovery. Higher pain intensity, longer duration, and workers’
compensation were associated with persistent high pain, whereas older age and increased number of episodes were associated with
fluctuating pain. Identification of discrete pain trajectory groups offers the potential to better manage acute low back pain.
Keywords: Latent class growth analysis, Low back pain, Primary care, Trajectory, Clinical course
1. Introduction
Low back pain (LBP) is a major cause of disability
and is an
extremely common condition presenting to primary care.
Most guidelines for acute LBP advise a minimal treatment
approach on the premise that the clinical course is typically
When patients are considered as a group, this
optimistic view seems consistent with the research evidence.
For example, a systematic review of 15 cohort studies reported
pooled mean pain scores, on a 0 to 100 scale of 52, 23, 12, and 6
at baseline, 6, 12, and 52 weeks follow-up, respectively.
However, the review also noted a moderate degree of person-to-
person variability with the SD of pain and disability outcomes
typically approximately 20 points on a 0 to 100 scale at all follow-up
time points.
The large variability in outcomes at each time point
suggests that it may be overly simplistic to describe the clinical
course of LBP by only considering the group mean over time.
There has been recent interest in studying the clinical course of
individual patients with LBP.
For example, Dunn et al.
studied 342 patients from primary care with back pain over 1 year
using latent class analysis. The method identified 4 clusters that
differed across pain status and impact, disability, and psycholog-
ical characteristics. However, the study provided little detail about
the early pain course and enrolled a mixed cohort of patients who
may not adequately describe the acute pain experience. Recently,
Kongsted et al.
studied 1082 patients from primary care by text
messagingusing latent class analysis to reveal clusters of LBP. The
study also enrolled a mixed cohort of patients with more than half
reporting leg pain and a low recovery rate at 12 weeks (;54%)
when compared with other studies.
The authors comment
that more appropriate clustering techniques may exist for
longitudinal data, such as latent class growth analysis (LCGA).
Given that the group level course of acute and chronic LBP is quite
different, it is likely that pain trajectory groupings identified in
chronic and mixed duration samples are not the same as those in
acute LBP. To date, no research has studied individual pain
trajectories exclusively in acute LBP using data from a large
inception cohort. Understanding the patient traits that are
associated with each pain trajectory group could advance
understanding of the mechanism(s) underlying the trajectories
and also assist in developing targeted therapies for each trajectory.
The aim of this study was to identify pain trajectory groups for
patients with acute LBP presenting to primary care and then to
explore individual characteristics that influenced group member-
ship. We hypothesized that for each trajectory group identified,
membership would be associated with clinical and demographic
characteristics measured at baseline.
2. Methods
Our interest was in the trajectories of patients’ pain during the
course of an episode of acute LBP. We used statistical procedures
Sponsorships or competing interests that may be relevant to content are disclosed
at the end of this article.
George Institute for Global Health, University of Sydney, Sydney, Australia,
Department of Chiropractic, Faculty of Science and Engineering, Macquarie
University, Sydney, Australia,
Department of Health Professions, Faculty of
Medicine and Health Sciences, Macquarie University, Sydney, Australia
*Corresponding author. Address: George Institute for Global Health, GPO Box
5389, Sydney NSW 2001, Australia. Tel.: 161 2 9657 0382; fax: 161 2 9657 0301.
E-mail address: (A. S. Downie).
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to (1) identify these trajectories and (2) characterise membership of
each trajectory using baseline patient characteristics. The data
were taken from a previous trial.
2.1. Data source
This is a secondary analysis of the PACE trial,
a placebo-
controlled trial of paracetamol for acute LBP. In brief, 235 primary
care clinicians screened 4606 consecutive patients who sought
care for an episode of acute LBP (,6 weeks duration and of at
least moderate-intensity pain as defined by item 7 of the Short
Form Health Survey, SF-36). A total of 1652 subjects were
recruited from across Sydney, Australia, during 2009 to 2012,
and pain outcomes were measured regularly over the following 12
weeks. There was no difference found in pain (or other) outcomes
for those receiving paracetamol vs placebo. Baseline information
was recorded by research staff through telephone interview and
occurred within 48 hours after the initial primary care consultation,
before study entry and randomization. Follow-up data at 1, 2, 4,
and 12 weeks were contemporaneously recorded into a booklet
by the subject and then transcribed through a telephone interview
with research staff or directly into an online database by the
A detailed description of the trial methods can be found
in the PACE study protocol
and statistical analysis plan.
2.2. Analysed participant characteristics
Our primary outcome was pain intensity numerical rating scale
(NRS) values, which were collected at 1, 2, 4, and 12 weeks. In
addition, baseline demographic (age, sex, and compensation
status) and clinical data (pain level, duration and referral beyond
knee, number of previous episodes, days of reduced activity,
perceived risk of persistence, sleep quality, quality of life, and
disability) were extracted from the PACE data set.
2.3. Statistical analysis
The analysis assumed that there exist a number of distinct and
unobserved trajectory groups for acute LBP that are revealed by
the patterns of recorded pain level data across 5 time points. The
data analysis comprised 3 steps. First, a LCGA model for ordinal
data was established to identify the minimum number of clusters
that explained variation in the data (step 1). Second, each subject
was assigned to one of the identified clusters (step 2).
multinomial logistic regression was applied to assess the
relationship between cluster membership and baseline charac-
teristics (step 3).
Internal (bootstrap) validation of the regression
model was undertaken.
2.3.1. Latent class growth analysis
Pain course was modeled over time using pain scores at 5 time
points (baseline, week 1, 2, 4, and 12). A requirement of LCGA is
normal distribution of the dependent variable.
Pain score
distribution was non-normal at each time point. The solution was
to trichotomize pain scores analogous to the method described by
with cut points used previously.
resulted in “pain recovery” (NRS #1); “low–moderate pain” (NRS
2-4); and “high pain” (NRS $5) strata. Latent class growth analysis
uses maximum likelihoodto assign subjects to a cluster, so missing
values are handled without need for imputation. We excluded
subjects missing greater than 2 of the 5 time points. Up to an
8-cluster solution was considered, with the optimum number of
clusters determined using goodness of fit criteria, namely, Akaike’s
information criterion (AIC); Bayesian information criterion (BIC); and
the bootstrap likelihood ratio test.
Pragmatic consideration
included high entropy
and high average posterior probability of
belonging to each cluster
; a minimum cluster size of 5%; and
a distinctive pain course for each trajectory.
2.3.2. Multinomial logistic regression analysis
After pain trajectories and subject membership were determined,
multivariate multinomial logistic regression analysis was used to
determine the characteristics of each cluster with the strength of the
relationship determined using proportional classification for all
The risk of belonging to each cluster for a given
characteristic was compared to the base cluster (defined by the
cluster with most rapid recovery) and expressed as a relative risk
ratio (RRR).
Baseline factors considered important to include in
the multivariate regression were those hypothesised to influence
cluster membership and were selected a priori (Table 1). Contin-
uous baseline variables were expressed as risk per SD increase.
Non-normally distributed count and continuous variables were
dichotomized. The variables’ duration of current episode,number of
previous episodes,andquality of sleep were dichotomized using
recommended against.
Testing for collinearity was performed with
tolerance of ,0.1 and variance inflation factor (VIF) #0.2 or $5.0
indicating possible collinearity.
Full and reduced unconditional
model predictive efficiency was determined by goodness of fit
indices, mainly AIC,
with the lowest score (AIC
better fitting model. Reduced models excluded all combinations of
the 5 least significant variables found on univariate analysis.
The robustness of the predictors in the multinomial logistic
regression model was tested using Monte Carlo modelling (non-
naive bootstrap).
The study population parameters (estimates
of relative risk per cluster pair) were compared to 1000 bootstrap
replications of the study data.
