IDEAS home Printed from https://ideas.repec.org/a/plo/pmed00/1002019.html
   My bibliography  Save this article

Estimating the Risk of Chronic Pain: Development and Validation of a Prognostic Model (PICKUP) for Patients with Acute Low Back Pain

Author

Listed:
  • Adrian C Traeger
  • Nicholas Henschke
  • Markus Hübscher
  • Christopher M Williams
  • Steven J Kamper
  • Christopher G Maher
  • G Lorimer Moseley
  • James H McAuley

Abstract

Background: Low back pain (LBP) is a major health problem. Globally it is responsible for the most years lived with disability. The most problematic type of LBP is chronic LBP (pain lasting longer than 3 mo); it has a poor prognosis and is costly, and interventions are only moderately effective. Targeting interventions according to risk profile is a promising approach to prevent the onset of chronic LBP. Developing accurate prognostic models is the first step. No validated prognostic models are available to accurately predict the onset of chronic LBP. The primary aim of this study was to develop and validate a prognostic model to estimate the risk of chronic LBP. Methods and Findings: We used the PROGRESS framework to specify a priori methods, which we published in a study protocol. Data from 2,758 patients with acute LBP attending primary care in Australia between 5 November 2003 and 15 July 2005 (development sample, n = 1,230) and between 10 November 2009 and 5 February 2013 (external validation sample, n = 1,528) were used to develop and externally validate the model. The primary outcome was chronic LBP (ongoing pain at 3 mo). In all, 30% of the development sample and 19% of the external validation sample developed chronic LBP. In the external validation sample, the primary model (PICKUP) discriminated between those who did and did not develop chronic LBP with acceptable performance (area under the receiver operating characteristic curve 0.66 [95% CI 0.63 to 0.69]). Although model calibration was also acceptable in the external validation sample (intercept = −0.55, slope = 0.89), some miscalibration was observed for high-risk groups. The decision curve analysis estimated that, if decisions to recommend further intervention were based on risk scores, screening could lead to a net reduction of 40 unnecessary interventions for every 100 patients presenting to primary care compared to a “treat all” approach. Limitations of the method include the model being restricted to using prognostic factors measured in existing studies and using stepwise methods to specify the model. Limitations of the model include modest discrimination performance. The model also requires recalibration for local settings. Conclusions: Based on its performance in these cohorts, this five-item prognostic model for patients with acute LBP may be a useful tool for estimating risk of chronic LBP. Further validation is required to determine whether screening with this model leads to a net reduction in unnecessary interventions provided to low-risk patients. Adrian Traeger and colleagues report the development and validation of a prognostiv model (PICKUP) for estimating risk of developing chronic low back pain.Why Was This Study Done?: What Did the Researchers Do and Find?: What Do These Findings Mean?:

Suggested Citation

  • Adrian C Traeger & Nicholas Henschke & Markus Hübscher & Christopher M Williams & Steven J Kamper & Christopher G Maher & G Lorimer Moseley & James H McAuley, 2016. "Estimating the Risk of Chronic Pain: Development and Validation of a Prognostic Model (PICKUP) for Patients with Acute Low Back Pain," PLOS Medicine, Public Library of Science, vol. 13(5), pages 1-21, May.
  • Handle: RePEc:plo:pmed00:1002019
    DOI: 10.1371/journal.pmed.1002019
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002019
    Download Restriction: no

