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General section
Review
Multidimensional screening for predicting pain
problems in adults: a systematic review of
screening tools and validation studies
Elke Veirman
a,
*, Dimitri M. L. Van Ryckeghem
a,b,c
, Annick De Paepe
a
, Olivia J. Kirtley
d
, Geert Crombez
a
Abstract
Screening tools allowing to predict poor pain outcomes are widely used. Often these screening tools contain psychosocial risk
factors. This review (1) identifies multidimensional screening tools that include psychosocial risk factors for the development or
maintenance of pain, pain-related distress, and pain-related disability across pain problems in adults, (2) evaluates the quality ofthe
validation studies using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and (3) synthesizes methodological concerns.
We identified 32 articles, across 42 study samples, validating 7 screening tools. All tools were developed in the context of
musculoskeletal pain, most often back pain, and aimed to predict the maintenance of pain or pain-related disability, not pain-related
distress. Although more recent studies design, conduct, analyze, and report according to best practices in prognosis research, risk
of bias was most often moderate. Common methodological concerns were identified, related to participant selection (eg, mixed
populations), predictors (eg, predictors were administered differently to predictors in the development study), outcomes (eg, overlap
between predictors and outcomes), sample size and participant flow (eg, unknown or inappropriate handling of missing data), and
analysis (eg, wide variety of performance measures). Recommendations for future research are provided.
Keywords: Multidimensional screening, Yellow flags, Pain, Risk of bias
1. Introduction
Chronic pain is a common experience, with a prevalence of between
10% and 20% in the general adult population.
6,7,34,95,114
Often,
chronic pain is disabling and notoriously difficult to treat.
87
At least 2
strategies are possible to face these challenges. First, we can
develop new and better medical and psychosocial interventions.
19
Second, we can prevent acute pain from becoming chronic. The
latter requires an understanding of how and why acute pain
becomes chronic, the identification of individuals at risk, and the
timely delivery of preventive actions.
67,126
Evidence has been accumulating that psychosocial variables
are important in the prediction and prevention of chronic pain.
First, available experimental and prospective research reveals the
role of psychosocial factors in explaining pain, distress, and
disability.
57
The roles of learning, emotions, and cognitive factors
are well established in laboratory studies,
123
and a number of
prospective studies have provided evidence for the role of
psychosocial factors in the development and maintenance of
pain.
3,60,102
For example, Sobol-Kwapinska et al.
106
reviewed
predictors of acute postsurgical pain and found pain catastroph-
izing, optimism, expectation of pain, neuroticism, anxiety (state
and trait), negative affect, and depression to be associated with
acute postsurgical pain. Second, contemporary theoretical
models have provided insight into how acute pain patients with
a particular psychosocial profile may become stuck in a vicious
cascade of further pain, distress, and disability.
13,122
Third,
evidence is increasing that the timely delivery of cognitive-
behavioral interventions can prevent persistent disability.
67
Taking this evidence into account, Kendall et al.
51
called for the
routine assessment of psychosocial factors in people experienc-
ing acute pain. They introduced the concept of “yellow flags” as
a method to screen for psychosocial risk factors predicting long-
term disability, a concept that has been adopted by a growing
number of researchers interested in examining the value of
prognostic models.
26,27
This has led to the development of
screening tools that include various psychosocial risk factors and
Sponsorships or competing interests that may be relevant to content are disclosed
at the end of this article.
a
Department of Experimental Clinical and Health Psychology, Faculty of Psychol-
ogy and Educational Sciences, Ghent University, Ghent, Belgium,
b
Institute for
Health and Behaviour, INSIDE, Faculty of Language and Literature, Humanities, Arts
and Education, University of Luxembourg, Esch-sur-Alzette, Luxembourg,
c
Section
Experimental Health Psychology, Clinical Psychological Science, Departments,
Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the
Netherlands,
d
Center for Contextual Psychiatry, Department of Neurosciences, KU
Leuven, Leuven, Belgium
*Corresponding author. Address: Department of Experimental Clinical and Health
Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Henri
Dunantlaan 2, B-9000 Gent, Belgium. Tel.: 132 92646392; fax: 132 9 264 64 89.
E-mail address: Elke.Veirman@UGent.be (E. Veirman).
Copyright ©2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf
of The International Association for the Study of Pain. This is an open access article
distributed under the Creative Commons Attribution License 4.0 (CCBY), which
permits unrestricted use, distribution, and reproduction in any medium, providedthe
original work is properly cited.
PR9 4 (2019) e775
http://dx.doi.org/10.1097/PR9.0000000000000775
4 (2019) e775 www.painreportsonline.com 1
a recommendation for their use in clinical practice (eg, Keele
STarT Back Screening Tool [STarT Back]
41
; Preventing the
Inception of Chronic Pain [PICKUP]
115
).
Several reviews have summarized the predictive performance
of screening tools.
30,42,49,68,99
For instance, in a meta-analysis of
screening tools, Karan et al.
49
showed that screening tools poorly
predicted pain, but were acceptable and excellent in predicting
disability, and absenteeism, respectively (eg, STarT Back,
OMPSQ). This meta-analysis is of high quality and according to
the highest standards in the field.
71,109
For that reason, our aim
was not to focus upon the actual performance of the screening
tools. Available meta-analyses
49,99
have also noted that the
methodological quality of studies investigating the predictive
performance of screening tools is variable. Nevertheless, these
reviews do not provide details of the methodological problems
and limitations.
For that reason, our review focuses upon the methodological
quality of studies that validate screening tools. First, a detailed
analysis and synthesis of the methodological quality of studies is
largely missing. Indeed, despite being considered fundamental to
guide interpretation of findings, and recommendations for future
research and practice,
52
available reviews spend little or no
attention to this topic. Second, the methodological quality of the
studies in these reviews, typically described as “risk of bias,”
40
was assessed using instruments that were not specifically
designed for evaluating the quality of prediction models (eg,
Quality in Prognostic Studies tool [QUIPS]).
36,37
Recently, “Pre-
diction model Risk Of Bias ASsessment Tool” (PROBAST), a tool
for assessing the risk of bias and applicability of diagnostic and
prognostic prediction model studies, has become available and
used.
76,127–129
The aim of this systematic review was 3-fold: (a) to identify
available multidimensional screening tools that include psycho-
social risk factors for poor pain outcomes (development or
maintenance of pain, pain-related distress, and pain-related
disability) across pain problems in adults, (b) to evaluate the
quality of prospective studies validating these screening tools
with up-to-date standards for clinical prediction models, and (c)
to synthesize methodological concerns that may bias the
predictive performance of these screening tools.
2. Methods
2.1. Literature search and eligibility criteria
The literature search comprised 4 steps. First, a search was
performed for studies published in peer-reviewed journals
across relevant electronic databases (MEDLINE, PsychINFO,
and Web of Science) using the following terms in the title, key
words, or abstract: screen*AND(tool OR questionnaire)AND
pain AND risk. Screening of titles, key words, and abstracts
allowed identification of screening tools and eligible studies.
Second, a list of publications was sent to lead authors in the
field of pain research to ask for any other available screening
tools of which they were aware. Third, the reference lists of
relevant systematic reviews were hand-searched for any
articles that were not yielded by our other search methods.
Finally, when only the development article for a tool fulfilling the
inclusion criteria (see below) was identified in the search,
a search was performed for additional articles that fulfilled the
inclusion criteria by screening all publications that cited this
development article.
The following eligibility criteria were used to identify screening
tools for inclusion in this systematic review:
(1) The screening tool is a self-report questionnaire.
(2) The screening tool is multidimensional, containing at least 2
psychosocial risk factors. The report of somatic experiences
such as pain, radiation, or other somatic complaints is not
considered as psychosocial factors.
(3) The screening tool aims to predict the development (,3
months) or maintenance ($3 months) of pain, pain-related
distress, or pain-related disability.
(4) The screening tool is specifically developed in the context of
pain and can target any type of pain (eg, neck pain and low
back pain).
(5) The screening tool is a standalone instrument. Therefore, the
tool should not consist of a battery of questionnaires, as is
often the case for research purposes.
(6) The screening tool is validated in at least 1 independent study,
ie, using data that were not used to develop the screening tool.
Six criteria (listed below) were used to select studies for
inclusion. Some criteria were included to set a minimum quality
(eg, criterion 1), whereas other criteria were applied to narrow the
scope of the review (eg, criterion 2).
(1) The study is a full report published in a peer-reviewed scientific
journal.
(2) The study includes an adult sample (the average age of the
sample was older than 18 years).
(3) At baseline, the study includes patients experiencing no or
(sub)acute pain (,3 months), without restriction in the type of
pain experienced (eg, musculoskeletal pain, neuropathic pain,
and postoperative pain). In line with the development studies
of screening tools, we excluded studies involving only patients
with chronic pain ($3 months). Studies involving mixed
samples with (sub)acute and chronic pain patients were
included. However, when data for separate subsamples were
reported, we only included the samples of interest.
(4) The study includes at least 1 screening tool, which is used in its
original form. Some differences in translations, item order, and
response scale are accepted. Shortened versions are
considered different instruments.
(5) The study includes at least one of the following outcomes
during outcome assessment (,2 years after baseline
assessment): (a) Pain intensity or pain bothersomeness,
assessed using a Visual Analogue Scale (VAS), a Numeric
Rating Scale (NRS), a verbal rating scale, or a Likert scale; (b)
pain-related disability including activity limitations (ie, diffi-
culties in executing a task or an action such as the ability to
walk, eat, shower, or dress) and participation restrictions (ie,
problems relating to the involvement in life situations such as
sick leave or days absent from work or return to work status)
according to the International Classification of Functioning,
Disability, and Health (ICF) framework.
130
Assessment of
these outcomes could be performed with (a subset of
questions from) a self-report questionnaire, single questions,
or data from existing registration systems; and (c) pain-
related distress (eg, anxiety, fear, or low mood), assessed
through self-report measures.
(6) The study is a prospective cohort study including patients
presenting in primary, secondary, and tertiary health care
settings.
Finally, studies were considered ineligible if they aimed to
investigate the impact of stratified care (ie, targeted treatment to
patient subgroups based on the results of the screening tool) or
interventions that specifically targeted psychosocial risk factors
(ie, cognitive behavioral therapy) or they consisted of a random-
ized control trial. We reasoned that the focus of these studies is
on the evaluation of a (psychological) therapeutic intervention
2E. Veirman et al.·4 (2019) e775 PAIN Reports
®
and not on the investigation of the predictive value of screening
tools.
