Article

A treatment-based classification algorithm for low back pain (LBP)
was created to help clinicians select treatments to which people are most likely
to respond. To allow the algorithm to classify all people with LBP, additional
criteria can help therapists make decisions for people who do not clearly fit
into a subgroup (ie, unclear classifications). Recent studies indicated that
classifications are unclear for approximately 34% of people with LBP. OBJECTIVE:
To guide improvements in the algorithm, it is imperative to determine whether
people with unclear classifications are different from those with clear
classifications. DESIGN: This study was a secondary analysis of data from 3
previous studies investigating the algorithm. METHODS: Baseline data from 529
people who had LBP were used (3 discrete cohorts). The primary outcome was type
of classification, that is, clear or unclear. Univariate logistic regression was
used to determine which participant variables were related to having an unclear
classification. RESULTS: People with unclear classifications had greater odds of
being older (odds ratio [OR]=1.01, 95% confidence interval [CI]=1.003-1.033),
having a longer duration of LBP (OR=1.001, 95% CI=1.000-1.001), having had a
previous episode(s) of LBP (OR=1.61, 95% CI=1.04-2.49), having fewer
fear-avoidance beliefs related to both work (OR=0.98, 95% CI=0.96-0.99) and
physical activity (OR=0.98, 95% CI=0.96-0.996), and having less LBP-related
disability (OR=0.98, 95% CI=0.96-0.99) than people with clear classifications.
LIMITATIONS: Studies from which participant data were drawn had different
inclusion criteria and clinical settings. CONCLUSIONS: People with unclear
classifications appeared to be less affected by LBP (less disability and fewer
fear avoidance beliefs), despite typically having a longer duration of LBP.
Future studies should investigate whether modifying the algorithm to exclude such
people or provide them with different interventions improves outcomes.

Langue : ANGLAIS