Prediction of Treatment Outcomes for Patients with Chronic Low Back Pain: the Development of a Clinical Decision Guideline for Spine Surgeons
Authors: Van Hooff Miranda , Van Dongen Hanneke , Spruit Maarten , Ostelo Raymond , De Kleuver Marinus 
Published: April 2016

Introduction Low back pain (LBP) is responsible for the greatest burden of all diseases.1 Chronic Low Back Pain (CLBP) is among the most common reasons why patients visit a spine surgeon. As the CLBP population is heterogeneous, it remains a challenge to address etiology and to suggest treatment options. Evidence2–4 recommends developing a decision tool to triage toward either surgical or nonsurgical interventions. The Nijmegen decision tool, consisting of a web-based questionnaire, a systematic follow up of outcomes built in the patient-based system of the SweSpine Registry,5 is developed to support patient-triage and is based on evidence and professional (Delphi) consensus. Since April2012 all new patients complete the questionnaire, consisting of 47 indicators potentially predicting successful treatment outcome or persistence of pain complaints, and are systematically followed over time.5 In this study pre-intervention patient profiles have been determined and a decision algorithm has been developed, based on indicators predicting successful one-year follow-up outcome of spine surgery and of a non-surgical, multidisciplinary Combined Physical and Psychological (CPP) program. Material and Methods A consecutive cohort study was performed. Diagnostics and decision-making were performed ‘as usual’. Data of patients were included who completed the one-year follow-up assessment: 219 had surgery and 171 followed a multidisciplinary CPP-program. The outcome was functional status (Oswestry Disability Index v2.1a [ODI]) and being successful was defined as one-year improvement to an absolute (‘normal’/healthy) ODI-threshold (value≤22). The 47 potential predictive indicators included indicators in the sociodemographic, pain-related, somatic, psychological, functional and quality of life domains.5 After data-cleaning and having fulfilled all assumptions for analyses, for each cohort a separate multiple logistic regression analysis was performed. Results Probability of successful surgical outcome: the prediction model (R2=31%) included pre-treatment previous surgery(OR 0.390 [95%CI 0.201–0.757]), expectations of recovery (OR 2.830 [95%CI 1.391–5.756]), expectation of work return (OR 0.824 [95%CI 0.706–0.960]), pre-treatment functioning (OR 0.961 [95%CI 0.939–0.984]), and ‘red flag’ for age (pain started age < 20 or  > 50 years; OR 2.321 [95%CI 1.214–4.435]). Probability of successful CPP-program outcome: the prediction model (R2=26%) included pre-treatment functioning (OR 0.963 [95%CI 0.937–0.990]), catastrophizing (OR 0.199 [95%CI 0.065–0.610]), and depressed mood (OR 0.205 [95%CI 0.063–0.665]). Interpretation Odds Ratio (OR): OR > 1=increased probability to success; OR < 1=reduced probability to success) Conclusion This is the first clinical decision tool available, created with a rigorous scientific basis that is designed for patient-triage. Different patient profiles predicting either a successful outcome for spine surgery or for CPP program are determined. The presented profiles are based on first analyses, after final analyses the two prediction models will be converted to probability formulae and built in the online tool. For every new patient the probabilities for treatment success will be available in the electronic patient file. The next step is to validate the tool in different hospitals. We expect that this relatively simple tool, based on validated questionnaires and current evidence, will considerably help in daily spine practice to guide the right patients to the right practitioners and to enhance personalized care. We will present the final prediction models at the Global Spine Congress. References Vos T; Global Burden of Disease Study 2013 Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;386(9995):743–800 Chou R, Loeser JD, Owens DK, et al; American Pain Society Low Back Pain Guideline Panel. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine 2009;34(10):1066–1077 National Institute for Health and Clinical Excellence. Low Back Pain: Early management of persistent non-specific low back pain. National Collaborating Centre for Primary Care. London, UK: NICE Clinical Guideline; 2009:88 Fairbank J, Gwilym SE, France JC, et al. The role of classification of chronic low back pain. Spine 2011;36(21, Suppl):S19–S42 Van Hooff ML, Van Loon J, Van Limbeek J, De Kleuver M. The Nijmegen Decision Tool for CLBP. Development of a clinical decision tool to select patients for secondary care. PLoS ONE 2014;9(8):104226