Predicting pain outcomes in primary care

Pain conditions, acute, sub-acute and chronic, are common presentations in primary care. Many patients recover or receive the help they need to cope with their pain, but for some patients the pain escalates, develop into chronic pain, or become non-manageable. This leads to suffering and limitations for the individual. To identify these patients before they go on to develop a more severe pain condition a clinical prediction rule, using patient specific characteristics, can be used to predict prognosis in individual patients.

SBU Enquiry Service

Consists of structured literature searches to highlight studies that can address questions received by the SBU Enquiry Service from Swedish healthcare or social service providers. We assess the risk of bias in systematic reviews and when needed also quality and transferability of results in health economic studies. Relevant references are compiled by an SBU staff member, in consultation with an external expert when needed.

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Question

What systematic reviews are there on clinical prediction rules used in primary care to identify patients with pain that will escalate or become persistent?

Identified literature

Table 1. Systematic reviews with low/medium risk of bias
AUC = Area under the curve; CI = Confidence interval; CPR = Clinical prediction rule; LBP = Low back pain; NLR = Negative likelihood ratio; PLR = Positive likelihood ratio
Included studies Population/Intervention Outcome and Results
Silva et al, 2022 [1]
No prognostic model for people with recent-onset low back pain has yet been demonstrated to be suitable for use in clinical practice: a systematic review.
18 included studies describing 17 different prediction models, of which 6 prediction models were classified as having low risk of bias
Setting:
Australia: 5 studies
USA: 4 studies
China: 3 studies
Netherlands: 2 studies
Denmark: 2 studies
Germany: 1 study
Canada: 1 study
Population:
Patients with recent onset low back pain (<3 months)
Intervention:
Clinical prediction models (in the stage of development or validation) for prognosis (pain intensity, progression to chronic LBP, severity of symptoms, disability, functional status, recovery or non-recovery)
Discrimination
Original Örebro Musculoskeletal Pain Questionnaire (ÖMPQ)
Non-recovery at 6 months (1 study):
AUC 0.58
(95% CI, 0.51 to 0.65)
Cut-off >99 non-informative

Non-recovery at 6 months (1 study):
AUC 0.61
(95% CI, 0.54 to 0.67)
Cut-off >68 poor

Da Silva Clinical Prediction Model
Recovery from pain at 3 months (1 study):
C-statistic 0.71
(95% CI, 0.63 to 0.78)
good

Hancock Clinical Prediction Model
Recovery from pain at 3 months (1 study):
C-statistic 0.60
(95% CI, 0.56 to 0.64)
poor

Low Back Pain Perception Scale (LBPPS)
Non-recovery at 12 months (1 study):
AUC 0.59
(95% CI, 0.52 to 0.66)
Cut-off ≥2 non-informative

Non-recovery at 12 months (1 study):
AUC 0.57
(95% CI, 0.50 to 0.64)
Cut-off ≥4 non-informative

Predicting the Inception of Chronic Pain Model (PICKUP) Chronic LBP at 3 months (1 study):
AUC 0.67
(95% CI, 0.64 to 0.70)
poor

Risk estimation by general practitioners
Non-recovery at 12 months (1 study):
AUC 0.59
(95% CI, 0.52 to 0.66)
non-informative
Authors' conclusion:
“Most prediction models for prognosis of patients with recent-onset LBP did not perform well at discrimination, few studies reported calibration and their performance varied across studies. It seems premature to advocate use of the available models, at their current state of development and validation, for low back pain in primary care, considering their generally poor methods and performance.”
Karran et al, 2017 [2]
Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis
18 included studies describing 7 different prediction models
Setting:
USA: 5 studies
United Kingdom: 3 studies
Australia: 2 studies
Netherlands: 2 studies
Norway, Denmark, China, Belgium, Germany, Canada: 1 study each
Population:
Patients with recent onset low back pain (<3 months)
Intervention:
Clinical prediction models (in the stage of validation) developed to provide prognostic information for musculoskeletal conditions.
Discrimination
STarT Back Tool
Pain (5 studies):
AUC 0.59
(95% CI, 0.55 to 0.63)
non-informative