The threshold for nonignorable
bias was set at 0.25 3SE of the bootstrap sample.
version 13.1 for Mac OS (Stata Corp, College Station, TX) was used
to analyse model collinearity, and Mplus version 7.31 (Muth´en &
Muth ´en, Los Angeles, CA) was used to generate the LCGA,
multivariate regression, and bootstrap validation models.
3. Results
3.1. Sample characteristics
Overall, of the 1652 individuals enrolled in PACE, 4.1% (n 567)
were excluded from this study on the basis of missing greater than
2 of 5 pain scores. Pain scores were then available for the
remaining 95.9% (n 51585) of subjects at baseline. Of these,
98.9% (n 51568), 99.1% (n 51570), 97.4% (n 51543), and
96.0% (n 51522) were recorded for weeks 1, 2, 4, and 12,
respectively. Table 2 displays baseline characteristics for included
subjects stratified by cluster for the final 5-cluster LCGA model.
3.2. Latent class growth analysis (selection of ideal number
of clusters)
The 3-cluster to 8-cluster models were tested using goodness of
fit indices to determine the best model and are displayed in
Table 3. The rate of change in log likelihood diminished rapidly
after the 5-cluster model (22LL difference 5498.9, 87.7, 121.5,
44.5, 25.6, and 23.6 for 3-8 cluster models, respectively),
whereas the bootstrap likelihood ratio test indicated up to an
8-cluster model was possible (Pvalue approached nonsignifi-
cance, P50.033). Fit statistics reached minima for the 8-cluster
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511,488) and 6-cluster (BIC
511,651) models,
respectively. Entropy was adequate for all solutions (0.79, 0.79,
0.78, 0.75, 0.77, and 0.77 for 3-cluster to 8-cluster models,
respectively). Given these results, the 4-cluster to 6-cluster
models warranted further (pragmatic) evaluation.
For each of the 4-cluster to 6-cluster models, the lowest
average posterior probabilities were 0.86, 0.82, and 0.74, with
minimum cluster membership of 11.6%, 5.4%, and 4.2%,
respectively. Table 4 details descriptors for the 4-cluster to 6-
cluster models to aid in assessment of model distinctiveness. All
models displayed 1 stable rapid recovery cluster. There was
variation in the proportion of delayed, incomplete, persistent high,
and fluctuating pain recovery clusters with decreasing member-
ship at higher levels of pain persistence as successive clusters
were added. For example, the 5-cluster model was the first to
identify persistent high pain with 86 subjects, a potentially
important subgroup. Beyond the 5-cluster model, no increase
in clinically distinct subgroups was identified. For example, the
6-cluster model divided the number of incomplete recovery
clusters into 2, which added unnecessary complexity. Therefore,
the 5-cluster model was chosen and satisfied efficiency,
parsimony, and its ability to identify 5 discrete pain courses.
The 5-cluster model comprised cluster 1, rapid pain recovery
(n 5567; 35.8%); cluster 2, pain recovery by week 12 (n 5543;
34.3%); cluster 3, incomplete pain recovery (n 5222; 14.0%);
cluster 4, fluctuating pain (n 5167; 10.5%); and cluster 5, persistent
high pain (n 586; 5.4%). Figure 1 displays the proportion of
subjects reporting none, low–moderate, or high pain at each time
point for each cluster in the 5-cluster LCGA model. Figure 2
displays the mean pain course for the 5-cluster LCGA model.
Cluster 1 (rapid pain recovery) was characterized by an early
rapid decrease in the proportion of subjects who reported any
pain over the first 4 weeks (39%, 0%, and 0% at weeks 1, 2, and
4). At week 12, all subjects reported pain recovery. Subjects in
cluster 1 reported a large decrease in their mean pain score over
the first 2 weeks (25.7; SD 51.5).
Cluster 2 (pain recovery by week 12) was characterized by
a decrease in the proportion of subjects who reported any pain
over the first 4 weeks (93%, 83%, and 33% at weeks 1, 2, and 4).
By week 12, all subjects had pain recovery. Subjects in cluster 2
Table 2
Baseline characteristics for each cluster in the 5-cluster model.
All: clusters
(n 51585)
Cluster 1: rapid
(n 5567)
Cluster 2: recovery
by week 12
(n 5543)
Cluster 3:
incomplete recovery
(n 5222)
Cluster 4:
fluctuating pain
(n 5167)
Cluster 5: persistent
high pain
(n 586)
Taking paracetamol (%) 1056 (66.6) 366/567 (64.6) 365/543 (67.2) 155/222 (69.8) 111/167 (66.5) 59/86 (68.6)
Age (y) 45.15 (15.76) 43.48 (14.94) 44.94 (16.20) 45.21 (16.21) 50.07 (15.49) 47.67 (15.47)
Female (%) 741 (46.7) 260/563 (46.2) 243/542 (44.8) 113/222 (50.1) 83/167 (49.7) 42/85 (49.4)
Compensable (%) 111 (7.0) 33/566 (5.8) 43/541 (7.9) 13/222 (5.9) 11/167 (6.6) 11/85 (12.9)
Baseline pain 6.25 (1.89) 5.93 (1.99) 6.28 (1.86) 7.11 (1.39) 5.72 (1.93) 6.90 (1.51)
Back pain days 9.84 (9.92) 7.06 (8.15) 9.55 (9.69) 12.87 (10.35) 13.15 (11.5) 15.73 (11.2)
Pain beyond knee (%) 309 (19.5) 74/567 (13.1) 104/541 (19.2) 69/221 (31.1) 36/165 (21.8) 26/86 (30.2)
Previous episodes 6.85 (15.43) 5.52 (13.13) 6.17 (13.44) 7.36 (16.85) 10.07 (15.86) 11.83 (25.89)
Days of reduced activity 3.60 (5.9) 2.36 (3.62) 3.67 (5.55) 5.5 (8.03) 4.51 (7.74) 4.97 (7.93)
Risk of persistence 4.50 (2.7) 3.83 (2.77) 4.34 (2.71) 5.74 (2.53) 5.30 (2.61) 6.05 (2.47)
Sleep quality 1.50 (0.8) 1.43 (0.77) 1.49 (0.78) 1.71 (0.79) 1.56 (0.82) 1.76 (0.84)
Quality of life—physical 42.56 (10.14) 45.37 (9.74) 42.41 (9.7) 38.77 (10.38) 39.5 (9.27) 36.73 (10.01)
Quality of life—mental 44.56 (7.95) 46.06 (7.45) 45.09 (7.85) 42.03 (8.45) 43.33 (7.42) 39.47 (8.01)
Disability 13.03 (5.48) 12.43 (5.64) 13.14 (5.36) 14.25 (5.3) 12.63 (5.48) 14.08 (5.12)
Data are mean (SD) or number (%).
Table 1
Baseline predictor variables considered for multinomial logistic regression.
Baseline measure Mean (SD) or number (%) Recoded for regression analysis
Taking paracetamol 1056/1585 (66.6%)
Age 45.15 (15.75)
Female 741/1585 (46.80%)
Currently on workers’ compensation 111/1585 (7.00%)
Baseline pain (NRS) 6.25 (1.89)
Duration of low back pain at baseline (d) 9.84 (9.92) 0 if score #5; 1 if score .5 (median split)
Pain referral beyond knee 309 (19.5%)
Number of previous episodes 6.85 (15.14) 0 if score #1; 1 if score .1 (median split)
Risk that pain may become persistent 4.55 (2.79)
Quality of sleep 1.52 (0.79) 0 if score 0 and 1; 1 if score 2 and 3 (median split)
SF12v2 (PCS) 42.43 (10.15) 100-score
SF12v2 (MCS) 44.57 (7.95) 100-score
Duration of reduced activity (d) 3.62 (5.93)
RMDQ 13.04 (5.48)
Taking paracetamol—as required dosing or time contingent group. Baseline pain—numerical rating scale (NRS), scored from 0 (no pain) to 10 (worst possible pain). Risk of persistence “In your view, how large is the risk that
your current pain may become persistent?” scored from 0 (no risk) to 10 (very large risk). Sleep quality “During the past week, how would you rate your sleep quality overall?”, item 6 Pittsburgh sleep quality index, scored 0 (very
good), 1 (fairly good), 2 (fairly bad), or 3 (very bad). Quality of Life—physical (PCS) and mental (MCS) components from SF12v2; population mean 550 and SD 510. Duration (days) of reduced activity “In relation to the current
episode, on how many days did back or leg pain force you to cut down on the things you usually do, for more than half a day?” Disability—Roland Morris Disability Questionnaire (RMDQ), scored from 0 (no disability) to 24 (high
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reported a moderate decrease in mean pain over the first 2 weeks
(23.3; SD 50.1).