2.2. Data extraction and risk of bias assessment
The assessment of the quality of studies that validated the
selected screening tools was based upon a prepublication
version of the Prediction model study Risk Of Bias ASsessment
Tool (PROBAST) (personal communication, January 2017, Dr.
Robert Wolff). The PROBAST has been developed by the
Cochrane Prognosis Methods Group using a Delphi process, in
which 40 experts in the fields of prediction research and
systematic review methodology participated.
129
Its use is
recommended by most recent guidelines for performing system-
atic reviews and meta-analyses of prediction model
performance.
16
Data extraction of eligible validation studies was conducted by
E.V. and O.K. following a customized PROBAST template that
was created for each of the 5 risk of bias assessment areas: (1)
participant selection, (2) predictors, (3) outcomes, (4) sample size
and participant flow, and (5) analysis (details can be retrieved from
the authors upon request).
74
Extracted data formed the basis for
the risk of bias assessment, where signaling questions across
those 5 important areas were rated as yes,probably yes,
probably no,no,orno information, with yes indicating the
absence of bias and probably no or no indicating the potential for
bias.
For participant selection, elements judged were whether
appropriate inclusion and exclusion criteria were used and
whether patients had a similar state of health at enrollment. For
predictors, questions considered were whether definition and
assessment of predictors were similar across participants, and
whether definition and assessment of predictors were similar
compared with those of the development model. For outcomes,
important elements judged were whether a valid outcome was
used, whether predictors were excluded from the outcome
definition, whether definition and assessment of outcomes were
similar across participants, whether definition and assessment
of outcomes were similar compared with those of the de-
velopment model, and whether outcome assessment was
blinded to predictor data. For sample size and participants
flow,elementsjudgedwerewhetherareasonablenumberof
outcome events were available, whether the time interval
between predictor and outcome assessment was appropriate,
whether all enrolled participants were included in the analyses,
and whether missing data occurred and participants with
missing data were handled appropriately. Finally, for analysis,
evaluated elements focused on whether relevant model
performance measures were evaluated. Domains were sub-
sequently rated as high,moderate,low,orunclear risk of bias.
Risk of bias assessment labels were discussed and assigned
upon agreement among team members (G.C., D.V.R.,
and E.V.).
3. Results
3.1. Study selection
The study selection process was guided by the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses
guidelines (PRISMA),
71
except for a preregistration of the review.
Electronic databases were searched from the earliest record
available on September 15, 2016, resulting in 1850 records. After
removal of duplicate articles, 2 reviewers (J.C. and E.V.)
independently screened a selection of the titles, key words, and
abstracts for possible study inclusion. First screening resulted in
187 remaining references.
In the second step, full copies of articles were obtained (E.V.).
Full-text reading of these articles resulted in exclusion of several
tools for the following reasons (1) not being a screening tool (eg,
“Amsterdam Preoperative Anxiety and Information Scale”),
70
(2)
the screening tool was not developed in the context of pain (eg,
“Distress and Risk Assessment Method”),
65
(3) the screening tool
did not assess any psychosocial factors (eg, “London Fibromyal-
gia Epidemiology Study Screening Questionnaire”),
54
and (4) the
screening tool assessed only 1 psychosocial factor (eg, “Fear
Avoidance Beliefs Questionnaire”).
93
For 3 potentially eligible screening tools, items were not
available in the literature and author contact yielded insufficient
access to the tools’ items (“Nijmegen Outcome of Lumbar Disc
surgery Screening-instrument”
17
; “ABLE Presurgical Assess-
ment Tool”
2
; and “Psychosocial Risk for Occupational Disability
Scale”
100
).
Finally, a number of eligible screening tools for which items
were available in the literature were not included in the current
review as no independent validation studies were retrieved from
the electronic database search nor through cited reference
search of the development articles of the screening tools (ie,
“Absenteeism Screening Questionnaire”
116
; “Back Disability Risk
Questionnaire”
103,104
; “Optimal Screening for Prediction of Re-
ferral and Outcome cohort yellow flag assessment tool”
58
; “Pain
Recovery Inventory of Concerns and Expectations”
105
;
“Screening-Instrument zur Feststellung des Bedarfs an
medizinisch-beruflich orientierter Rehabilitation”
112
; “Traumatic
Injuries Distress Scale”
125
; and “Work and Health
Questionnaire”
1
).
In addition to the 27 articles that were considered eligible from
the electronic database search, 2 articles
25,50
were identified
through cited reference search of the development articles of the
screening tools on May 4, 2017, and 3 references
32,53,64
were
retrieved by hand-searching of relevant review
articles
8,30,42,45,49,59,68,86,88,90,99,107
(O.K.), resulting in a total of
32 references fulfilling the inclusion criteria for the current review.
Additional author contact yielded no other tools or studies (see
Figure 1 for a flowchart).
Doubts and disagreements on the inclusion of screening tools
and eligible studies were resolved by discussion within the team
(G.C., D.V.R., E.V., A.D.P., and O.K.) until consensus was
reached. After finalizing the systematic search, all screening tools
and development studies were retrieved to extract essential data
for the risk of bias assessment. During the screening process,
reviewers were not blind to authorship, institution, journal, or
results.
3.2. Study characteristics: screening tools
The 32 included articles contained 42 study samples. Notably,
several articles reported on a similar sample as earlier published
articles, whereas other study samples completed multiple
screening tools. The articles reported on the validation of 7
screening tools:
(1) Acute Low Back Pain Screening Questionnaire (ALBPSQ; 7
studies)
62
/¨
Orebro Musculoskeletal Pain Screening Question-
naire (OMPSQ; 10 studies)
61
/¨
Orebro Musculoskeletal Screen-
ing Questionnaire (OMSQ; 3 studies).
25
The ALBSQ is a 24-
item self-report questionnaire aiming to predict poor progno-
sis—operationalized as accumulated sick leave—in acute and
subacute patients presenting with musculoskeletal pain
4 (2019) e775 www.painreportsonline.com 3
(back, neck, and shoulder pain). A few years after its
development, it was relabeled as the OMPSQ, including an
additional unscored item on employment status. More
recently, the OMSQ broadened the focus of the ALBPSQ to
general musculoskeletal problems and simplified the
questions.
(2) ¨
Orebro Musculoskeletal Pain Screening Questionnaire
short version (OMPSQs; 2 studies).
63
The OMPSQs is
a 10-item self-report questionnaire designed to predict
disability—operationalized as sick leave—in workers suf-
fering from musculoskeletal pain (back pain).
(3) ¨
Orebro Musculoskeletal Screening Questionnaire short version
(OMSQs; 1 study).
23
TheOMSQsisa12-itemself-report
questionnaire aiming to predict a wide variety of outcomes—including
problem severity, functional impairment, absenteeism, long-term
absenteeism, cost, and recovery time—in acute and subacute work-
injured patients presenting with musculoskeletal pain (whiplash, low
back pain).
(4) Heidelberger Kurzfragebogen R ¨uckenschmerz (HKF-R10; 1
study).
79
The HKF-R10 is a 27-item self-report questionnaire
developed to predict the likelihood of chronicity in patients with
acute low back pain.
(5) Pain Belief Screening Instrument (PBSI; 1 study).
97
The PBSI is
a 7-item self-report questionnaire aiming to predict disability in
subacute and chronic pain patients with musculoskeletal pain
(neck, shoulder, and low back pain).
(6) Keele STarT Back Screening Tool (SBT; 11 studies).
41
The
SBT is a 9-item self-report questionnaire developed to predict
poor outcome—operationalized as disability—in (sub)acute
and chronic primary care patients with nonspecific low back
pain.
(7) Preventing the Inception of Chronic Pain (PICKUP; 2
studies).
115
The PICKUP is a 5-item self-report questionnaire
aiming to predict the risk of chronic low back pain in patients
with acute low back pain.
An overview of the included instruments and more detailed
characteristics (as described in the base article) can be found in
Table 1.
3.3. Study characteristics: sources and samples
Studies were conducted between 2000 and 2017.
43,50
The majority
of the studies included samples that were collected in Northern
European countries (N 511) or Western European countries (N 5
11). A small number of studies collected data from samples outside
Europe, including Canada (N 51), the United States (N 53),
Australia and New Zealand (N 57), and China (N 51).
Sex and age of participants differed largely between study
samples. The average/median age of participants ranged
between 37.7 years and 53.0 years.
21,64
The sex of participants
varied from 33.7% female participants to 83.0% female
participants.
18,63
Figure 1. Flow of studies through the review.
4E. Veirman et al.·4 (2019) e775 PAIN Reports
®
Study samples were collected in primary care (83.3%) and
secondary care settings (11.9%), and 1 study included a com-
bined sample of participants from primary and secondary care
units (4.8%).
92
The terminology used to describe the settings
varied, by reference to providers (eg, general practitioner or
a physical therapist)
61
and/or type of services (eg, spinal
Table 1
Summary of included screening tools.
Screening tool Development study Summary of instrument Scoring method Cutoff scores/subgrouping,
follow-up
Acute Low Back Pain Screening
Questionnaire (ALBPSQ), later
renamed as ¨
Orebro
Musculoskeletal Pain Screening
Questionnaire (OMPSQ), and
reframed as ¨
Orebro
Musculoskeletal Screening
Questionnaire (OMSQ)
Linton and Hallden,
62
Sweden
24 items
Risk assessment for poor
prognosis—operationalized as
accumulated sick leave.
In acute and subacute patients
presenting with musculoskeletal pain
(lower back, neck, and shoulder).
In primary care setting.
21 items are scored, covering pain
experience (5 items), physical
functioning (5 items), coping (1 item),
job satisfaction (1 item), anxiety/
stress (1 item), depression (1 item),
fear-avoidance beliefs (3 items),
recovery expectations (2 items),
heavy or monotonous work (1 item),
and sick leave (1 item).
Miscellaneous items relate to age,
sex, and nationality.
Cutoff score of 105.
6-month follow-up.
¨
Orebro Musculoskeletal Pain
Screening Questionnaire short
(OMPSQs)
Linton et al.,
63
Sweden
10 items
Risk assessment for poor
prognosis—operationalized as sick
leave.
In workers suffering from
musculoskeletal pain (back).
In occupational health care setting.
10 items are scored, covering pain
experience (2 items), self-perceived
function (2 items), distress (2 items),
return to work expectancies
(2 items), and fear avoidance beliefs
(2 items).