    File URL: https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1002019&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pmed.1002019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ewout W Steyerberg & Karel G M Moons & Danielle A van der Windt & Jill A Hayden & Pablo Perel & Sara Schroter & Richard D Riley & Harry Hemingway & Douglas G Altman & for the PROGRESS Group, 2013. "Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research," PLOS Medicine, Public Library of Science, vol. 10(2), pages 1-9, February.
    2. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    3. Ewout W. Steyerberg & Andrew J. Vickers, 2008. "Decision Curve Analysis: A Discussion," Medical Decision Making, , vol. 28(1), pages 146-149, January.
    4. Richard D Riley & Jill A Hayden & Ewout W Steyerberg & Karel G M Moons & Keith Abrams & Panayiotis A Kyzas & Núria Malats & Andrew Briggs & Sara Schroter & Douglas G Altman & Harry Hemingway & for the, 2013. "Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research," PLOS Medicine, Public Library of Science, vol. 10(2), pages 1-9, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. François Luthi & Olivier Deriaz & Philippe Vuistiner & Cyrille Burrus & Roger Hilfiker, 2014. "Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-11, April.
    2. Nath Adulkasem & Phichayut Phinyo & Jiraporn Khorana & Dumnoensun Pruksakorn & Theerachai Apivatthakakul, 2021. "Development of Clinical Prediction Rules for One-Year Postoperative Functional Outcome in Patients with Intertrochanteric Fractures: The Intertrochanteric Fracture Ambulatory Prediction (IT-AP) Tool," IJERPH, MDPI, vol. 19(1), pages 1-16, December.
    3. Julius Sim & Lucy Teece & Martin S Dennis & Christine Roffe & SOࠢS Study Team, 2016. "Validation and Recalibration of Two Multivariable Prognostic Models for Survival and Independence in Acute Stroke," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-17, May.
    4. Mario Dioguardi & Francesca Spirito & Diego Sovereto & Mario Alovisi & Giuseppe Troiano & Riccardo Aiuto & Daniele Garcovich & Vito Crincoli & Luigi Laino & Angela Pia Cazzolla & Giorgia Apollonia Cal, 2022. "MicroRNA-21 Expression as a Prognostic Biomarker in Oral Cancer: Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 19(6), pages 1-12, March.
    5. Catherine Beauregard-Paultre & Claire Nour Abou Chakra & Allison McGeer & Annie-Claude Labbé & Andrew E Simor & Wayne Gold & Matthew P Muller & Jeff Powis & Kevin Katz & Suzanne M Cadarette & Jacques , 2019. "External validation of clinical prediction rules for complications and mortality following Clostridioides difficile infection," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    6. Chang Wook Jeong & Sangchul Lee & Jin-Woo Jung & Byung Ki Lee & Seong Jin Jeong & Sung Kyu Hong & Seok-Soo Byun & Sang Eun Lee, 2014. "Mobile Application-Based Seoul National University Prostate Cancer Risk Calculator: Development, Validation, and Comparative Analysis with Two Western Risk Calculators in Korean Men," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-7, April.
    7. Stephana J Cherak & Andrea Soo & Kyla N Brown & E Wesley Ely & Henry T Stelfox & Kirsten M Fiest, 2020. "Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
    8. Lonneke van Hoeven & Yvonne Vergouwe & P D M de Buck & Jolanda J Luime & Johanna M W Hazes & Angelique E A M Weel, 2015. "External Validation of a Referral Rule for Axial Spondyloarthritis in Primary Care Patients with Chronic Low Back Pain," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-12, July.
    9. Daan Nieboer & Tjeerd van der Ploeg & Ewout W Steyerberg, 2016. "Assessing Discriminative Performance at External Validation of Clinical Prediction Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-10, February.
    10. Ja Hyeon Ku & Myong Kim & Seok-Soo Byun & Hyeon Jeong & Cheol Kwak & Hyeon Hoe Kim & Sang Eun Lee, 2015. "External Validation of Models for Prediction of Lymph Node Metastasis in Urothelial Carcinoma of the Bladder," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    11. Alwin Schierenberg & Margaretha C Minnaard & Rogier M Hopstaken & Alma C van de Pol & Berna D L Broekhuizen & Niek J de Wit & Johannes B Reitsma & Saskia F van Vugt & Aleida W Graffelman & Hasse Melby, 2016. "External Validation of Prediction Models for Pneumonia in Primary Care Patients with Lower Respiratory Tract Infection: An Individual Patient Data Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-16, February.
    12. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    13. Yiwang Zhou & Peter X.K. Song & Haoda Fu, 2021. "Net benefit index: Assessing the influence of a biomarker for individualized treatment rules," Biometrics, The International Biometric Society, vol. 77(4), pages 1254-1264, December.
    14. Konstantina Chalkou & Andrew J. Vickers & Fabio Pellegrini & Andrea Manca & Georgia Salanti, 2023. "Decision Curve Analysis for Personalized Treatment Choice between Multiple Options," Medical Decision Making, , vol. 43(3), pages 337-349, April.
    15. Dexin Chen & Meiting Fu & Liangjie Chi & Liyan Lin & Jiaxin Cheng & Weisong Xue & Chenyan Long & Wei Jiang & Xiaoyu Dong & Jian Sui & Dajia Lin & Jianping Lu & Shuangmu Zhuo & Side Liu & Guoxin Li & G, 2022. "Prognostic and predictive value of a pathomics signature in gastric cancer," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    16. Jing Sun & Yue Liu & Jianhui Zhao & Bin Lu & Siyun Zhou & Wei Lu & Jingsun Wei & Yeting Hu & Xiangxing Kong & Junshun Gao & Hong Guan & Junli Gao & Qian Xiao & Xue Li, 2024. "Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    17. Anirudh Tomer & Daan Nieboer & Monique J. Roobol & Ewout W. Steyerberg & Dimitris Rizopoulos, 2019. "Personalized schedules for surveillance of low‐risk prostate cancer patients," Biometrics, The International Biometric Society, vol. 75(1), pages 153-162, March.
    18. Bernd Lütkenhöner & Türker Basel, 2013. "Predictive Modeling for Diagnostic Tests with High Specificity, but Low Sensitivity: A Study of the Glycerol Test in Patients with Suspected Menière’s Disease," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    19. Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    20. Minta Thomas & Yu-Ru Su & Elisabeth A. Rosenthal & Lori C. Sakoda & Stephanie L. Schmit & Maria N. Timofeeva & Zhishan Chen & Ceres Fernandez-Rozadilla & Philip J. Law & Neil Murphy & Robert Carreras-, 2023. "Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pmed00:1002019. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosmedicine (email available below). General contact details of provider: https://journals.plos.org/plosmedicine/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.