Cluster 3 (incomplete pain recovery)wascharacterizedby
over 12 weeks (95%, 82%, 38%, and 4% at weeks 1, 2, 4, and 12)
with a reciprocal increase in the proportion of low–moderate pain
over the same period (5%, 18%, 61%, and 77%). At week 12, the
LCGA model classified 19% (n 541) of subjects who reported no
pain at week 12 into this cluster. This was due to classification based
on subjects’ trajectory regardless ofrecoverystatus(unconditional
LCGA model). Subjects in cluster 3 reported only a small decrease in
mean pain over the first 2 weeks (21.5; SD 50.3).
Cluster 4 (fluctuating pain) was characterized by a diminishing
proportion of subjects who reported any pain over the first 4 weeks
(93%, 80%, and 66% at weeks 1, 2, and 4), followed by all subjects
reporting increased pain at week 12. At week 12, no subject had
pain recovery. Mean pain course over the first 4 weeks for subjects
classified with fluctuating pain was similar to those classified with
pain recovery by week 12, but with an increase in mean pain score
between week 4 and week 12 (1.9; SD 50.5).
Cluster 5 (persistent high pain) was comprised entirely subjects
who reported high pain over the first 4 weeks (98%, 100%, and
100% at weeks 1, 2, and 4). At week 12, all subjects continued to
report high pain. Subjects in cluster 5 reported negligible change
in mean pain scores over the first 2 weeks (0.0; SD 50.2), with
only marginal decrease in mean pain over the whole study period
(20.4; SD 50.2).
3.3. Multinomial logistic regression
Multivariate multinomial logistic regression models compared
baseline characteristics of all subjects between clusters, with
rapid pain recovery (cluster 1) as a reference. There was low
chance of collinearity (tolerance range 50.62-0.96; mean VIF 5
1.28) when all variables were included. The AIC statistic that
evaluated model best fit achieved a minimum value after removal
of the variables “trial allocation,” “quality of sleep,” and “disability”
52894) compared with the full model (AIC 52913).
Change in regression coefficients between the reduced and full
models ranged from 0% (age) to 5.5% (quality of life physical
subscale). Given the low chance of collinearity and marginal
difference in magnitude of regression coefficients between full
and reduced models, we were able to retain all a priori
hypothesized predictors. The final model included baseline
measures of age, sex, compensation status, pain (intensity,
duration, number of previous episodes, and pain beyond the
knee), duration of reduced activity, perceived risk of persistence,
quality of sleep, quality of life, disability, and trial group allocation.
Internal validation of the regression model, which used the
difference in parameter estimates (bias) between the study
sample and average of 1000 bootstrap samples for all covariates
in each cluster pair, was of lesser magnitude than the threshold
set for nonignorable bias (equal to 0.25 3SE of bootstrap
Compared to the study sample, the risk ratio for all
variables in the bootstrap sample was in the same direction and of
similar magnitude. Bootstrapping had the effect of changing
some marginally nonsignificant variables in the original analyses
to significant in the bootstrapped analyses but did not result in
important changes in the magnitude of the relative risk. For
example, when measuring relative risk of pain recovery by week
12 compared to rapid pain recovery, the relative risk for the
variable “previous episodes” changed from 1.29 (95% confi-
dence interval [CI], 0.89-1.86) in the original analyses to 1.30
(95% CI, 1.02-1.67) in the bootstrapped analyses.
3.4. Risk of belonging to delayed pain recovery clusters
compared with rapid pain recovery
Figure 3 reveals the relative risk of belonging to each cluster
compared to rapid pain recovery.
Relative risk ratios for continuous variables are expressed per
SD increase in the predictor variable.
Table 3
Goodness of fit criteria for 3-cluster to 8-cluster models. Shaded region indicates models considered for pragmatic evaluation.
Goodness of fit criteria Model
3-cluster 4-cluster 5-cluster 6-cluster 7-cluster 8-cluster
Sequential model comparison (K11 cluster vs
3 vs 2 4 vs 3 5 vs 4 6 vs 5 7 vs 6 8 vs 7
LL (K11) 25859.5 25815.6 25759.1 25736.9 25724.1 25712.3
22LL difference 498.9 87.7 121.5 44.5 25.6 23.6
BLRT 22LL diff (Pvalue) ,0.001 ,0.001 ,0.001 ,0.001 0.033
Information criterion
AIC 11743.0 11663.3 11558.3 11521.8 11504.2 11488.7
BIC 11807.4 11749.2 11665.7 11650.7 11654.5 11660.5
Entropy 0.789 0.789 0.777 0.751 0.771 0.772
22LL difference, change in LL as each additional cluster is added; AIC, Akaike’s information criterion; BIC, Bayesian information criterion; BLRT 22LL diff (Pvalue), bootstrap likelihood ratio test for (k21) vs k-class model
(P.0.05 suggests no further improvement in model fit by adding another cluster); Entropy, the ability of the model to provide well-separated clusters (range: 0-1 with higher values superior); LL, log-likelihood.
Table 4
Trajectory descriptors for the 4-cluster to 6-cluster solutions.
4-cluster model* 5-cluster model 6-cluster model
Cluster 1 36% rapid pain recovery 36% rapid pain recovery 36% rapid pain recovery
Cluster 2 39% pain recovery by week 12 34% pain recovery by week 12 20% pain recovery by week 4
Cluster 3 14% persistent pain 14% incomplete recovery 18% incomplete recovery type I
Cluster 4 12% fluctuating pain 11% fluctuating pain 17% incomplete recovery type II
Cluster 5 5% persistent high pain 5% persistent high pain
Cluster 6 4% fluctuating pain
* Cluster (column) membership does not sum to 100% because of rounding.
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3.4.1. Comparison of pain recovery by week 12 (cluster 2)
with rapid pain recovery (cluster 1)
Subjects with greater pain duration were 1.81 times more likely
(95% CI, 1.17-2.78) to recover by week 12 than have rapid pain
recovery. Subjects on workers’ compensation were 2.06 times
more likely (95% CI, 1.03-4.14) to recover by week 12 than have
rapid pain recovery.
3.4.2. Comparison of incomplete recovery (cluster 3) with
rapid pain recovery (cluster 1)
Subjects with higher baseline pain (RRR 52.00; 95% CI, 1.45-
2.85), of greater pain duration (RRR 53.41; 95% CI, 1.94-5.97),
increase in perceived risk of persistence (RRR 51.53; 95% CI,
1.16-2.07) and decrease in quality of life (mental, MCS) (RRR 5
1.44; 95% CI, 1.09-1.94), were more likely to have incomplete
pain recovery than have rapid pain recovery.
3.4.3. Comparison of fluctuating pain (cluster 4) with rapid
pain recovery (cluster 1)
Subjects of older age (RRR 51.56; 95% CI, 1.15-2.20), with
greater pain duration (RRR 52.68; 95% CI, 1.36-5.30), who had
multiple previous episodes (RRR 52.75; 95% CI, 1.43-5.31),
who believed they were at greater risk of persistence (RRR 5
1.74; 95% CI, 1.20-2.63), and who had lower physical (PCS)
(RRR 51.98; 95% CI, 1.31-3.13) and MCS (RRR 51.40; 95% CI,
1.02-1.99) quality of life scores were more likely to experience
fluctuating pain than have rapid pain recovery.