Cutoff score of 50.
1-year follow-up.
¨
Orebro Musculoskeletal
Screening Questionnaire short
(OMPQs)
Gabel et al.,
23
Australia
12 items
Risk assessment for poor
prognosis—operationalized as
problem severity, functional status,
absenteeism, long-term
absenteeism, recovery time, and
cost.
In acute and subacute workers
presenting with musculoskeletal pain
(whiplash and low back pain).
In primary care setting.
12 items are scored, covering pain/
problem experience (3 items),
physical function (2 items), life
satisfaction (1 item), depression
(1 item), anxiety (1 item), fear-
avoidance beliefs (2 items), recovery
expectations (1 item), and other
(1 item).
No optimal cutoff recommended.
6-month follow-up.
Heidelberger Short Early Risk
Assessment Questionnaire (HKF-
R 10)
Neubauer et al.,
79
Germany
27 items
Risk assessment for chronic low back
pain
In patients with acute low back pain.
In primary care setting.
26 items are scored, covering
sociodemographic information (2
items), pain intensity and duration (4
items), efficacy of massage (1 item),
depression (5 items), catastrophizing
(5 items), and helplessness and
hopelessness (9 items).
An additional item regarding pain
intensity in the past week is present
in the measure, but is not included
within the total score.
No optimal cutoff recommended.
6-month follow-up.
Pain Belief Screening Instrument
(PBSI)
Sandborgh et al.,
97
Sweden
7 items.
Risk assessment for disability.
In subacute and chronic pain patients
with musculoskeletal pain (neck,
shoulder, and low back)
In primary care setting.
7 items are scored, covering pain
intensity (1 item), functional ability (1
item), fear-avoidance (2 items),
catastrophizing (1 item), and self-
efficacy (2 items).
No optimal cutoff recommended.
No follow-up.
STarT Back Tool (SBT) Hill et al.,
41
United
Kingdom
9 items.
Risk assessment for pain-related
disability.
In (sub)acute and chronic patients
with nonspecific back pain.
In primary care setting.
9 items are scored, covering
bothersomeness of pain (1 item),
presence of referred pain (1 item),
comorbid pain (1 item), disability (2
items), catastrophizing (1 item), fear
(1 item), anxiety (1 item), and
depression (1 item).
Stratification of patients in low
(overall score 0–3), medium (overall
score .3; psychosocial subscale
score ,4), or high risk (psychosocial
subscale scores $4) categories of
poor clinical outcome, assisting in
decision-making about the specific
course treatment.
6-month follow-up.
Predicting the Inception of
Chronic Pain (PICKUP)
Traeger et al.,
115
Australia
5 items.
Risk assessment for chronic low back
pain.
In patients with acute low back pain.
In primary care setting.
5 items are scored online through
http://pickuptool.neura.edu.au/,
covering pain intensity (1 item), leg
pain (1 item), disability compensation
(1 item), depression (1 item), and
perceived risk (1 item).
Predicted probability risk score in
percentage.
3-month follow-up.
4 (2019) e775 www.painreportsonline.com 5
outpatient clinic).
50
Although some studies detailed the treatment
patients received (eg, work conditioning program),
66
others often did
not (eg, treated as usual).
81
If information about the use of treatments
is reported with insufficient detail, it can potentially bias performance
results of the included screening tools because it does not allow
researchers to evaluate the impact it might have had on the
results.
82,83
Moreover, within the studies that reported on the use of
treatments, none of the studies accounted for treatment use.
Most study samples comprised participants with musculoskeletal
pain. In particular, patients with back pain were overrepresented.
Study samples often also included participants from other populations,
such as those experiencing neck pain, pain between the shoulder
blades,
124
or multisite pain
24,25
(see Table 2 for an overview).
3.4. Risk of bias assessment of included studies
3.4.1. Participant selection
The majority of the study samples consisted of mixed samples
containing both acute and chronic pain patients (59.5%). The
remaining samples comprised patients with acute pain (33.3%) or
samples for which the type of pain (acute, subacute, or chronic
pain) was not clearly described (7.1%) (Table 2).
For the PROBAST “participant selection” domain, the majority of the
study samples were rated as having a moderate risk of bias (51.1%).
Fewer study samples were rated as having low (16.7%) or high risk of
bias (19.0%). For the remaining study samples, the risk of bias was
rated as unclear (Table 3) because the presented information was
insufficient to evaluate the appropriateness of the inclusion criteria or
the state of health of participants. The reasons for increasing the risk of
bias related to the specified inclusion and exclusion criteria and
differences in the state of health of participants at enrollment.
3.4.1.1. Inclusion and exclusion criteria
The eligibility criteria were sometimes inappropriate or unclear.
For example, some studies did not exclude unemployed
participants
14
or did not report information on employment,
38
although the screening tools contained work-related questions.
Most studies reported inclusion and exclusion criteria. However,
sometimes the criteria had to be retrieved from descriptive
information
66
or from a previously published study.
64
3.4.1.2. Participants’ state of health at enrollment
Although most studies aimed to recruit a homogeneous sample,
other studies did not. Participants were found not to be in a similar
state of health at baseline in cases when studies included patients
with (sub)acute and chronic pain in a single
sample.
5,14,20–22,25,28,41,43,44,50,53,61,63,64,77,78,80,92,98
For in-
stance, despite George and Beneciuk
28
reported detailed
information about their patients with (sub)acute and chronic
pain, the analyses were based upon the full sample. For
a considerable number of studies, the state of health of the
participants had to be derived from descriptive information. For
example, Margison and French
66
only reported on average pain
duration in weeks. Sometimes, insufficient information was
available to conclude whether participants were in a similar state
of health at enrolment
63
(Table 2).
3.4.2. Predictors
The most frequently used screening tool was the ALBPSQ/
OMPSQ/OMSQ. However, we noted that the cutoff score to
identify the high risk group varied substantially, ranging between
72 and 147 (Table 4).
For the PROBAST “predictors” domain, the majority of the
study samples were rated as having a low risk of bias (40.5%).
Only a small number of study samples were rated as having
moderate (19.0%) or high risk of bias (11.9%). For 28.6% of the
study samples, the risk of bias was rated as unclear (Table 3)
because the presented information was insufficient to evaluate
whether differences occurred in the assessment of the
screening tools either across participants or compared with
the development study. The reasons for increasing the risk of
bias related to differences in the assessment of the screening
tools across participants and differences in the assessment of
screening tools compared with the development study.
3.4.2.1. Definition and assessment of predictors across
participants
Study samples that validated the ALBPSQ, the OMPSQ, and the
PICKUP—tools that include work-related questions—sometimes
did not report information on employment status. This could
mean that participants were all employed, or that some of the
participants were unemployed, but it was not reported.
50
Furthermore, those studies that did report on employment status
did not always administer these tools in a similar way across
participants. For example, Hurley et al. instructed participants to
fill out ALBPSQ work-related questions as best they could, even
when they were unemployed.
43,44
When these questions were
left blank, the mean score of the other questions was used as
replacement. In the study by Grotle et al.,
33
it is noted that for
participants who were unemployed, OMPSQ work-related
questions were replaced by the mean score of the other
questions.
3.4.2.2. Definition and assessment of predictors compared
with the developmental model
Furthermore, across the included studies, significant variation
was observed in the applied screening tool cutoff points used to
categorize patients. Selective reporting of results based only on
cutoff values other than those specified in the original de-
velopment study for the screening tool, was considered a risk for
underestimation or overestimation of the screening tool’s pre-
dictive accuracy. Moreover, variable use of cutoffs prohibits to
estimate the influence of a given setting on the performance at the
recommended (original) threshold. For example, for the ALBPSQ,
the standard cutoff originates from Linton and Halld ´en,
62
who
used 105 as their cutoff score for detecting poor prognosis in the
form of sick leave. Hurley et al.
44
and Vos et al.
124
only reported
results using a cutoff of 112 and 72, respectively, for the outcome
sick leave. In addition, few studies also treated screening tool
scores as continuous without additional reporting of the cutoff
values from the screening tool’s development study
38
(Table 4).
3.4.3. Outcomes
The majority of study samples assessed one or more outcomes
related to pain (66.7%), activity limitations (54.8%), and participation
restrictions (50.0%). In addition, about half of the study samples
reported also mixed or composite outcomes (40.5%) (Table 4).
For the PROBAST “outcomes” domain, the majority of the
study samples were assigned an unclear risk of bias (40.5%),
mainly due to insufficient information to evaluate blinding, or
a moderate risk of bias (42.9%). None of the study samples were
6E. Veirman et al.·4 (2019) e775 PAIN Reports
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Table 2
Key study and participant characteristics of included validation studies.
Study Country; setting Age in years [SD; (IQ-)range] % female Pain type Pain duration Pain intensity
ALBPSQ
Hurley et al.
43
United Kingdom;
Primary care
Physiotherapy departments and
health centers
M543.19 [range: 17–77] 60 Low back pain ,12 weeks: N 564
.12 weeks: N 550
MPQ
ALBPSQ #112
:
Med
514.5 [IQ range 512.2;
range 51–54]
MPQ
ALBPSQ .112
:
Med
527.5 [IQ range 524.5;
range 50–70]
Hurley et al.
44
United Kingdom;
Primary care
Physiotherapy departments
Med
541.5 [range: 17–77] 60 Low back pain ,12 weeks: 56% MPQ:
Med
519.0 [IQ range: 20.0,
range: 0–70]
Grotle et al.
32
Norway;
Primary care
General practitioners, chiropractors,
and physical therapists (27%
recruited through advertisement)
M538.9 [SD 510.3] 57 Low back pain with or without
radiation
#3 days: N 541, 34%
4–12 days: N 543, 36%
13–20 days: N 536, 30%
NA
Grotle et al.
33
Norway;
Primary care
General practitioners, chiropractors,
and physical therapists (27%
recruited through advertisement)
M538.0 [SD 510.1] 54 Low back pain with or without
radiation
M52.3 weeks [SD 52.2] ALBPSQ current pain:
M56.7 (SD 51.8)
ALBPSQ average pain:
M53.0 (SD 52.5)
Grotle et al
31
Norway;
Primary care
General practitioners, chiropractors,
and physical therapists (27%
recruited through advertisement)
M537.9 [SD 510.1] 55 Low back pain with or without
radiation
M58.1 days [SD 56.6] NRS pain intensity last week:
M56.7 [SD 51.8]
Heneweer et al.
