Swedish Agency for Health Technology Assessment and Assessment of Social Services
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.
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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
| 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. |
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| 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.” |
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| 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 |
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| 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.” |
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| Haskins et al, 2015 [3] Validation and impact analysis of prognostic clinical prediction rules for low back pain is needed: a systematic review |
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35 studies included
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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.” |
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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.
Literature search
Medline via OvidSP 16 May 2022
| 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 |
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| 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
| 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 |
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| Search terms | Items found | |
| Cited articles | ||
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"10.1097/01.brs.0000172159.47152.dc" OR "10.1097/BRS.0b013e3180b9f5f7" OR "10.1016/j.pain.2004.02.017" OR "10.1016/j.pain.2005.11.004" OR "10.1093/pm/pnaa004" OR "10.3109/03009749509095151" OR "10.1002/j.1532-2149.2012.00117.x" OR "10.2340/16501977-1885" OR "10.1007/s10926-012-9404-y" OR "10.1016/j.jmpt.2004.08.001" OR "10.5271/sjweh.764" OR "10.1016/j.rehab.2018.09.007" OR "10.1097/00002508-200303000-00002" OR "10.1097/00002508-199809000-00007" OR "10.1097/BRS.0b013e3181f8f775" OR "10.1097/00007632-200204150-00013" OR "10.1016/s0304-3959(98)00209-7" OR "10.1016/0022-3999(83)90040-5" OR "10.1002/ejp.1782" OR "10.1097/JOM.0b013e31802db51e" OR "10.1002/ejp.615" OR "10.1016/j.spinee.2015.03.003" OR "10.1007/s00264-008-0707-8" OR "10.3233/WOR-131672" OR "10.1080/08035259950168199" OR "10.1007/s00586-013-2690-z" OR "10.1007/s00586-013-2861-y" OR "10.1016/j.ejpain.2005.08.002" OR "10.1016/j.math.2014.08.001" OR "10.1016/j.ejpain.2005.12.008" OR "10.1097/PR9.0000000000000919" 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 |
| Citing articles | ||
| #1 View cited by | 23 035 | |
| Search within results: systematic reviews and meta-analysis | ||
| TITLE-ABS-KEY((systematic W/2 review) OR “meta analys*” OR metaanalys*) OR (SRCTITLE(cochrane) AND DOCTYPE(re)) | 824 | |
| #3 | 824 | |
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