3.4.4. Comparison of persistent high pain (cluster 5) with
rapid pain recovery (cluster 1)
Subjects who were on workers’ compensation (RRR 53.84;
95% CI, 1.41-10.43), with higher baseline pain (RRR 51.88;
95% CI, 1.29-2.84), who had greater pain duration (RRR 5
4.89; 95% CI, 2.25-10.62), who believed that they were at
greater risk of persistence (RRR 51.66; 95% CI, 1.12-2.54),
and who had lower PCS (RRR 52.31; 95% CI, 1.46-3.83) and
MCS (RRR 52.06; 95% CI, 1.43-3.08) quality of life scores
were more likely to experience persistent high pain than have
rapid pain recovery.
4. Discussion
4.1. Summary of main findings
We identified 5 distinct pain trajectory patterns for patients with
acute nonspecific LBP presenting to primary care. Two of the
identified clusters included subjects who recovered from their
pain by 12 weeks but differed substantially in the rate at which
pain diminished and time of pain recovery. One cluster
comprised subjects whose pain diminished but did not recover
Figure 1. Proportion of subjects reporting pain recovery, low–moderate pain, or high pain at each time point for each of the 5 clusters.
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Figure 2. Mean pain course for each of the 5 trajectory groups in the 5-cluster latent class growth analysis model. Error bars are cluster SDs. NRS, numerical rating
230 A.S. Downie et al.·157 (2016) 225–234 PAIN
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Figure 3. Risk profile for each cluster compared to cluster 1 (rapid pain recovery). Plot of average pain over time for investigated cluster vs reference cluster is
provided to assist with interpretation of risk. Regression coefficients at .95% significance are coloured blue. *RRR per SD increase. †Quality of life reverse scored
(100-score), ie, greater risk with lower quality of life. RRR, relative risk ratio; CI, confidence interval.
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by 12 weeks, another cluster comprised subjects with
fluctuating pain, and the last contained subjects who experi-
enced persistent high-level pain over the study period.
Characteristics for each cluster identified were reported and
compared to the rapid pain recovery cluster. Compared to
rapid pain recovery, we found subjects who had pain duration
of greater than 5 days experienced delayed recovery or
nonrecovery by 12 weeks. Similarly, belief in greater risk of
persistence was associated with a lack of pain recovery at 12
weeks compared to rapid pain recovery.Higherpainintensity,
longer duration, and workers’ compensation were strongly
associated with persistent high pain, whereas older age and
increased number of episodes were uniquely associated with
fluctuating pain when compared to rapid pain recovery.
4.2. Strengths and limitations of the study
The key strengths of our study are the large inception cohort (n 5
1585, drawn from original sample of 1652) recruited from primary
care for a new episode of LBP,
with substantially complete data
($96%) for 5 time points over 3 months. Robust methodology
was used to determine distinct pain trajectory groups with
membership. We a priori selected predictors that we believed
likely to be important to the pain course, and the large sample size
allowed for inclusion of multiple predictors in the multivariate
regression model without overfitting.
The low number of subjects on workers’ compensation (n 5
102, 7%) influenced the CI for this predictor, but the prevalence
was similar to other studies investigating patients with acute LBP
presenting to primary care.
Although the data originate
from a clinical trial, there was no effect of treatment on time to
recovery within the trial, and allocation to active or placebo did not
discriminate membership between trajectory groups. Monitoring
beyond 12 weeks may have helped further define pain trajectories
(eg, place more subjects into the fluctuating trajectory group if
monitored over a longer period), but we were primarily concerned
with exploring latent trajectories for people with acute LBP and
believe this information is critical to patients and clinicians.
Patterns of recovery identified in other studies that followed
patients for 1 year
were also identified in our study (rapid
recovery, delayed recovery, fluctuation pain, and persistent high
pain), suggesting that patterns of persistent pain behavior may be
apparent early in the recovery from acute LBP.
This study does not seek to provide prediction of recovery
given the study design, but does profile the patient who is more
likely to follow one of the identified 5 pain courses. For example,
rapid pain recovery membership was associated with episode
duration (patients with pain greater than 5 days were nearly twice
as likely to recover at 12 weeks vs 2 weeks). Similarly, fluctuating
pain membership was associated with older age when compared
to rapid pain recovery, but older age was not associated with
incomplete pain recovery or persistent high pain trajectory
patterns when compared to rapid pain recovery.
4.3. Strengths and weaknesses in relation to other studies,
discussing important differences in results
This is the first large study to explore pain trajectories for patients
with only acute LBP. It extends the previous work in mixed
cohorts of patients with acute and chronic LBP by showing that
distinct pain trajectory groups are apparent early in the course of
the condition. Information on individual pain course is infrequently
but may be a key to understanding recovery when used
as an element within a prognostic framework.
Our study goes beyond previous studies,
have noted different trajectory groupings and differences in
membership, by identifying patient characteristics at baseline
that associate with pain course compared to ideal (rapid) pain
recovery. The large enrollment provided a base category of
more than 500 patients who recovered by week 2 to represent
rapid pain recovery. We have avoided the problems of previous
studies in this area by enrolling an inception cohort rather than
sampling prevalent cases
and by keeping loss to follow-up to
less than 5%, whereas in other studies, loss to follow-up has
been as high as 54%.
A number of psychosocial factors have been previously found
to be associated with pain trajectory groups (eg, anxiety,
and sick leave
). In our study, only a few
psychosocial factors were available, and so we can make only
limited inferences about the impact of psychological character-
istics on acute LBP trajectories.
4.4. Meaning of the study: implications for clinicians
and policymakers
This study provides strong evidence that averaged pain
trajectories for all people with LBP, which are widely
do not adequately reflect the complexity of the
clinical course. The different trajectory patterns potentially
represent subgroups, which may logically require quite
different interventions. For example, engaging early intensive
therapy to address biopsychological factors may be effective
for patients likely to follow a persistent pain trajectory, whereas
those likely to recover by week 4 may require only watchful
waiting or minimal intervention. Patients likely to improve early
but then worsen may represent a group prone to recurrence
that would benefit from effective strategies to prevent re-
currence. Further research is required to develop simple
approaches for clinicians to identify patients likely to belong
to a particular cluster and also test if targeting treatments
based on likely cluster membership lead to improved out-
comes. This study provides further argument that defining
chronicity based on time may be overly simplistic,
given that
differentiation of our persistent high pain group occurred as
early as week 2 and had a distinctive baseline profile compared
to patients who had rapid pain recovery.
4.5. Unanswered questions and future research
A key research question is whether management focused on
the patient attributes associated with cluster membership can
effectively shift a patient from a less favorable course to a more
favorable course or whether the identified pain courses
represent patient phenotypes.
Another question is
whether communicating pain course heterogeneity in a graph-
ical way during the clinical encounter could assist patient
understanding of pain course
or support recovery through
modification of pain beliefs.
Further investigation is also
required to determine whether simple clinical tools,
appropriate for use in clinical practice, can be developed that
enable early identification of patients who will follow each
trajectory pattern. If this is achieved, it would allow clinicians to
provide more individualized information on course to their
Conflict of interest statement
The authors have no conflicts of interest to declare.
232 A.S. Downie et al.·157 (2016) 225–234 PAIN
2015 by the International Association for the Study of Pain. Unauthorized reproduction of this article is prohibited.
This research received no specific grant from any funding
agency in the public, commercial, or not-for-profit sectors. A. S.
Downie and M. J. Hancock are employed by Macquarie
University; C. -W. C. Lin and C. G. Maher are employed by the
George Institute for Global Health; C. G. Maher is supported by
National Health and Medical Research Council Fellowship
(APP1002081); C. -W. C. Lin is supported by a Career De-
velopment Fellowship from the National Health and Medical
Research Council, Australia (APP1061400); and C. M. Williams is
employed by Hunter Medical Research Institute.
