Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen decision tool for chronic low back pain

Miranda L. van Hooff, Johanna M. van Dongen, Veerle M. Coupé, Maarten Spruit, Raymond W.J.G. Ostelo, Marinus de Kleuver

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Introduction Chronic Low Back Pain (CLBP) is a heterogeneous condition with lack of diagnostic clarity. Therapeutic interventions show small effects. To improve outcomes by targeting interventions it is recommended to develop a triage system to surgical and non-surgical treatments based on treatment outcomes. The objective of the current study was to develop and internally validate prognostic models based on pre-treatment patient-reported profiles that identify patients who either respond or do not respond to two frequently performed treatments (lumbar spine surgery and multidisciplinary pain management program). Methods A consecutive cohort study in a secondary referral spine center was performed. The study followed the recommendations of the PROGRESS framework and was registered in the Dutch Trial Register (NTR5946). Data of forty-seven potential pre-consultation (baseline) indicators predicting ‘response’ or ‘non-response’ at one-year follow-up for the two treatments were obtained to develop and validate four multivariable logistic regression models. The source population consisted of 3,410 referred CLBP-patients. Two treatment cohorts were defined: elective ‘spine surgery’ (n = 217 [6.4%]) and multidisciplinary bio-psychosocial ‘pain management program’ (n = 171 [5.0%]). Main inclusion criteria were age 18, CLBP (6 months), and not responding to primary care treatment. The primary outcome was functional ability: ‘response’ (Oswestry Disability Index [ODI] 22) and ‘non-response’ (ODI 41). Results Baseline indicators predictive of treatment outcome were: degree of disability (all models), 2 previous spine surgeries, psychosocial complaints, age (onset <20 or >50), and patient expectations of treatment outcomes. The explained variances were low for the models predicting response and non-response to pain management program (R2 respectively 23% and 26%) and modest for surgery (R2 30% and 39%). The overall performance was acceptable (c-index; 0.72–0.83), the model predicting non-response to surgery performed best (R2 = 39%; c-index = 0.83). Conclusion This study was the first to identify different patient-reported profiles that predict response to different treatments for CLBP. The model predicting ‘non-response’ to elective lumbar spine surgery performed remarkably well, suggesting that referrals of these patients to a spine surgeon could be avoided. After external validation, the patient-reported profiles could potentially enhance timely patient triage to the right secondary care specialist and improve decision-making between clinican and patient. This could lead to improved treatment outcomes, which results in a more efficient use of healthcare resources.

Original languageEnglish
Article numbere0203518
Issue number9
Publication statusPublished - 19 Sept 2018

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