38
The Netherlands;
Primary care
Physical therapists
Recovered*: M 540.8 [SD 59.2]
Not recovered*: M 543.1
[SD 59.1]
39 Nonspecific low back pain Recovered*:
,4 weeks: N 520, 64.5%
4–6 weeks: N 59, 29.0%
7–12 weeks: N 52, 6.5%
Not recovered*:
,4 weeks: N 59, 36.0%
4–6 weeks: N 56, 24.0%
7–12 weeks: N 510, 40.0%
NA
Vos et al.
124
The Netherlands;
Primary care
General practitioners
Male/female: M 543.2/38.2 64 Neck pain M 52.76 weeks [SD 53.00] ALBPSQ current pain:
M56.5 [SD 51.75]
ALBPSQ average pain:
M53.78 [SD 52.76]
OMPSQ
Linton and Boersma
61
Sweden;
Primary care
General practitioners and physical
therapists
M541.1 [range: 22–66] 48 Neck and back pain .24 weeks: 43% OMPSQ current pain:
M56.2 [SD 52.1]
OMPSQ average pain:
M55.1 [SD 52.2]
Dunstan et al.
18
Australia;
Primary care
Occupational injury compensation
database
[range: 18–65] 34 Musculoskeletal pain NA NA
Margison and
French
66
—Derivation
sample
Canada;
Primary care
Private-sector clinics and
physiotherapy clinics
M541.2 [SD 510.8] 41 Neck, shoulder, upper back, lower
back, arm, wrist, and hand, leg,
ankle, and foot, and other pain
M56.7 weeks [SD 51.7] Pain intensity past 3 months:
M56.8 [SD 52.0]
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4 (2019) e775 www.painreportsonline.com 7
Table 2 (continued)
Key study and participant characteristics of included validation studies.
Study Country; setting Age in years [SD; (IQ-)range] % female Pain type Pain duration Pain intensity
Margison and
French
66
—Validation
sample
Canada;
Primary care
Private-sector clinics and
physiotherapy clinics
M541.5 [SD 59.8] 39 Neck, shoulder, upper back, lower
back, arm, wrist, and hand, leg,
ankle, and foot, and other pain
M56.6 weeks [SD 51.5] Pain intensity past 3 months:
M57.1 [SD 52.1]
Maher and
Grotle
64
—Australasian
sample
Australia and New Zealand;
Primary care
Physiotherapy clinics
M543.3 [SD 512.1] 43 Nonspecific low back pain 6–8 weeks: N 545
9–11 weeks: N 538
12 weeks: N 517
OMPSQ current pain:
M55.2 [SD 51.9]
Maher and
Grotle
64
—Norwegian
sample
Norway;
Primary care
Doctors and chiropractors
M538.7 [SD 59.7] 56 Low back pain ,1 week: N 550
1–2 weeks: N 521
2–3 weeks: N 529
OMPSQ current pain:
M56.8 [SD 51.8]
Gabel et al.
25
—OMPSQ Australia;
Primary care
Physiotherapy outpatient clinics
M539 [SD 57; range: 18–58] 42 Lower back, lower back and leg,
lower back and neck, back, neck, and
shoulder pain
M54.0 weeks [SD 58.2]
6% chronic
OMPSQ current pain:
M56.5 [SD 51.8]
OMPSQ average pain:
M56.2 [SD 53.0]
Gabel et al.
25
—OMSQ Australia;
Primary care
Physiotherapy outpatient clinics
M539 [SD 59; range: 18–58] 43 Neck/back, arm, leg, both sides, and
several areas
M54.1 weeks [SD 58.1]
8% chronic
OMSQ intensity acute:
M56.6 [SD 51.9]
OMSQ severity chronic:
M55.8 [SD 52.7]
Linton et al.
63
Sweden;
Primary care
M548 83 Nonspecific back or neck pain NA NA
Gabel et al.
24
Australia;
Primary care
Physiotherapy centers
M538.9 [SD 510.5; range:
18–65]
43 Musculoskeletal pain resulting from
work injury (back, neck, upper limbs,
lower limbs, and multisite pain)
Item 3 OMSQ: M 54.1 [SD: 2.9] OMSQ intensity acute:
M56.3 [SD 52.0]
OMSQ severity chronic:
M56.0 [SD 52.9]
Nonclercq and Berquin
81
Belgium;
Secondary care
Emergency facility and outpatient
clinic
M542.2 [SD 510.7] 56 Back pain (lumbar pain, cervical pain,
and multisite pain)
,3 weeks: 58% NA
Dagfinrud et al.
14
Norway;
Primary care
Manual therapists
M544.3 [SD 514.4; range:
18–81]
59 Neck pain and low back pain 0–2 weeks: 23.4%
2–12 weeks: 24.1%
3–12 months: 13.9%
.1 year: 38.6%.
OMPSQ current pain:
M56.36 [SD 53.54]
Gabel et al.
23
Australia;
Primary care
Physiotherapy centers
M539.3 [SD 59.7] 43 General musculoskeletal pain (spine,
upper and lower limbs)
NA NA
Law et al.
55
China;
Primary care
Physiotherapy outpatient clinics
M544.2 [SD 511.2] 43 Nonspecific low back pain M 53.0 weeks [SD 51.8]
1–2 weeks: N 5114, 47.3%
3–5 weeks: N 5100, 41.5%
6–10 weeks: N 524, 9.9%
NPRS pain intensity:
M55.8 [SD 52.1]
Riewe et al.
92
Germany;
Primary, secondary care
Orthopaedic specialists,
rehabilitation facilities, and private
physiotherapy practices
M543 65 Nonspecific back pain .1 week: 94%
.24 weeks: 15%
OMPSQ current pain:
M55.5 [SD 52.1]
OMPSQ average pain:
M54.8 [SD 52.0]
OMPSQs
Linton et al.
63
Sweden;
Primary care
M548 83 Nonspecific back or neck pain NA NA
Karran et al.
50
Australia;
Secondary care
Spinal outpatient clinic
M549 [SD 516] 49 Low back pain, with or without leg
symptoms
,3 months: 20.9%
3–6 months: 33.6%
.6 months: 44.6%
NRS pain intensity previous week:
M57.1 [SD 52.2]
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8E. Veirman et al.·4 (2019) e775 PAIN Reports
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Table 2 (continued)
Key study and participant characteristics of included validation studies.
Study Country; setting Age in years [SD; (IQ-)range] % female Pain type Pain duration Pain intensity
OMSQs
Gabel et al.
23
Australia;
Primary care
Physiotherapy centers
M539.3 [SD 59.7] 43 General musculoskeletal pain (spine,
upper and lower limbs)
NA NA
STarT Back
Hill et al.
41
United Kingdom;
Primary care
General practice
M545 [SD 59.7] 59 Nonspecific back pain ,1 month: N 583, 17%
1–3 months: N 594, 19%
4–6 months: N 577, 15%
7 months–3 years: N 5125, 25%
.3 years: N 5112, 22%
NRS pain intensity mean (least,
average, current):
Mild (0–5): N 5325, 65%
Moderate (6–7): N 5113, 23%
Severe (8–10): N 554, 11%
Fritz et al.
22
United States;
Primary care
Outpatient physical therapy clinics
M544.3 [SD 515.8] 57 Low back pain
Med
546 days [IQ range:
18.5–147]
NRS initial pain intensity:
M55.3 [SD 52.3]
Field and Newell
20
United Kingdom;
Primary care
Chiropractic clinics
Low risk: M 545.4 [SD 515.1]
Medium risk: M 545.9 [SD 515.0]
High risk: M 545.8 [SD 514.1]
Low risk: 55
Medium
risk: 53
High risk:
51
Nonspecific low back pain ,1 month: 56.2%
1–3 months: 12.4%
.3 months: 31.4%
BQ pain:
Low risk:
Med
55 (range: 4–7)
Medium risk:
Med
57 (range: 6–8)
High risk:
Med
57 (range: 6–9)
Beneciuk et al.
5
United States;
Primary care
Outpatient physical therapy clinics
M541.1 [SD 513.5] 61 Low back pain
Med
590.0 days [IQ range:
30–365]
#14 days: 11.8%
15–90 days: 39.2%
$90 days: 49.0%
NRS pain intensity mean (current,
best, and worst):
M55.3 [SD 52.0]
Morsø et al.
77
—UK sample United Kingdom;
Primary care
General practices
Med
546.0 [IQ range 539–53] 59 Nonspecific low back pain ,4 weeks: N 5327, 38.2%
4–12 weeks: N 5221, 25.8%
.12 weeks: N 5285, 33.3%
NRS pain intensity:
Med
55 [IQ range: 3–7]
Mild (0–5): N 5527, 61.6%
Moderate (6–7): N 5196, 22.9%
Severe (8–10): N 5127, 14.8%
Morsø et al.
77
—Danish
sample
Denmark;
Primary care
General practices and physiotherapy
clinics
Med
550.0 [IQ range 541–59] 58 Nonspecific low back pain ,4 weeks: N 5149, 44.2%
4–12 weeks: N 566, 19.6%
.12 weeks: N 5122, 36.2%
NRS pain intensity:
Med
57 [IQ range: 5–8]
Mild (0–5): N 5130, 38.7%
Moderate (6–7): N 598, 29.2%
Severe (8–10): N 5108, 32.1%
Morsø et al.
78
—Primary care
sample
Denmark;
Primary care
General practices and physiotherapy
clinics
M552.0 [SD 515.2] 57 Low back pain ,1 month: N 565, 38.9%
1–3 months: N 539, 23.4%
.3 months: N 563, 37.7%
NRS low back pain intensity:
Med
56 (IQ range: 4–7)
NRS leg pain intensity:
Med
53 (IQ range: 0–6)
Morsø et al.
78
—Secondary
care sample
Denmark;
Secondary care
Spine center
M552.0 [SD 514.1] 54 Low back pain ,1 month: N 547, 5.0%
1–3 months: N 5139, 14.9%
.3 months: N 5746, 80.0%
NRS low back pain intensity:
Med
55 (IQ range: 4–7)
NRS leg pain intensity:
Med
55 (IQ range: 2–7)
Foster et al.