Article history:
Received 14 April 2015
Received in revised form 17 August 2015
Accepted 31 August 2015
Available online 7 September 2015
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2015 by the International Association for the Study of Pain. Unauthorized reproduction of this article is prohibited.
... The pain scores were categorized as "no pain" (NRS1), "mild-moderate pain" (NRS=2À4), and "severe pain" (NRS5). 9,32 Similarly, disability scores were grouped into three categories: "no disability" (RMDQ<4), "mild-moderate disability" (RMDQ=4À14), and "severe disability" (RMDQ>14). 20,33,34 The LCGA linear model was used, unconditionally and without the inclusion of covariables. ...
... 38 However, for a better fit of the LCGA model, those with "missing data" in more than two of the five assessments were excluded from the analyses. 9 We used one-way analysis of variance (ANOVA) (continuous variables) or the chi-square test (categorical variables) to determine whether baseline biopsychosocial health characteristics were different among the trajectory subgroups. The Bonferroni post-hoc test or the adjusted residual value was used to define significant differences in trajectory comparisons by one-way ANOVA and the chi-square test, respectively. ...
... LBP trajectories have already been reported in different studies and populations, 9,[11][12][13]32,39 with high variability in the number and size of the subgroups. Our results corroborate this variability; however, a detailed comparison of our results is more adequate with the trajectories of the two studies with older adults. ...
Background Although the literature describes a favorable course of low back pain (LBP) in adults, little information is available for older adults. LBP is prevalent and complex in older adults, and the analysis of its trajectories may contribute to the improvement of therapeutic approaches. Objective To describe pain and disability trajectories in older adults with a new episode of LBP. Methods Older adults (n = 542), aged >55 years with a new episode of nonspecific LBP, were followed for 12 months in a prospective cohort. Pain intensity (Numeric Rating Scale) and disability (Roland-Morris Disability Questionnaire) were assessed at baseline and 3, 6, 9, and 12 months. Latent class growth analysis (LCGA) was used to model pain and disability scores over time. Baseline biopsychosocial characteristics were compared using one-way analysis of variance or Chi-square test (α=0.05). Results The LCGA identified three and four trajectories in the pain and disability courses, respectively. Trajectories with low, intermediate, or high scores over time were defined. Worse biopsychosocial status at baseline was associated with worst prognosis over 12 months. Low educational level, physical inactivity, poor mobility, recent falls, worse fall self-efficacy, presence of depressive symptoms, more kinesiophobia, greater number of comorbidities, and the presence of other LBP-associated complaints were found in older adults with severe and persistent symptoms. Conclusion The trajectories allow the identification of clusters with similar clinical prognoses in older adults with a new episode of LPB. In practice, excessive treatments and unnecessary tests can be avoided, while more accurate and targeted interventions can be implemented.
... The proportion of patients in each cluster, however, differed, possibly because of variations in the study designs. Patients were recruited in General Practice [6][7][8]27], at chiropractic clinics [10,28], combined in General Practice and Chiropractic clinics [29], combined in General Practice and physiotherapy practices [30], or in a population based survey [31]. Also, studies varied in recruiting patients: i.e. ...
... Also, studies varied in recruiting patients: i.e. only patients with chronic [30], only acute [27], or a mix of both chronic and acute LBP [6-8, 10, 28, 29, 31]. Moreover, followup periods varied from 12 weeks [27] to one year [10,[29][30][31], and follow-up measurements varied form weekly text messages [10,28,29] to monthly questionnaires [6][7][8]. ...
... only patients with chronic [30], only acute [27], or a mix of both chronic and acute LBP [6-8, 10, 28, 29, 31]. Moreover, followup periods varied from 12 weeks [27] to one year [10,[29][30][31], and follow-up measurements varied form weekly text messages [10,28,29] to monthly questionnaires [6][7][8]. The population based study was the only study in which a cluster showing improvement was not reported [31]. ...
Full-text available
Background In The Netherlands, low back pain patients can consult physicians specialized in musculoskeletal (MSK) medicine. Previous studies have reported on the characteristics of patients consulting MSK physicians, and the treatment options used. There are no studies yet reporting on the course of Low Back Pain (LBP) after treatment by musculoskeletal (MSK) physicians in The Netherlands. Methods In an observational cohort study MSK physicians recorded data about all low back pain patients presenting for a first consultation. At baseline they recorded age, gender, type and duration of the main complaint, and concomitant complaints. At the end of treatment they recorded the type of treatment and the number of treatment sessions. Patients were recruited to answer questionnaires at baseline, and at 6-weekly intervals during a follow-up period of six months. Patient questionnaires included information about previous medical consumption, together with PROMs measuring the level of pain and functional status. Latent Class Growth Analysis (LCGA) was used to classify patients into different groups according to their pain trajectories. Baseline variables were evaluated as predictors of a favourable trajectory using logistic regression analyses, and treatment variables were evaluated as possible confounders. Results A total of 1377 patients were recruited, of whom 1117 patients (81%) answered at least one follow-up measurement. LCGA identified three groups of patients with distinct pain trajectories. A first group (N = 226) with high pain levels showed no improvement, a second group (N = 578) with high pain levels showed strong improvement, and a third group (N = 313) with mild pain levels showed moderate improvement. The two groups of patients presenting with high baseline pain scores were compared, and a multivariable model was constructed with possible predictors of a favourable course. Male gender, previous specialist visit, previous pain clinic visit, having work, a shorter duration of the current episode, and a longer time since the complaints first started were predictors of a favourable course. The multivariable model showed a moderate area under the curve (0.68) and a low explained variance (0.09). Conclusions In low back pain patients treated by musculoskeletal physicians in The Netherlands three different pain trajectories were identified. Baseline variables were of limited value in predicting a favourable course.
... Although a number of studies have assessed trajectories of pain in LBP, 5,[7][8][9][10][11][12][13][14][15][16]22 fewer studies have assessed trajectories of disability. 6,9 Only Deyo et al. 9 aimed to identify subgroups and this was in older adults (.65 years) with no psychological predictors in the models. ...
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Introduction: Low back pain (LBP) is the leading course of years lived with disability. Unfortunately, not much knowledge exists about distinct trajectories of recovery from disability after LBP and their potential psychological predictors. Objectives: Hence, the aim of the present study was to identify trajectories of functional disability in LBP and their potential baseline psychological predictors. Methods: A 1-year consecutive cohort (N = 1048) of patients with LBP referred to the Spine Centre if they have not improved satisfactorily from a course of treatment in primary care after 1 to 2 months were assessed by self-report questionnaires at their first visit and at 6- and 12-month follow-up. Data from patients who responded to the Roland Morris Disability Questionnaire at least twice (N = 747) were used to assess trajectories of functional disability by Latent Growth Mixture Modeling. The following measures were used as baseline predictors of the trajectories: Pain Intensity Numerical Rating Scales, Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia, and Hospital Anxiety and Depression Scale. Results: Four distinct trajectories were identified: high-stable (22.0%), high-decreasing (20.4%), medium-stable (29.7%), and low-decreasing (27.9%). Using the low-decreasing trajectory as reference, baseline pain intensity, depressive symptoms, and pain-catastrophizing predicted membership of all 3 symptomatic trajectories. However, using the high-decreasing trajectory as reference, age, baseline pain intensity, and depression were predictors of the high-stable trajectory. Conclusion: In particular, the finding of a high-stable trajectory characterized by high levels of baseline psychological distress is of potential clinical importance because psychological distress may be targeted by cognitive behavioral therapeutic approaches.
... Psychosocial factors, such as low workplace social support and low job satisfaction, have crucial roles in NLBP (5). Although most cases of NLBP spontaneously resolve after several weeks to 3 months (6,7), 14% of patients experience chronic NLBP (CNLBP), which is defined as NLBP that lasts for over 3 months (8). Furthermore, a small percentage of patients (2%-7%) experience intense, persistent, CNLBP (5,7). ...