Disability (3 studies):
AUC 0.74
(95% CI, 0.66 to 0.82) acceptable

Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ)
Pain (4 studies):
AUC 0.69
(95% CI, 0.62 to 0.76)
poor

Disability (3 studies):
AUC 0.75
(95% CI, 0.69 to 0.82)
acceptable

>28 days absenteeism at 6 months (3 studies):
AUC 0.83
(95% CI, 0.75 to 0.90)
excellent

>30 days absenteeism at 12 months (2 studies):
AUC 0.71
(95% CI, 0.64 to 0.78)
acceptable
Authors' conclusion:
“LBP screening instruments administered in primary care perform poorly at assigning higher risk scores to individuals who develop chronic pain than to those who do not. Risks of a poor disability outcome and prolonged absenteeism are likely to be estimated with greater accuracy. It is important that clinicians who use screening tools to obtain prognostic information consider the potential for misclassification of patient risk and its consequences for care decisions based on screening. However, it needs to be acknowledged that the outcomes on which we evaluated these screening instruments in some cases had a different threshold, outcome, and time period than those they were designed to predict.”
Haskins et al, 2015 [3]
Validation and impact analysis of prognostic clinical prediction rules for low back pain is needed: a systematic review
35 studies included
  • 9 studies describing prognostic CPR
  • 26 studies describing prescriptive CPR
Population:
Adults with LBP
Intervention:
Prognostic or prescriptive CPRs (in the stage of derivation, validation, or impact analysis) related to the nonsurgical management of adults with LBP.
Outcome:
The “Cassandra Rule”
≥50% disability
High/moderate risk vs low risk (3 studies):
PLR: 1.3 to 2.0
NLR: 0.25 to 0.40
Authors' conclusion:
“Most of the identified prognostic CPRs for LBP are in the initial phase of development and are consequently not recommended for direct application in clinical practice at this time. The body of evidence provides emergent confidence in the limited predictive performance of the Cassandra rule and the five-item Flynn manipulation CPR in comparable clinical settings and patient populations.”

References

1. Silva FG, Costa LO, Hancock MJ, Palomo GA, Costa LC, da Silva T. No prognostic model for people with recent-onset low back pain has yet been demonstrated to be suitable for use in clinical practice: a systematic review. J Physiother. 2022;68(2):99-109. Available from: https://doi.org/10.1016/j.jphys.2022.03.009.

2. Karran EL, McAuley JH, Traeger AC, Hillier SL, Grabherr L, Russek LN, et al. Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis. BMC Med. 2017;15(1):13. Available from: https://doi.org/10.1186/s12916-016-0774-4.

3. Haskins R, Osmotherly PG, Rivett DA. Validation and impact analysis of prognostic clinical prediction rules for low back pain is needed: a systematic review. J Clin Epidemiol. 2015;68(7):821-32. Available from: https://doi.org/10.1016/j.jclinepi.2015.02.003.

Published: Report no: ut202215 Registration no: SBU 2022/80

Literature search

Medline via OvidSP 16 May 2022

Title: Persistent pain
The final search result, usually found at the end of the documentation, forms the list of abstracts.