21
United Kingdom;
Primary care
Family practices
M553.0 [SD 515.0] 55 Nonspecific low back pain ,1 month: N 575, 20%
1–3 months: N 562, 17%
3–6 months: N 575, 20%
6 months–3 years: N 582, 22%
.3 years: N 574, 20%
NRS pain intensity:
M55.3 [SD: 2.4]
George and Beneciuk
28
United States;
Primary care
Outpatient physical therapy clinics
Med 545, M 543.5 [SD 512.4] 65 Low back pain $90 days: N 553, 47.7% NRS pain intensity mean (current,
best, and worst):
Med
55.3, M 55.4 [SD: 1.9]
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4 (2019) e775 www.painreportsonline.com 9
Table 2 (continued)
Key study and participant characteristics of included validation studies.
Study Country; setting Age in years [SD; (IQ-)range] % female Pain type Pain duration Pain intensity
Newell et al.
80
United Kingdom;
Primary care
Chiropractic clinics
M547.8 [SD 513.9] 57 Nonspecific low back pain ,1 month: 43.2%
1–3 months: 10.0%
.3 months: 46.6%
BQ pain:
M56.4 [SD: 2.0]
Kongsted et al.
53
Denmark;
Primary care
Chiropractic clinics
M543 44 Nonspecific low back pain or lumbar
nerve root involvement
0–2 week: 62%
2–4 weeks: 13%
1–3 months: 11%
.3 months: 14%
NRS low back pain intensity:
M56.5
NRS leg pain intensity:
M52.4
Karran et al.
50
Australia;
Secondary care
Spinal outpatient clinic
M549 [SD 516] 49 Low back pain, with or without leg
symptoms
,3 months: 20.9%
3–6 months: 33.6%
.6 months: 44.6%
NRS pain intensity previous week:
M57.1 [SD 52.2]
HKF-R10
Riewe et al.
92
Germany;
Primary, secondary care
Orthopaedic specialists,
rehabilitation facilities, and private
physiotherapy practices
NA 67 Nonspecific back pain .8 days: 88% HKF-R10 pain past week:
M553.14 [SD 522.13]
HKF-R10 pain past week in best
stage:
M527.85 [SD 521.14]
PBSI
Sandborgh et al.
98
Sweden;
Primary care
Physical therapy departments and
occupational health care organization
M546 [SD 511; range: 19–64] 68 Musculoskeletal pain
Med
512 months (IQ range: 3–59,
range 1–300).
Subacute: N 522, 22%
Chronic: N 5131, 78%
NA
PICKUP
Traeger et al.
115
Australia;
Primary care
General practitioners, pharmacists,
and physiotherapists
M545 [SD 515.8] 46 Low back pain with or without leg
pain
,2 weeks: N 51183, 78%
2–3 weeks: N 5149, 10%
3–4 weeks: N 577, 5%
4–6 weeks: N 5116, 8%
Likert Pain intensity:
None: N 50, 0%
Very mild: N 5290, 19%
Mild: N 5242, 16%
Moderate: N 5565, 37%
Severe: N 5346, 23%
Very severe: N 570, 5%
Karran et al.
50
Australia;
Secondary care
Spinal outpatient clinic
M549 [SD 516] 49 Low back pain, with or without leg
symptoms
,3 months: 20.9%
3–6 months: 33.6%
.6 months: 44.6%
NRS pain intensity previous week:
M57.1 [SD 52.2]
* Split by the outcome recovery, which is defined as the patient’s individual perception of well-being within the current health state.
BQ, Bournemouth Questionnaire; MPQ, McGill Pain Questionnaire; NA, not available; NRS, Numeric Rating Scale.
10 E. Veirman et al.·4 (2019) e775 PAIN Reports
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rated as having low risk of bias. For 16.7% of the study samples,
the risk of bias was not rated because no performance measures
were reported for the outcomes of interest (Table 3). The reasons for
increasing the risk of bias related to the validity of the outcome,
overlap between predictors and outcomes, differences in the
assessment of outcomes across participants, differences in the
assessment of outcomes compared with the development study,
and blinding.
3.4.3.1. Validity of outcome definition
Outcome measures that mixed outcome domains were rated as
inadequate. Also, composite outcomes that combined outcome
measures or outcome domains were considered inadequate.
28
For example, the 10-item modified version of the Oswestry
Disability Index contains items that assess activity limitations and
participation restrictions.
22
Mixed or composite outcomes have
the potential to increase the event rate and thus the statistical
power. However, they may be misleading when the outcome
domains included in the outcome differ in importance to patients,
the number of events in the outcome domains of greater
importance is small, and the magnitude of effect differs markedly
across the outcome domains.
72
3.4.3.2. Exclusion of predictors from outcome definition
Next, overlap between predictor and outcome assessment was
frequently observed and considered as problematic. Several
Table 3
Methodological quality of included validation studies.
Study Participant selection Predictors Outcomes Sample size and participants flow Analysis
ALBPSQ
Hurley et al.
43
High High Unclear Unclear Moderate
Hurley et al.
44
High High Unclear Unclear Moderate
Grotle et al.
32
Moderate Unclear Moderate Unclear Moderate
Grotle et al.
33
Moderate High Unclear Unclear Moderate
Grotle et al.
31
Moderate Unclear Moderate Unclear Moderate
Heneweer et al.
38
Unclear Unclear — Unclear —
Vos et al.
124
Moderate High Unclear Unclear Moderate
OMPSQ
Linton and Boersma
61
High Unclear Moderate Unclear Moderate
Dunstan et al.
18
Low Unclear — Unclear —
Margison and French
66
—Derivation sample Low Moderate Moderate Unclear Moderate
Margison and French
66
—Validation sample Low Moderate Moderate Unclear Moderate
Maher and Grotle
64
—Australasian sample Moderate Moderate Unclear Unclear Moderate
Maher and Grotle
64
—Norwegian sample Low Moderate Unclear Unclear Moderate
Gabel et al.
25
—OMPSQ Moderate Moderate Moderate Unclear Moderate
Linton et al.
63
Unclear Low Unclear Unclear Moderate
Nonclercq and Berquin
81
Moderate Unclear Moderate Unclear Moderate
Dagfinrud et al.
14
High Unclear Unclear Unclear Moderate
Law et al.
55
Moderate Unclear Moderate High Moderate
Riewe et al.
92
High High Moderate Moderate Moderate
OMSQ
Gabel et al.
25
Moderate Moderate Moderate Unclear Moderate
Gabel et al.
24
Low Moderate Moderate Unclear Moderate
Gabel et al.
23
Low Moderate Moderate Unclear Moderate
OMPSQs
Linton et al.
63
Unclear Low Unclear Unclear Moderate
Karran et al.
50
High Unclear Unclear Low Low
OMSQs
Gabel et al.
23
Low Unclear Moderate Unclear Moderate
HKF-R10
Riewe et al.
92
Moderate Low Moderate Moderate Moderate
PBSI
Sandborgh et al.
98
High Low Unclear Low Moderate
SBT
Hill et al.
41
Moderate Low Unclear Unclear Moderate
Fritz et al.
22
Moderate Low — Unclear —
Field and Newell
20
Moderate Low — Unclear —
Beneciuk et al.
5
Moderate Low Moderate Unclear Moderate
Morsø et al.
77
—UK sample Moderate Low Moderate Unclear Moderate
Morsø et al.
77
—Danish sample Moderate Low Moderate Unclear Moderate
Morsø et al.
78
—Primary care sample Moderate Low Unclear Unclear Moderate
Morsø et al.
78
—Secondary care sample Moderate Low Unclear Unclear Moderate
Foster et al.
21
Moderate Low — Unclear —
George and Beneciuk
28
Moderate Low — High Moderate
Newell et al.
80
Moderate Low — Unclear —
Kongsted et al.
53
Moderate Low Moderate Low Moderate
Karran et al.
50
Moderate Low Unclear Low Low
PICKUP
Traeger et al.
115
Moderate Unclear Unclear Low Low
Karran et al.
50
High Unclear Unclear Low Low
4 (2019) e775 www.painreportsonline.com 11
Table 4
Key predictor, outcome, sample size and participants flow, and analysis characteristics of included validation studies.
Study N at baseline, (follow-up(s); N at
follow-up(s); % at final follow-up)
Outcome (assessment, applied cutoff) Events (N and/or %) Recommended criterion Performance measures
ALBPSQ
Hurley et al.
43
118 (at treatment discharge; 118; 100%) Pain intensity (MGPQ, NA)
Functional disability (RMDQ, NA)
Return to work (yes/no)
NA
NA
29/15
112 Kendall’s t
Kendall’s t
Mann–Whitney U tests, sensitivity, and
specificity
Hurley et al.
44
118 (12 months; 90; 76%) Pain intensity (MGPQ, NA)
Functional disability (RMDQ, NA)
Work loss (yes/no)
NA
NA
14/55 (20.2%/79.7%)
112 Kendall’s t
Kendall’s t
Mann–Whitney U tests, sensitivity, and
specificity
Grotle et al.
32
123 (1, 3 months; 120; 98%) Pain intensity (NRS, NA)
Disability (RMDQ, .4 on both 1 and 3
months)
Sickness absence (NA)
NA
24%
8% at 1 month
6% at 3 months
90 NA
ORs
NA
Grotle et al.
33
123 (6 and 12 months; 112; 91%) Pain intensity (NRS, score .2)
Disability (RMDQ, .4)
Work loss (disability days, .30 days)
NA
NA
NA
90 (105 for 12 months RMDQ) Specificity, sensitivity, LRs (2/1), AUC,
and ORs (for all outcomes)
Grotle et al.
31
123 (1, 3, 6, 9, and 12 months; 112; 91%) Pain intensity (NRS, NA)
Disability (RMDQ, .4)
Sickness absence (disability days, NA)
NA
17% at 12 months
12 (11%) at 1 month
10 (9%) at 3 months
7 (7%) at 6 months
7 (8%) at 9 months
9 (9%) at 12 months
112 NA
ORs at 12 months
NA
Heneweer et al.
38
66 (2, 4, 8, and 12 weeks; 56; 95%) Pain intensity (VAS, NA)
Disability (QBPDS, NA)
Work absenteeism (yes/no)
NA
NA
7/49 (87%/13%) at 12
weeks
Continuous NA
NA
NA
Vos et al.
124
187 (6, 12, 26, and 52 weeks; 180; 96%) Pain intensity (NRS, NA)
Sick leave (.7 days)
NA
31 (22%)
72 NA
Specificity, sensitivity, PPV, NPV, and AUC
OMPSQ
Linton and Boersma
61
122 (6 months, 107; 88%) Pain intensity (OMPSQ items, $17)
Function (OMPSQ items, $45)
Sick leave (.0 days, .30 days)
48%
60%
60%/23%/17%
90 Specificity, sensitivity, and Wilks’ l(for all
outcomes)
Dunstan et al.