Background: Associations between attention-deficit/hyperactivity disorder (ADHD) and chronic pain disorders, such as fibromyalgia, have been reported. However, associations between persistent chronic nonspecific low back pain (CNLBP) and ADHD have not yet been investigated. Objectives: This study aimed to investigate the positive rates of possible ADHD, as assessed by self-reported ADHD scales, in patients with persistent CNLBP, using data from self-reported questionnaires completed by patients and their families. This study also aimed to compare the self-reported scores obtained from existing standardized data for healthy individuals, and to examine whether the ADHD scale scores of patients with persistent CNLBP are associated with pain variables. Study design: Cross-sectional study. Setting: The specialized pain clinic at our university hospital. Methods: This cross-sectional study included 60 consecutive patients with persistent CNLBP who were diagnosed with a possible somatic symptom disorder and were referred to a psychiatrist in our pain clinic. The Conners' Adult ADHD Rating Scales (CAARS) self-report (CAARS-S) and observer-rated (CAARS-O) questionnaires were utilized. We investigated the CAARS scores, and the association between the CAARS subscale scores and pain variables (pain duration and pain Numeric Rating Scale) in patients with persistent CNLBP. Results: Of the 60 patients, 19 (31.7%) were positive on both CAARS-S and CAARS-O questionnaires (T-score > 65). The ADHD indices, which comprised subscales of the CAARS estimating the necessity of treatment for ADHD, were significantly higher in both male and female patients with persistent CNLBP than in the Japanese standardized sample (P < 0.005). CAARS-S hyperactivity/restlessness, CAARS-O hyperactivity/restlessness, and the Diagnostic and Statistical Manual of Mental Disorders, fourth edition hyperactive-impulsive symptom subscale scores also correlated with the pain intensity (P < 0.05). Limitations: In this study, ADHD tendency was evaluated using only a self-reported questionnaire. Hence in the future, accurate and precise assessments of ADHD symptoms using structured clinical interviews conducted by ADHD experts are warranted. Additionally, the study only included patients with persistent CNLBP. Therefore in the future, it will be valuable to investigate ADHD scale scores (e.g., CAARS) among patients with CNLBP and nonspecific low back pain with larger sample sizes. Conclusions: Our findings revealed that the subscale scores on an ADHD scale were considerably high in patients with persistent CNLBP. As a previous study of our clinical experience indicates that persistent CNLBP can be substantially relieved by administering ADHD medications, ADHD screening is warranted in the treatment of persistent CNLBP.
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Worldwide, low back pain has been the leading cause of disability since 1990. Although most people recover rather quickly, one in five adults develops chronic low back pain (CLBP). The main aim of this thesis was to gain more insight into the impact and course of CLBP in patients referred to a multidisciplinary spine center. The results showed that for these patients the personal and societal impact of CLBP is very high. Specifically, quality of life and work ability were very poor. Moreover, average health care costs in the year before referral to the spine center were twice as high compared to patients seeking primary LBP care. Furthermore, the studies in this thesis show that most patients that are referred to multidisciplinary spine care are likely to belong to subgroups that experience a pattern of little to no improvement in LBP symptoms during two years follow-up. However, predicting the course of CLBP symptoms remains challenging. Finally, the thesis contains several messages with regards to CLBP research. In order to really understand both the (natural) course of CLBP and results of interventions over time, frequent measurements of relevant patient-centered outcomes are needed, as well as the use of complete core outcome sets that reflect the multidimensionality of CLBP. Furthermore, since the population-averaged course of CLBP and summary measures of treatment effects do not adequately reflect the underlying patterns of CLBP, research should focus on clinically relevant patient subgroups.
Background: Imbalance or decreased trunk strength has been associated with non-specific low back pain (NSLBP). Objective: This systematic review aimed (I) to evaluate the quality of evidence of studies evaluating the reliability of trunk strength assessment with an isokinetic dynamometer in NSLBP patients, (II) to examine the reliability of trunk strength assessment using an isokinetic dynamometer in NSLBP patients and (III) to determine the most reliable protocol for trunk strength assessment in NSLBP patients. Method: PRISMA guidelines were followed. Three databases were used: PubMed, Scopus, and Web of Science with the following keywords: Isokinetic, Dynamometer, Trunk strength testing, Muscle testing, Isokinetic measurement, CORE, Abdominal muscles, Abdominal wall, Torso, Trunk, Spine, Reliability and, Reproducibility. We included only test-retest studies, focused on the reliability of isometric and isokinetic strength assessed with an isokinetic dynamometer in NSLBP adults' patients, published in English and from inception to March 30, 2021. The methodological quality was evaluated with the CAT scale and QAREL checklist. Results: Five hundred and seventy-seven articles were retrieved, of which five are included in this review. Three articles provide good quality of evidence, the reliability of trunk strength assessment in NSLBP patients is excellent, and the most reliable protocol for isometric assessment is in a seated position (ICC = 0.94-0.98) and for isokinetic strength in standing position, at 60∘/s and 120∘/s (ICC = 0.98). Conclusion: There is good quality evidence regarding the trunk strength assessment's reliability. Reliability is excellent in NSLBP patients; however, a familiarization process should be considered to obtain clinically reliable data. The most reliable protocol is in a seated position for isometric strength and a standing position for isokinetic strength.
Introduction. In the structure of the global burden of diseases, back pain ranks first in the world in the list of causes of disability. Neck pain is one of the most common diseases of the musculoskeletal system. An integral part of the strategy for the treatment of acute neck pain is the use of original nonsteroidal anti-inflammatory drugs (NSAIDs) with high proven effectiveness. Objective . To conduct a comparative analysis of the therapeutic efficacy of the original drug meloxicam (Movalis) and its generic (Amelotex) in the treatment of patients with acute nonspecific musculoskeletal pain of cervical localization. Materials and methods. The article provides an assessment of the therapeutic effectiveness of the original drug meloxicam (Movalis) and its generic (Amelotex) in the treatment of patients with acute neck pain. Comparative analysis of the results of treatment of 108 patients hospitalized in the Branch of the MC JSC “Admiralteyskie Verfi” aged 18 to 60 years (58 men and 50 women) with a diagnosis of acute dorsalgia of cervical localization (M54.2 according to ICD-10). The average age of patients is 42.5 ± 11.1 years. Results . As a result of the clinical and neurological study and statistical processing of the data obtained, it was found that the use of Movalis in the treatment of patients with acute neck pain was more effective compared to the use of generic (Amelotex). In the Movalis group, the level of pain intensity and the index of disability after treatment was significantly lower than in the Amelotex group at an equivalent dose of 15 mg per day (p < 0.01). The analgesic effect in the group of patients receiving Movalis was longer. There was a significant decrease in sleep disorders associated with pain syndrome in the group (Movalis) compared with patients of the second group (Amelotex). Conclusions. The absence of therapeutic bioequivalence between Movalis and Amelotex revealed in our study determines the relevance of the choice of drugs for the complex therapy of patients of this profile. A multimodal approach to the strategy of treating neck pain, individually patient-oriented, including the use of effective and safe medicines, physical exercises, manual therapy, is optimal.>< 0.01). The analgesic effect in the group of patients receiving Movalis was longer. There was a significant decrease in sleep disorders associated with pain syndrome in the group (Movalis) compared with patients of the second group (Amelotex). Conclusions . The absence of therapeutic bioequivalence between Movalis and Amelotex revealed in our study determines the relevance of the choice of drugs for the complex therapy of patients of this profile. A multimodal approach to the strategy of treating neck pain, individually patient-oriented, including the use of effective and safe medicines, physical exercises, manual therapy, is optimal.
