IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0094268.html
   My bibliography  Save this article

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model

Author

Listed:
  • François Luthi
  • Olivier Deriaz
  • Philippe Vuistiner
  • Cyrille Burrus
  • Roger Hilfiker

Abstract

Background: Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker’s background. Methods: Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients’ data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. Results: At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. Conclusions: Non-RTW may be predicted with a simple model constructed with variables independent of the patient’s education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0094268
    DOI: 10.1371/journal.pone.0094268
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0094268
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0094268&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0094268?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. MacKenzie, E.J. & Shapiro, S. & Smith, R.T. & Siegel, J.H. & Moody, M. & Pitt, A., 1987. "Factors influencing return to work following hospitalization for traumatic injury," American Journal of Public Health, American Public Health Association, vol. 77(3), pages 329-334.
    3. 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.
    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.
    5. Franziska Matzer & Ursula V Wisiak & Monika Graninger & Wolfgang Söllner & Hans Peter Stilling & Monika Glawischnig-Goschnik & Andreas Lueger & Christian Fazekas, 2012. "Biopsychosocial Health Care Needs at the Emergency Room: Challenge of Complexity," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-7, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nathan N O’Hara & Marckenley Isaac & Gerard P Slobogean & Niek S Klazinga, 2020. "The socioeconomic impact of orthopaedic trauma: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.

    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. 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.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Nan Maxwell & Albert Liu & Nathan Wozny & Caroline Massad Francis, 2013. "Addressing Return-to-Work Issues in the Federal Employees' Compensation Act with Administrative Data," Mathematica Policy Research Reports 8e3f929cbea0422e8272f03ab, Mathematica Policy Research.
    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:pone00:0094268. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.