18
55 (6 months, 55; 100%) Return to work (yes/no) 24/31 Continuous NA
Margison and French
66
—Derivation
sample
200 (200; 100%) Clinical discharge status (fit/not fit for
return to work)
NA 147 Sensitivity and FPR
Margison and French
66
—Validation
sample
211 (211; 100%) Clinical discharge status (fit/not fit for
return to work)
195/16 147 Specificity, sensitivity, PPV, and NPV
Maher and Grotle
64
—Australasian
sample
133 (6 weeks, 3, 12 months; 133; 100%) Pain intensity (OMPSQ item, NA)
Disability (RMDQ, NA)
NA
NA
Continuous Regression coefficients
Regression coefficients
Maher and Grotle
64
—Norwegian
sample
97 (4 weeks, 3, 12 months; 97; 100%) Pain intensity (OMPSQ item, NA)
Disability (RMDQ, NA)
NA
NA
Continuous Regression coefficients
Regression coefficients
Gabel et al.
25
—OMPSQ 66 (6 months; 58; 88%) Problem severity (NRS, .1)
Functional status (SFI, $10%)
Absenteeism (PDO, .0 days)
Long-term absenteeism (PDO, .28 days)
NA
NA
NA
NA
113
113
115
120
Specificity, sensitivity, LRs, and AUC (for all
outcomes)
Gabel et al.
25
—OMSQ 106 (6 months; 97; 92%) Problem severity (NRS, .1)
Functional status (SFI, $10%)
NA
NA
112
112
Specificity, sensitivity, LRs, and AUC (for all
outcomes)
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12 E. Veirman et al.·4 (2019) e775 PAIN Reports
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Table 4 (continued)
Key predictor, outcome, sample size and participants flow, and analysis characteristics of included validation studies.
Study N at baseline, (follow-up(s); N at
follow-up(s); % at final follow-up)
Outcome (assessment, applied cutoff) Events (N and/or %) Recommended criterion Performance measures
Absenteeism (PDO, .0 days)
Long-term absenteeism (PDO, .28 days)
NA
NA
116
120
Linton et al. (2011) 183 (12 months; 183; 100%) Sick leave (.14 days of work during past 6
months)
171 90 Specificity, sensitivity, LRs, and AUC
Gabel et al. (2012) 143 (1 month, 6 months; 43; 100%) Problem severity (NRS, .10%)
Functional status (SFI/LLFI, .10%)
Absenteeism (PDO, .0 days)
Long-term absenteeism (PDO, .28 days)
NA
NA
NA
NA
114 Specificity, sensitivity, and LRs (1) (for all
outcomes)
Nonclercq & Berquin (2012) 91 (6 months; 73; 80%) Pain intensity (OMPSQ items, .16)
Function (OMPSQ items, ,45; ODI,
.20%)
Work absence (OMPSQ item, .6 [scores
corresponding to .30 days])
34%
58%; 18%
37%
Low/high
75/97
76/86
75/106
71/106
Specificity, sensitivity, PPV, and AUC (for all
outcomes)
Dagfinrud et al. (2013) 157 (8 weeks; 128; 82%) Functional limitations (ODI/NDI, NA) NA Continuous/105 Regression coefficients
Gabel et al. (2013) 143 (6 months; 143; 100%) Problem severity (NRS, .10%)
Functional status (PRO, .10%)
Absenteeism (PDO, .0 days)
Long-term absenteeism (PDO, .28 days)
NA
NA
NA
NA
126 Specificity, sensitivity, LRs (1), and
t
tests
Law et al. (2013) 241 (3–4 weeks, 12 months; per outcome:
184, 160, 220, 202; 76%, 66%, 91%,
84%)
Pain intensity (NRS, NA)
Functional disability (RMDQ, NA)
Return to work (yes/no)
Sick leave (.30 days)
NA
NA
171/49 at 12 months
88 at 12 months
105, 130 NA
NA
Specificity, sensitivity, AUC, and ORs (both
outcomes)
Riewe et al. (2016) 241 (6 months; per outcome: 122, 122,
108; 51%, 51%; 45%)
Pain intensity (OMPSQ items, $17)
Function (OMPSQ items, ,45)
Sick leave (.0 days)
61
64
40
84 Specificity, sensitivity, PPV, NPV, LRs
(1/2), and AUC (for all outcomes)
OMPSQs
Linton et al.
63
183 (12 months; 183; 100%) Sick leave (.14 days of work during past 6
months)
171 50 Specificity, sensitivity, LRs, and AUC
Karran et al.
50
220 (4 months; 195; 89%) Poor outcome (composite pain/disability
NRS, $3)
Pain intensity (NRS, $3)
Disability (NRS, $3)
High pain (NRS, $5)
High disability (NRS, $5)
164 (84%)
155 (79%)
159 (82%)
129 (66%)
126 (65%)
Lowest 10th through highest 10th decile
of risk
Nagelkerke
R
2
, AUC, calibration plot (for
poor outcome), net benefit, post hoc
sensitivity analysis (for poor outcome and
high pain), and AUC (for all outcomes)
OMSQs
Gabel et al.
23
143 (6 months; 143; 100%) Problem severity (NRS, .10%)
Functional status (PRO, .10%)
Absenteeism (PDO, .0 days)
Long-term absenteeism (PDO, .28 days)
NA
NA
NA
NA
72 Specificity, sensitivity, LRs (1), and
t
tests
STarT Back
Hill et al.
41
500 (6 months; 500, 100%) Disability (RMDQ, $7) Low risk: 39 (16.7%)
Medium risk: 99 (53.2%)
High risk: 58 (78.4%)
Low, medium, and high risk groups Sensitivity, specificity, LRs (1/2), and
AUC
Fritz et al.
22
214 (at each visit; 177, 83%) Pain intensity (NRS, NA)
Disability (DISQ, NA)
NA Low, medium, and high risk groups NA
NA
Field and Newell
20
404 (14, 30, 90 days; per follow-up per
outcome: 218/204, 123/119, 142/136;
54%/50%, 30%/29%, 35%/34%)
Pain (BQ, NA)
Total (BQ, NA)
NA
NA
Low, medium, and high risk groups NA
NA
(continued on next page)
4 (2019) e775 www.painreportsonline.com 13
Table 4 (continued)
Key predictor, outcome, sample size and participants flow, and analysis characteristics of included validation studies.
Study N at baseline, (follow-up(s); N at
follow-up(s); % at final follow-up)
Outcome (assessment, applied cutoff) Events (N and/or %) Recommended criterion Performance measures
Beneciuk et al.
5
146 (4 weeks, 6 months; 128, 111; 88%,
76%)
Pain intensity (NRS, NA)
Disability (RODQ, NA)
NA
NA
Continuous Regression coefficients
Regression coefficients
Morsø et al.
77
—UK sample 856 (3 months; 845, 99%) Pain intensity (NRS, $8)
Activity limitations (RMDQ, .30)
Pain bothersomeness (1 item, severe or
very severe)
NA
36%
NA
Low, medium, and high risk groups AUC
RR, ORs, and AUC
AUC
Morsø et al.
77
—Danish sample 344 (3 months, 322, 94%) Pain intensity (NRS, $8)
Activity limitations (RMDQ, .30)
Pain bothersomeness (1 item, severe or
very severe)
NA
47%
NA
Low, medium, and high risk groups AUC
RR, ORs, and AUC
AUC
Morsø et al.
78
—Primary care
sample
172 (6 months; 144, 83%) Pain intensity (NRS, $8)
Activity limitations (RMDQ, .30)
NA
40.2%
Low, medium, and high risk groups AUC
RR, ORs, and AUC
Morsø et al.
78
—Secondary care
sample
960 (6 months; 960, 100%) Pain intensity (NRS, $8)
Activity limitations (RMDQ, .30)
NA
69.0%
NA
Low, medium, and high risk groups AUC
RR, ORs, and AUC
Foster et al.
21
368 (2, 6 months; 254 (69%), 233 (63%) Pain intensity (NRS, NA)
Disability (RMDQ, NA)
NA Low, medium, and high risk groups NA
NA
George and Beneciuk
28
146 (6 months; 111, 76%) Pain intensity (NRS 50)
Disability (RMDQ, #2)
Recovery (NRS 50 and RMDQ #2)
14 (12.6%)
36 (32.4%)
14 (12.6%)
Low, medium, and high risk groups Wilks’ l
Wilks’ l
Wilks’ l
Newell et al.
80
Initial treatment/2-days post-initial
treatment: 749/716 (14, 30, 90 days; per
follow-up: 542, 416, 318; 58%
Pain (BQ, NA)
Total (BQ, NA)
NA Low, medium, and high risk groups NA
Kongsted et al.
53
859 (2 weeks, 3, 12 months; per follow-up:
710, 676, 636; 83%, 79%, 74%)
Pain intensity (NRS, .0)
Disability (RMDQ, .8)
92% at 2 weeks
60% at 3 months
56% at 12 months
79% at 2 weeks
61% at 3 months
57% at 12 months
Low, medium, and high risk groups LR (1/2), AUC, and
R
2
Karran et al.
50
220 (4 months; 195; 89%) Poor outcome (composite pain/disability
NRS, $3)
Pain intensity (NRS, $3)
Disability (NRS, $3)
High pain intensity (NRS, $5)
High disability (NRS, $5)
164 (84%)
155 (79%)
159 (82%)
129 (66%)
126 (65%)
Low, medium, and high risk groups Nagelkerke
R
2
, AUC, calibration plot (for
poor outcome), net benefit, post hoc
sensitivity analysis (for poor outcome and
high pain), and AUC (for all outcomes)
HKF-R10
Riewe et al.
92
242 (6 months; 128; 58%) Pain intensity (HKF-R10 items, $30) 90 37 Specificity, sensitivity, PPV, NPV, LRs
(1/2), and AUC
PBSI
Sandborgh et al.
98
168 (8 months; 146, 85%) High pain intensity (NRS, $5)
High disability (PDI, $35)
NA
33
Continuous NA
Specificity, sensitivity, and Wilks’ l
(continued on next page)
14 E. Veirman et al.·4 (2019) e775 PAIN Reports
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studies used items of the investigated screening tool, measured
at follow-up, as primary outcome. For instance, Linton and
Boersma
61
used the OMPSQ in its entirety during the outcome
assessment, selecting the items on pain, activity limitations, and
sick leave. Studies also often included outcomes that showed
overlap with domains assessed by the screening tool items. In the
study by Grotle et al.,
32
both the activity items of the ALBPSQ and
the items of the Roland–Morris Disability Questionnaire (RMDQ)
outcome measure address activity limitations. This overlap may
lead to overestimation of the predictive performance of the
screening tool.