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PurposeThe COVID-19 pandemic and the containment measures such as social distancing, mobility restrictions and quarantine have significantly impacted the delivery of healthcare services, with possible negative effects on low back pain patients. In this study, we used an innovative agent-based model to quantify the effects of COVID-19 on the prevalence and severity of low back pain in the general population.Methods Epidemiological data were used to simulate the low back pain evolution in a population of 300,000 agents. Reduced access to treatment due to the containment measures was simulated with a probabilistic approach, in which 500 random scenarios (differing in: length of the lockdown, probability of having access to treatment, time before the resumption of treatment, duration of the effects of the treatment after its interruption) were simulated.ResultsThe lockdown may increase the mean pain score higher than 1/10 points for patients suffering from acute low back pain, up to 4–5/10 points for specific individuals. The lockdown also affected the length of pain episodes, possibly impacting chronicity and disability. All the variables describing the random scenarios had a relevant impact in determining both the increase of pain intensity in the population and the length of the effects of the lockdown.Conclusions“Optimal lockdown parameters” which minimize the impact on low back pain while preserving the effects on infection spread and mortality could not be identified. Policies favouring a prompt resumption of treatments after the lockdown may be effective in shortening the duration of its negative effects.
Purpose: Physiotherapists are key providers of care for patients with low back pain (LBP); however, information on Canadian physiotherapists’ use of evidence-based clinical practice guidelines (EBCPGs) for LBP is lacking. We aimed to (1) describe Canadian physiotherapists’ adherence to EBCPGs for LBP; (2) compare beliefs and attitudes of physiotherapists with higher and lower adherence; (3) identify predictors of adherence; and (4) gather physiotherapists’ perceptions about the care provided to patients with LBP. Method: This mixed methods study involved two phases: (1) a survey containing a LBP clinical scenario and (2) qualitative semi-structured interviews with physiotherapists. Results: A total of 406 (77%) of the 525 survey respondents demonstrated higher adherence (score of 3 or 4) to EBCPGs; however, only 29.5% chose interventions to address psychosocial issues. Postgraduate training was the strongest predictor of higher adherence. Interviewed physiotherapists reported being highly satisfied with the care provided to patients with LBP even when psychosocial issues are present, despite low confidence in addressing those issues. Conclusions:Although overall adherence was high, Canadian physiotherapists do not frequently address psychosocial issues with LBP patients, and often do not feel confident or competent in that aspect of practice. This suggests an opportunity for developing additional training for addressing psychosocial issues in LBP patients.
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Objective The Traumatic Injuries Distress Scale (TIDS) is a 12-item self-report tool intended for prognostic risk phenotyping in people with acute musculoskeletal (MSK) trauma. The initial validation study showed good associations with outcomes 12 weeks later in a cohort of 72 acutely injured patients from one region in Canada. This study aims to provide further clinical utility through identification of meaningful cut scores in a larger, mixed geography sample, and expands the prediction window from 12 to 52 weeks. Methods Data were drawn from databanks in London, Canada and Chicago, United States. Participants were recruited within 3 weeks of non-catastrophic MSK trauma and followed for 12 months. Using outcomes trajectories, the TIDS underwent linear regression-based analysis to predict 52-week outcomes, and area under the receiver operating characteristic curves to identify discriminative accuracy and meaningful cut scores. Results N = 224 participants with acute trauma were followed and both %Interference and Pain Severity were captured at intake and 3 follow-ups to establish curvilinear recovery trajectories. The TIDS explained significant variance in both the interference and severity outcomes after controlling for sex, region of injury, and baseline scores. ROC analysis revealed significant discriminative accuracy for predicting both the trajectories and the distal outcomes over 52 weeks. The TIDS was more accurate for identifying the low-risk than high-risk patients. Conclusion The TIDS is a useful tool for ‘ruling out’ high risk of poor outcome in a mixed sample of participants from two different countries. Impact statement The TIDS will be a useful tool for clinicians to predict the rate of recovery by displaying meaningful cut-scores for their patients after an acute musculoskeletal injury. This could lead to reduced burden of care for low risk patients and more informed treatment options for higher risk patients.
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Researchers using latent class (LC) analysis often proceed using the following three steps: (1) an LC model is built for a set of response variables, (2) subjects are assigned to LCs based on their posterior class membership probabilities, and (3) the association between the assigned class membership and external variables is investigated using simple cross-tabulations or multinomial logistic regression analysis. Bolck, Croon, and Hagenaars (2004) demonstrated that such a three-step approach underestimates the associations between covariates and class membership. They proposed resolving this problem by means of a specific correction method that involves modifying the third step. In this article, I extend the correction method of Bolck, Croon, and Hagenaars by showing that it involves maximizing a weighted log-likelihood function for clustered data. This conceptualization makes it possible to apply the method not only with categorical but also with continuous explanatory variables, to obtain correct tests using complex sampling variance estimation methods, and to implement it in standard software for logistic regression analysis. In addition, a new maximum likelihood (ML)-based correction method is proposed, which is more direct in the sense that it does not require analyzing weighted data. This new three-step ML method can be easily implemented in software for LC analysis. The reported simulation study shows that both correction methods perform very well in the sense that their parameter estimates and their SEs can be trusted, except for situations with very poorly separated classes. The main advantage of the ML method compared with the Bolck, Croon, and Hagenaars approach is that it is much more efficient and almost as efficient as one-step ML estimation. © The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved.
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Diagnosis is the traditional basis for decision-making in clinical practice. Evidence is often lacking about future benefits and harms of these decisions for patients diagnosed with and without disease. We propose that a model of clinical practice focused on patient prognosis and predicting the likelihood of future outcomes may be more useful. Disease diagnosis can provide crucial information for clinical decisions that influence outcome in serious acute illness. However, the central role of diagnosis in clinical practice is challenged by evidence that it does not always benefit patients and that factors other than disease are important in determining patient outcome. The concept of disease as a dichotomous 'yes' or 'no' is challenged by the frequent use of diagnostic indicators with continuous distributions, such as blood sugar, which are better understood as contributing information about the probability of a patient's future outcome. Moreover, many illnesses, such as chronic fatigue, cannot usefully be labelled from a disease-diagnosis perspective. In such cases, a prognostic model provides an alternative framework for clinical practice that extends beyond disease and diagnosis and incorporates a wide range of information to predict future patient outcomes and to guide decisions to improve them. Such information embraces non-disease factors and genetic and other biomarkers which influence outcome. Patient prognosis can provide the framework for modern clinical practice to integrate information from the expanding biological, social, and clinical database for more effective and efficient care.
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Background context: The clinical presentation and outcome of patients with nonspecific low back pain (LBP) are very heterogeneous and may be better understood by the recognition of reproducible subgroups. One approach to subgrouping is the identification of clinical course patterns (trajectories). However, it has been unclear how dependent these trajectories are on the analytical model used and the pain characteristics included. Purpose: To identify LBP trajectories using LBP intensity and frequency measured once a week over 1 year and compare the results obtained using different analytical approaches. Study design: A prospective observational cohort study. Patient sample: Patients presenting with nonspecific LBP to general practitioners and chiropractors. Outcome measures: Weekly self-report of LBP intensity (0-10) and the number of LBP days measured by short message service cell phone questions over a 1-year follow-up period. Methods: Latent class analysis was used to identify the trajectories of LBP and 12 different analytical models were compared. The study was a component of a broader study funded by an unrestricted grant from the Danish Chiropractors' Foundation (USD 370,000). Results: The study included 1,082 patients. The 12 models resulted in 5 to 12 subgroups, with a number of trajectories stable across models that differed on pain intensity, number of LBP days, and shape of trajectory. Conclusions: The clinical course of LBP is complex. Most primary care patients do not become pain-free within a year, but only a small proportion reports constant severe pain. Some distinct patterns exist which were identified independently of the way the outcome was modeled. These patterns would not be revealed by using the simple summary measures traditionally applied in LBP research or when describing a patient's pain history only in terms of duration. The appropriate number of subgroups will depend on the intended purpose of subgrouping.