.ab. = Abstract; .ab,ti. = Abstract or title; .af. = All fields; Exp = Term from the Medline controlled vocabulary, including terms found below this term in the MeSH hierarchy; .sh. = Term from the Medline controlled vocabulary; .ti. = Title; / = Term from the Medline controlled vocabulary, but does not include terms found below this term in the MeSH hierarchy; * = Focus (if found in front of a MeSH-term); * or $ = Truncation (if found at the end of a free text term); .mp = Text, heading word, subject area node, title; " " = Citation Marks; searches for an exact phrase; ADJn = Positional operator that lets you retrieve records that contain your terms (in any order) within a specified number (n) of words of each other
Search terms Items found
Population: Pain
1. chronic pain/ 19 890
2. (chronic pain OR persistent pain OR noncancer pain OR non-cancer pain OR musculoskeletal pain or back pain).tw 104 540
3. ((chronicity or chronification) and pain*).tw,kw. 1 976
4. or/1-3 [population] 110 866
Intervention: Prediction
5. (identif* OR indicator* OR predict* OR probab* OR risk OR prognostic OR screening OR tool*).tw 8 163 514
6. Electronic Health Records/ 25 142
7. (health record* OR EHR OR Administrative data*).tw 44 058
8. exp Algorithms/ 394 179
9. algorithm*.tw 310 707
10. Clinical Decision Rules/ 868
11. (clinical adj (decision or prediction) adj rule*).tw,kw 2 084
12. 6 or 7 [EHR] 58 069
13. 8 or 9 [algorithm] 565 657
14. 10 or 11 [CPR] 2 833
15. 12 and 13 [EHR and algorithm] 6 252
16. 5 or 15 or 14 [identification] 8 165 157
Study types: systematic reviews and meta-analysis
7. Systematic Review.pt. OR Meta-Analysis.pt. OR Cochrane Database Syst Rev.ja. OR ((systematic adj3 review) OR "meta analys*" OR metaanalys*).ti,ab. 388 126
Combined sets
8. 4 and 16 [population and identification] 39 667
9. 18 and 17 3 505
Final result
10. 15 3 505

Scopus via Elsevier (citation search) 30 March 20

Title: Persistent pain
The final search result, usually found at the end of the documentation, forms the list of abstracts.

TITLE-ABS-KEY = Title or abstract or keywords; ALL = All fields; DOI = Digital Object Identifier; EID = Document identifier in Scopus; PMID = PubMed Identifier; PRE/n = "precedes by". The first term in the search must precede the second by a specified number of terms (n).; W/n = "Within". The terms in the search must be within a specified number of terms (n) in any order.; * = Truncation; " " = Citation Marks; searches for a phrase; ? = replaces one letter; LIMIT-TO (SRCTYPE, "j" = Limit to source type journal; LIMIT-TO (DOCTYPE, "ar" = Limit to document type article; LIMIT-TO (DOCTYPE, "re" = Limit to document type review
Search terms Items found
Cited articles
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"10.1370/afm.1625" OR "10.1016/j.pain.2009.11.002" OR "10.1097/00007632-200201010-00022" OR "10.2522/ptj.20100109" OR "10.1016/j.math.2013.01.002" OR"10.1016/j.math.2012.05.014" OR "10.1007/s00586-010-1509-4" OR "10.1111/j.1533-2500.2008.00176.x" OR "10.1097/00007632-199512150-00011" OR "10.1186/s12891-015-0509-2" OR "10.1097/00007632-199012000-00013" OR "10.2147/ijgm.s5703" OR "10.2147/jpr.s4949" OR "10.1016/j.ejpain.2006.03.004" OR "10.1097/01.brs.0000158972.34102.6f" OR "10.1016/j.pain.2010.09.014" OR "10.1097/01.ajp.0000208243.33498.cb" OR "10.1097/01.brs.0000214878.01709.0e" OR "10.1016/s0304-3959(00)00483-8" OR "10.1097/00007632-199512150-00012" OR "10.1016/j.ejpain.2008.03.007" OR "10.1097/00007632-200012010-00013" OR "10.1016/j.pain.2011.10.019" OR "10.1016/j.jclinepi.2015.02.003" OR "10.1097/00007632-199712150-00019" OR "10.1097/00007632-199604150-00008" OR "10.1007/s00586-009-1254-8" OR "10.1097/01.brs.0000256447.72623.56" OR "10.1136/bmj.a171" OR "10.1002/acr.21665" OR 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"10.1016/j.spinee.2013.09.036" OR "10.2519/jospt.1998.27.5.331" OR "10.1016/j.ejpain.2007.10.007" OR "10.1007/s10926-010-9238-4" OR "10.1016/S0003-9993(98)90135-6" OR "10.1093/ptj/78.6.613" OR "10.1093/ptj/78.6.624" OR "10.1186/s12998-016-0090-2" OR "10.1016/j.pain.2005.05.029" OR "10.1016/j.injury.2010.07.245" OR "10.1016/j.pain.2007.03.032" OR "10.3233/BMR-150609" ) 222
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