91,117
3.4.3.3. Definition and assessment of outcomes across
participants
For all studies, outcomes were defined and determined in
a similar way across participants. However, they were not
always defined and determined similarly to those in the
development studies. Indeed, although different outcomes
most probably have different predictors, a number of studies
targeted outcome domains (eg, pain intensity through OMPSQ
items and activity limitations through the RMDQ and not
participation restrictions through accumulated sick leave)
64
which differed from the development study. Other studies
focused on similar outcome domains, but used other measures
(eg, activity limitations through a NRS and not the RMDQ due to
the large amount of missing data).
50
3.4.3.4. Definition and assessment of outcomes compared
with the developmental model
In addition, some studies focused on similar outcome domains
and used the same outcome measures as the development
study, but used different cutoff points for the outcome measures
from those used in the development study. For example, large
differences were observed for sick leave. Vos et al.
124
defined
long-term sick leave as .7 days off work, while Linton and
Hallden
62
initially defined long-term sick leave as being sick listed
for .30 days (Table 4).
3.4.3.5. Determination of outcomes without knowledge of
predictor information
Information on blinding was most often not reported, which could
either mean that the outcome assessment was not blinded or that
it was blinded but not described. In cases where studies reported
on blinding of outcome assessment, researchers usually applied
blinding.
24
3.4.4. Sample size and participant flow
There was a huge difference between sample sizes of the
validation studies. Sample sizes varied considerably at follow-up,
ranging from ,100 participants,
18,25,38,43,44,64,81
over 500 to
1000 participants,
41,53,77,78,80
to .1500 participants.
115
Also,
the number of outcome events differed largely between studies
ranging from 14 to 291. The most frequently observed time
intervals were 3, 6, and 12 months
92
(see Table 4 for an
overview).
Few studies were rated as having low (16.7%), moderate
(2.4%), or high (4.8%) risk of bias for the PROBAST “sample size
and participants flow” domain. The majority of studies were
assigned an unclear risk of bias (76.2%; Table 3) because
insufficient information was presented to evaluate the number of
Table 4 (continued)
Key predictor, outcome, sample size and participants flow, and analysis characteristics of included validation studies.
Study N at baseline, (follow-up(s); N at
follow-up(s); % at final follow-up)
Outcome (assessment, applied cutoff) Events (N and/or %) Recommended criterion Performance measures
PICKUP
Traeger et al.
115
1528 (3 months; per outcome: 1528,
1525, 1504; 100%, 99%, 98%)
Pain intensity (Likert, .2)
High pain intensity (Likert, .3)
Disability (Likert, $2)
291 (19%)
162 (10%)
217 (14%)
Calculator Nagelkerke
R
2
, AUC, calibration plot
(intercept/slope), net benefit at incidence
rate cutoff, and net number of unnecessary
interventions avoided at 30% risk cutoff (for
all outcomes)
Karran et al.
50
220 (4 months; 195; 89%) Poor outcome (composite pain/disability
NRS, $3)
Pain intensity (NRS, $3)
Disability (NRS, $3)
High pain intensity (NRS, $5)
High disability (NRS, $5)
164 (84%)
155 (79%)
159 (82%)
129 (66%)
126 (65%)
Lowest 10th through highest 10th decile
of risk
Nagelkerke
R
2
, AUC, calibration plot (for
poor outcome), net benefit, post hoc
sensitivity analysis (for poor outcome and
high pain), and AUC (for all outcomes)
AUC, area under the curve; BQ, Bournemouth Questionnaire; DISQ, 10-item modified version of the Oswestry Low Back Pain Disability Questionnaire; FNR, false negative rate; FPR, false positive rate; LLFI, Lower Limb Functional Index; LRs, likelihood ratios; MCID, minimal clinically important difference; MGPQ,
McGill Pain Questionnaire; NA, not available; NDI, Neck Disability Index; NPV, negative predicted value; NRS, Numeric Rating Scale; ODI, Oswestry Disability Index; ORs, odds ratios; PDI, Pain Disability Index; PDO, paid days off; PPV, positivepredicted value;PRO, patient-reported outcome; QBPDS, Quebec Back
Pain Disability Scale; RMDQ, Roland–Morris Disability Questionnaire; RODQ; Revised Oswestry Disability Questionnaire; RR, relative risk; SFI, Spine Functional Index; VAS, Visual Analogue Scale.
4 (2019) e775 www.painreportsonline.com 15
outcome events, the inclusion of enrolled participants, or the
occurrence and handling of missing data. The reasons for
increasing the risk of bias related to the number of outcome
events, the time interval between the assessment of the
screening tools and the outcome assessment, dropout, and
missing data.
3.4.4.1. Number of outcome events
The number of events (ie, the number of individuals with the
outcome event) was not reported in a large number of
studies
5,14,21–25,33,38,64,66,80
and considered inappropriate in 5
studies.
28,31,44,63,81
These studies reported ,20 events, raising
the issue of overfitting (ie, the probability of an event is typically
underestimated in low-risk patients and overestimated in high-
risk patients).
4,85
3.4.4.2. Time interval between predictor assessment and
outcome determination
Studies sometimes performed multiple follow-ups, reporting
results on the predictive validity for one or only a selection of
follow-ups (eg, follow-ups at 2- and 4-week intervals until
discharge or study completion at 6 months, report of results for
6-month follow-up).
24
Time between screening and outcome
assessment was considered inappropriate when results only
reported on follow-ups of ,3 months, as chronic pain is defined
as pain $3 months (eg, six weeks).
66
Follow-ups .12 months
were also considered inappropriate, as people’s (mental) health
status changes during the follow-up period and the baseline
information becomes increasingly less accurate as time passes
(none of the studies). In addition, follow-ups that varied across
participants (eg, at treatment discharge, dependent on the
number of therapy treatments)
43
were deemed inappropriate.
Surprisingly, most studies did not present any theoretical
considerations underpinning the choice of a specific follow-up
timeframe (Table 4).
3.4.4.3. Inclusion of enrolled participants in analysis
Dropout attrition is often poorly reported or presented in a way
that prevents readers from being able to fully understand the
risk of attrition bias. Studies often limit themselves to reporting
the dropout rate. We considered dropout as inappropriate
when .20%
96
of the participants were lost at follow-
up.
5,20,21,28,44,53,80,92
However, dropout can occur for a num-
ber of reasons that may lead to differential dropout, such as
motivation (participants lost interest), mobility (participants
moved and are no longer able to continue participation),
morbidity (participants experience illness preventing their
participation), or mortality (participants die before study
completion). For example, a low psychosocial risk group may
lose more unmotivated participants—that in turn may have
different outcomes due to being unmotivated—than a high
psychosocial risk assessment group, and this differential
dropout may lead to differences in outcomes measured
among the remaining participants. Reasons for dropout are,
however, rarely specified among the included studies.
Furthermore, although characteristics of dropout (ie, baseline
characteristics: eg, age, sex, pain intensity, and pain duration)
should be available to examine whether systematic differences
exist between those who completed a study and those who
dropped out,
36
only few studies reported on the differences
between completers and noncompleters.
5,28,44,50,53,80,81,98
Of these studies, some provided a detailed tabulation of the
characteristics and statistical comparison,
50
whereas other
studies only reported the characteristics for which differences
were found.
5
Further, numerous studies do not mention
whether differences were examined, which could either mean
that differences were examined for all or some baseline
characteristics but none were found, or no differences were
tested.
55
3.4.4.4. Handling of missing data
Finally, studies did often not report on missing values or how they
were or would have been handled,
78
which could either mean
that there were no missing data or that missing data were present
but not described. Missing values were considered inappropri-
ately handled when complete-case analysis was applied.
92
They
were judged as appropriately handled when multiple imputation
was used.
74
For example, Karran et al.
50
used Little’s Missing
Completely at Random test to determine whether values were
missing completely at random and used a maximization algorithm
to impute missing values.
3.4.5. Analyses
Statistics of reported performance measures for pain and related
outcomes varied widely. Many studies report sensitivity and
specificity of screening tools,
61
whereas others included further
details, reporting area under the curve using receiver operating
characteristics analyses.
53
Wilk’s lambda for discriminative
validity is also reported in some studies,
28,98
as are the odds
ratios from logistic regression analyses
33
(see Table 4 for an
overview).
For the PROBAST “analyses” domain, the majority of study
samples were assigned a moderate risk of bias (76%), and
only a few study samples were rated as low risk of bias (9.5%).
For 14.3% of the study samples, no risk of bias labels was
assigned because no performance measures were reported
for the outcomes of interest. The reason for increasing the
risk of bias related to the poor use of the performance
measures.
3.4.5.1. Evaluation of relevant model performance measures
Statistical analyses were found appropriate when they reflected
both calibration (ie, agreement between predicted and observed
event rates) and discrimination (ie, the screening tool’s ability to
distinguish between patients developing and not developing the
outcome of interest) components of predictive validity for pain
and related outcomes.
74
This was only the case in 2 studies.
50,115
These studies also reported more recently introduced perfor-
mance measures (eg, net benefit). Moreover, not all studies
reported performance measures for pain and related outcomes
despite assessing those outcomes. Some studies reported on
the course of particular pain and related outcomes. For example,
Grotle et al.
31
reported the course of pain intensity, disability, and
sickness absence from baseline across follow-ups, but reported
no information on the predictive validity of the ALBPSQ for those
outcomes, except for disability where odds ratios were provided.
Other studies reported differences in mean scores on the
screening tool for particular outcomes, used change scores for
particular outcomes, or reported on composite outcomes. For
example, Dunstan et al.
18
reported differences in mean ALBPSQ
scores between those who did and did not return to work.
Dagfinrud et al.
14
assessed functional limitations at baseline and
16 E. Veirman et al.·4 (2019) e775 PAIN Reports
®
follow-up; however, the predictive validity of the OMPSQ was
examined for functional improvement, and the categorization of
those that were improved and those that were not was based on
change scores. Finally, George and Beneciuk
28
assessed pain
intensity and disability; yet, discriminative validity was only
examined for recovery, a composite pain intensity and disability
outcome. Still others assessed pain and related outcomes, but
only reported performance measures related to outcomes that
were not within the scope of the current review. For instance,
Heneweer et al.