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Background: In Western Europe, low back pain has the greatest burden of all diseases. When back pain persists, different medical specialists are involved and a lack of consensus exists among these specialists for medical decision-making in Chronic Low Back Pain (CLBP). Objective: To develop a decision tool for secondary or tertiary spine care specialists to decide which patients with CLBP should be seen by a spine surgeon or by other non-surgical medical specialists. Methods: A Delphi study was performed to identify indicators predicting the outcome of interventions. In the preparatory stage evidence from international guidelines and literature were summarized. Eligible studies were reviews and longitudinal studies. Inclusion criteria: surgical or non-surgical interventions and persistence of complaints, CLBP-patients aged 18-65 years, reported baseline measures of predictive indicators, and one or more reported outcomes had to assess functional status, quality of life, pain intensity, employment status or a composite score. Subsequently, a three-round Delphi procedure, to reach consensus on candidate indicators, was performed among a multidisciplinary panel of 29 CLBP-professionals (>five years CLBP-experience). The pre-set threshold for general agreement was ≥70%. The final indicator set was used to develop a clinical decision tool. Results: A draft list with 53 candidate indicators (38 with conclusive evidence and 15 with inconclusive evidence) was included for the Delphi study. Consensus was reached to include 47 indicators. A first version of the decision tool was developed, consisting of a web-based screening questionnaire and a provisional decision algorithm. Conclusions: This is the first clinical decision tool based on current scientific evidence and formal multidisciplinary consensus that helps referring the patient for consultation to a spine surgeon or a non-surgical spine care specialist. We expect that this tool considerably helps in clinical decision-making spine care, thereby improving efficient use of scarce sources and the outcomes of spinal interventions.
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To describe long-term trajectories of back pain. Monthly data collection for 6 months at 7-year follow-up of participants in a prospective cohort study. Primary care practices in Staffordshire, UK. 228 people consulting their general practitioners with back pain, on whom information on 6-month back pain trajectories had been collected during 2001-2003, and who had valid consent and contact details in 2009-2010, were contacted. 155 participants (68% of those contacted) responded and provided sufficient data for primary analyses. Trajectories based on patients' self-reports of back pain were identified using longitudinal latent class analysis. Trajectories were characterised using information on disability, psychological status and presence of other symptoms. Four clusters with different back pain trajectories at follow-up were identified: (1) no or occasional pain, (2) persistent mild pain, (3) fluctuating pain and (4) persistent severe pain. Trajectory clusters differed significantly from each other in terms of disability, psychological status and other symptoms. Most participants remained in a similar trajectory as 7 years previously (weighted κ 0.54; 95% CI 0.42 to 0.65). Most people with back pain appear to follow a particular pain trajectory over long time periods, and do not have frequently recurring or widely fluctuating patterns. The results are limited by lack of information about the time between data collection periods and by loss to follow-up. However, findings do raise questions about standard divisions into acute and chronic back pain. A new framework for understanding the course of back pain is proposed.
Acute low back pain is a common reason for patient calls or visits to a primary care clinician. Despite a large differential diagnosis, the precise etiology is rarely identified, although musculoligamentous processes are usually suspected, For most patients, back symptoms are nonspecific, meaning that there is no evidence for radicular symptoms or underlying systemic disease. Because episodes of acute, nonspecific low back pain are usually self-limited, many patients treat themselves without contacting their primary care clinician, When patients do call or schedule a visit, evaluation and management by primary care clinicians is appropriate. The history and physical. examination usually provide clues to the rare but potentially serious causes of low back pain, as well as identify patients at risk for prolonged recovery, Diagnostic testing, including plain x-rays, is often unnecessary during the initial evaluation, For patients with acute, nonspecific low back pain, the primary emphasis of treatment should be conservative care, time, reassurance, and education. Current recommendations focus on activity as tolerated (though not active exercise while pain is severe) and minimal if any bed rest. Referral for physical treatments is most appropriate for patients whose symptoms are not improving over 2 to 4 weeks. Specialty referral should be considered for patients with a progressive neurologic deficit, failure of conservative therapy, or an uncertain or serious diagnosis. The prognosis for most patients is good, although recurrence is common. Thus, educating patients about the natural history of acute low back pain and how to prevent future episodes can help ensure reasonable expectations.
Background Regular paracetamol is the recommended first-line analgesic for acute low-back pain; however, no high-quality evidence supports this recommendation. We aimed to assess the efficacy of paracetamol taken regularly or as-needed to improve time to recovery from pain, compared with placebo, in patients with low-back pain. Methods We did a multicentre, double-dummy, randomised, placebo controlled trial across 235 primary care centres in Sydney, Australia, from Nov 11, 2009, to March 5, 2013. We randomly allocated patients with acute low-back pain in a 1:1:1 ratio to receive up to 4 weeks of regular doses of paracetamol (three times per day; equivalent to 3990 mg paracetamol per day), as-needed doses of paracetamol (taken when needed for pain relief; maximum 4000 mg paracetamol per day), or placebo. Randomisation was done according to a centralised randomisation schedule prepared by a researcher who was not involved in patient recruitment or data collection. Patients and staff at all sites were masked to treatment allocation. All participants received best-evidence advice and were followed up for 3 months. The primary outcome was time until recovery from low-back pain, with recovery defined as a pain score of 0 or 1 (on a 0–10 pain scale) sustained for 7 consecutive days. All data were analysed by intention to treat. This study is registered with the Australian and New Zealand Clinical Trial Registry, number ACTN 12609000966291. Findings 550 participants were assigned to the regular group (550 analysed), 549 were assigned to the as-needed group (546 analysed), and 553 were assigned to the placebo group (547 analysed). Median time to recovery was 17 days (95% CI 14–19) in the regular group, 17 days (15–20) in the as-needed group, and 16 days (14–20) in the placebo group (regular vs placebo hazard ratio 0·99, 95% CI 0·87–1·14; as-needed vs placebo 1·05, 0·92–1·19; regular vs as-needed 1·05, 0·92–1·20). We recorded no difference between treatment groups for time to recovery (adjusted p=0·79). Adherence to regular tablets (median tablets consumed per participant per day of maximum 6; 4·0 [IQR 1·6–5·7] in the regular group, 3·9 [1·5–5·6] in the as-needed group, and 4·0 [1·5–5·7] in the placebo group), and number of participants reporting adverse events (99 [18·5%] in the regular group, 99 [18·7%] in the as-needed group, and 98 [18·5%] in the placebo group) were similar between groups. Interpretation Our findings suggest that regular or as-needed dosing with paracetamol does not affect recovery time compared with placebo in low-back pain, and question the universal endorsement of paracetamol in this patient group. Funding National Health and Medical Research Council of Australia and GlaxoSmithKline Australia.
It has been suggested that low back pain (LBP) is a condition with an unpredictable pattern of exacerbation, remission and recurrence. However, there is an incomplete understanding of the course of LBP and the determinants of the course. To identify clusters of LBP patients with similar fluctuating pain patterns over time and investigate whether demographic and clinical characteristics can distinguish these clusters. This study was a secondary analysis of data extracted from a randomized controlled trial. Pain scores were collected from 155 participants with chronic nonspecific LBP. Pain intensity was measured once a month over one year by mobile phone. Cluster analysis was used to identify participants with similar fluctuating patterns of pain based on the pain measures collected over a year. We used t-tests to evaluate if the clusters differed in terms of baseline characteristics. The clusters analysis revealed the presence of 3 main clusters, two of which were considered to be of fluctuating nature. Overall, 21 (13.5%) of individuals had fluctuating pain. Baseline disability (Roland Morris,-24) and treatment groups (from the initial randomized controlled trial) were significantly different between clusters of patients with fluctuating pain or not. Limitations of this study include the fact that participants were undergoing treatment, which could ultimately be responsible for the rather positive prognosis observed. We have identified a small number of patients with fluctuating patterns of pain over time. This number may increase if individuals with episodic pain are included within this fluctuating group.