38
assessed pain intensity, disability, work
absenteeism, and self-reported recovery, but only reported area
under the curve values for the ALBPSQ total and subscale scores
in predicting recovery or nonrecovery at final follow-up (Table 4).
4. General discussion
This review (1) identified multidimensional screening tools that
assess psychosocial risk factors for poor pain outcomes, (2)
appraised the quality of the evidence in prospective studies
validating these tools, and (3) synthesized common methodo-
logical concerns in these validation studies.
Seven screening tools were identified, all developed for use in
primary care settings to predict chronic pain (HKF-R10, PICKUP)
or chronic disability (ALBPSQ/OMPSQ, OMPSQs, OMSQs,
PBSI, and STarT Back) in patients with back pain. Notably, we
found no tools for the prediction of pain-related distress, a key
indicator of health, or for the prediction of acute pain onset,
including postoperative pain. These appear to be significant gaps
in the literature.
101
We assessed the quality of the evidence of 32 studies including
42 study samples aiming to validate the predictive value of
identified screening tools. Overall, studies showed a moderate
risk of bias, which varied largely from domain to domain. Here, we
discuss the most notable methodological problems.
Most screening tools were developed to predict the chron-
ification of pain problems, except for the SBT and the PBSI, which
were developed to support decision-making for a wide range of
patients with pain conditions, regardless of pain duration.
41,97
It is
reasonable to expect that validation studies include similar patient
populations as those from the development study. Surprisingly,
this was often not the case. Indeed, although most tools were
developed to be used in patients with acute pain, a substantial
number of these validation study samples included also patients
with chronic pain. This is concerning for several reasons. First,
these studies do not address the same key question as the
development study. It may also well be that risk factors
developing chronic pain are different from predictors for the
maintenance of chronic pain. Second, it is likely that the recovery
rate of chronic pain is less than the one of acute pain.
39
Therefore,
the presence of chronic patients with chronic pain may (at least
partly) account for the apparently high performance in predicting
poor pain outcomes. This complicates interpretation of results
and may result in an underestimation or overestimation of the
predictive value of the screening tools. There is a need to define
the inclusion criteria for participants in a more clear and restrictive
way and to align these with the original purpose of the screening
tools.
The success of initial studies revealing the value of psychoso-
cial risk factors in predicting chronic pain problems has boosted
research in this area. However, some of the original studies were
designed with specific (clinical) groups in mind. An example is the
ALBPSQ, which was designed to target a working population.
Some items that are directly related to work (eg, “If you take into
consideration your work routines, management, salary,
promotion possibilities, and workmates, how satisfied are you
with your job?”) are therefore inapplicable to a nonworking
population. The authors have addressed this problem in various
ways. Some replaced the missing scores for those items by the
mean for nonworking patients.
33
Others asked patients to fill out
those questions related to either current paid or unpaid work.
43,44
Likewise, screening tools were developed for patients with
musculoskeletal, in particular back pain, but studies have also
investigated the value of the tools in other patient groups (eg,
neck pain).
124
Sometimes, items have been adapted accordingly
and/or left out. There is a lack of evidence, however, to suggest
that these changes are appropriate for the populations in
question.
All studies agree that screening tools need to predict poor pain
outcome. However, there is less agreement about what exactly
poor outcome means. Indeed, a gold standard for poor outcome
is lacking. The constructs addressed and the measures and
cutoffs used vary largely between studies. For some, poor
outcome simply means pain, for others not being able to work, or
difficulties in performing physical activities. However, different
outcomes most probably also have different predictors. The
broad use of the umbrella term “disability” brings additional
complications. Indeed, in pain research, “disability” may indicate
difficulties in performing particular physical activities (eg, ability to
walk, eat, shower, or dress) but also problems related to social
role functioning (eg, sick leave, days absent from work, or return
to work status). According to the International Classification of
Functioning (ICF),
130
these are 2 different constructs, ie, activity
limitations and participation restrictions, which should not be
confused. The lack of a gold standard may also explain the
inconsistency in criteria used across studies. For instance, Morsø
et al. defined poor pain outcome as a score greater than 7 on an
11-point NRS,
77,78
whereas George and Beneciuk
28
defined it as
a score greater than 0. It is obvious that the patients defined as
recovered differ between these studies. The use of an agreed-
upon set of outcome measures may provide a solution.
10,11,89
In
doing so, we also recommend the selection of measures that are
readily applicable to different contexts—occupational and non-
occupational settings—and to different pain problems. Such
measures already exist, but are underused (eg, Patient-Reported
Outcomes Measurement Information System, PROMIS,
9,118,119
available at www.healthmeasures.net).
Some of the identified screening tools were developed to
screen for psychosocial risk factors (“yellow flags”), or, at least,
are presented as such in studies. Some cautionary notes are
warranted. First, all screening tools also include items that could
be categorized otherwise (eg, pain duration and disability
compensation). Second, screening tools often contain items that
could equally well be the primary outcomes (pain intensity,
disability, and days off work). Although this may be less of
a problem when simply aiming to predict, it is premature to
explain the predictive power of these instruments in terms of
psychosocial processes. Indeed, given that it is generally known
that the best predictor of events in the future is their occurrence in
the present or past, it remains to be investigated whether the
predictive validity of screening tools is due to the overlap between
predictor and outcome.
91,117
To address this problem, one may
examine whether tools are able to predict outcomes, beyond the
predictive power of baseline pain and pain-related disability.
Most studies are not in line with the current guidelines for
reporting measures of performance.
110,111
In fact, there is a large
disparity in reported performance measures. Many studies
reported conventional performance measures, often reporting
either calibration (ie, how close predictions are to observed
4 (2019) e775 www.painreportsonline.com 17
outcomes) or discrimination (ie, screening tool’s ability to
correctly distinguish the 2 outcome classifications of event vs
nonevent). However, the reporting of both performance
measures is crucial. Furthermore, most studies do not
consider the clinical consequences of decisions made using
a screening tool. Therefore, there is the implicit assumption
that false-positive (ie, patient being treated unnecessarily) and
false-negative (ie, patient not getting a treatment that (s)he
would benefit from) predictions are equally harmful (ie, equally
weighted). More recent studies
50,115
do consider the relative
harms or benefits of these alternative clinical outcomes. They
apply novel performance measures such as net benefit (ie, the
expected utility of a decision to treat patients at some
threshold, compared with a decision based on an alternative
policy such as treating nobody)
75,110,111,120,121
(see also
www.decisioncurveanalysis.org).
An assessment of the risk of bias was not possible in
a considerable number of studies because of incomplete
reporting. A balanced evaluation of the risk of bias of studies
may be impeded due to nontransparent reporting. An increased
quality of reporting was observed over time, but there is still room
for improvement and there is a need for guidance. The “Trans-
parent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis” (TRIPOD) statement is particularly
helpful and provides guidance for the reporting of studies that
develop, validate, or update prediction models
12,73
(available
atwww.tripod-statement.org). We encourage researchers to
follow its recommendations. Equally important are the availability
of study protocols and the availability of data sets. Protocol
registration, either through publications, or through open science
applications, may reduce the impact of publication bias.
84
A large
number of validation studies in our review reported significant
results; yet, only 2 studies mentioned a protocol.
50,115
Protocol
registration may also reduce reporting bias.
40
It is common
practice to measure several outcomes, but the lack of a readily
accessible research protocol makes these studies vulnerable to
selective reporting of analyses that “worked.”
35
Another possi-
bility is to make data sets open, ie, available to all researchers.
115
Available data sets provide the opportunity to conduct secondary
analyses that may be informed by advances in theory and
scientific standards in the field.
There are some limitations to our review. First, we used a strict
search strategy. We excluded batteries of questionnaires and
tools that were not originally developed in the context of pain. This
may have resulted in missing instruments that are potentially
valuable. For example, the Amsterdam Preoperative Anxiety and
Information Scale (APAIS) was originally developed to evaluate
patient’s preoperative anxiety and need for preoperative in-
formation regarding the scheduled surgery and anesthesia.
70
Subsequently, this tool was used to predict postoperative
pain.
46,48
Second, we focused upon multidimensional screening
tools. Otherwise, one may make use of unidimensional ques-
tionnaires assessing single psychosocial risk factors to investi-
gate the predictive power of unique psychosocial variables (eg,
Pain Catastrophizing Scale
113
and Tampa Scale for Kinesiopho-
bia
69
) for poor pain outcomes. For screening purposes, however,
one should aim to minimize the burden of filling out questionnaires
for participants. The use of large questionnaire batteries should
therefore be avoided. Third, this research field is quickly evolving,
with new validation studies appearing at a fast pace. Since our
search, new instruments have been validated in an independent
study. For instance, the Optimal Screening for Prediction of
Referral and Outcome cohort yellow flag assessment tool was
developed in a cross-sectional cohort in 2016.
58
Recently,
a validation study was published.
29
Fourth, clinical prediction
modelling is a dynamic and evolving field
15,47,56,94,108–111
(see
also progress-partnership.org). One should keep in mind that the
present review is an exploratory mapping of this rapidly evolving
field. Assessment of the quality evidence in the included studies
was based upon a prepublication version of the PROBAST. This
version did not yet provide a guideline for scoring the questions.
We constructed, therefore, our own coding system. Now,
PROBAST has been published, with some minor changes from
the prepublication version of the PROBAST (eg, the signaling
questions of the domain “Sample size and participants flow” are
now included in the domain Outcomes and the domain
Analysis).
76,128
Despite this minor changes, the resulting map-
ping fulfills the primary goal of providing an entry point to reduce
risk of bias in this field. Fifth, we did not perform a meta-analysis.
Several meta-analyses are available that synthesize the predictive
value of screening tools. They indicate that (1) the predictive value
of these screening is highly variable depending on the pain
outcome of interest (eg, pain and disability) and (2) substantial
heterogeneity between studies exist.
49,99
Taking into account
methodological differences and quality criteria is therefore crucial
to further our understanding of the predictive value of screening
tools. Our insights have the potential to improve research in this
area and decision-making based on this research.
Disclosures
The authors have no conflict of interest to declare.
Preparation of this article was supported by funding from the
European Union’s Horizon 2020 research and innovation pro-
gram (Grant 633491).
Article history:
Received 11 March 2019
Received in revised form 11 June 2019
Accepted 26 June 2019
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