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Sökning: WFRF:(Hedlund Rune) > Uppsala universitet

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  • Grunnesjö, Marie (författare)
  • Low Back Pain : With Special Reference to Manual Therapy, Outcome and its Prognosis
  • 2011
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Objectives. To assess outcome of manual therapy in addition to stay-active care in sub-acute low back pain patients and to investigate the predictive power of pain drawing sketch variables for return to work. Materials and methods. The study was designed as a randomised controlled trial with a factorial design, and included 160 patients with acute or sub-acute low back pain allocated to one of the four treatment groups during 10 weeks. Group 1 received stay-active care only, Group 2 the same treatment as in Group 1 + muscle stretching, Group 3 the same treatment as in Group 2 plus manual therapy, and Group 4 the same treatment as Group 3 plus steroid injections. Outcome included pain intensity, pain extension, functional and health related quality of life variables and return to work. Results. Pain intensity and disability rating improved faster in Groups 3 and 4 than in Groups 1 and 2 (p<0.05 and p<0.05). Also health related quality of life was affected by the treatments given; the more treatment options the better the effect (trend across the groups p<0.05). Pain extension as described on a pain drawing sketch decreased in all groups across the study period. The pain modality ‘numbness’ was the most painful one among patients with no pain radiation. Pain radiation according to the pain drawing sketch was the strongest predictor for return to work (p=0.03, Wald χ2=4.56). Conclusions. The manual therapy concept used in this study reduced pain intensity and disability rating better than the stay active concept. The effects on health related quality of life were greater the larger the number of treatment modalities available. Pain drawing information was significantly correlated with pain and functional variables. Pain radiation according to the pain drawing adds significant information to the prediction of return to work.
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  • Iderberg, Hanna, et al. (författare)
  • Predicting clinical outcome and length of sick leave after surgery for lumbar spinal stenosis in Sweden : a multi-register evaluation
  • 2019
  • Ingår i: European spine journal. - : Springer Science and Business Media LLC. - 0940-6719 .- 1432-0932. ; 28:6, s. 1423-1432
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Lumbar spinal stenosis (LSS) can be surgically treated, with variable outcome. Studies have linked socioeconomic factors to outcome, but no nation-wide studies have been performed. This register-based study, including all patients surgically treated for LSS during 2008-2012 in Sweden, aimed to determine predictive factors for the outcome of surgery.Methods: Clinical and socioeconomic factors with impact on outcome in LSS surgery were identified in several high-coverage registers, e.g., the national quality registry for spine surgery (Swespine, FU-rate 70-90%). Multivariate regression analyses were conducted to assess their effect on outcome. Two patient-reported outcome measures, Global Assessment of leg pain (GA) and the Oswestry Disability Index (ODI), as well as length of sick leave after surgery were analyzed.Results: Clinical and socioeconomic factors significantly affected health outcome (both GA and ODI). Some predictors of a good outcome (ODI) were: being born in the EU, reporting no back pain at baseline, a high disposable income and a high educational level. Some factors predicting a worse outcome were previous surgery, having had back pain more than 2years, having comorbidities, being a smoker, being on social welfare and being unemployed.Conclusions: The study highlights the relevance of adding socioeconomic factors to clinical factors for analysis of patient-reported outcomes, although the causal pathway of most predictors' impact is unknown. These findings should be further investigated in the perspective of treatment selection for individual LSS patients. The study also presents a foundation of case mix algorithms for predicting outcome of surgery for LSS.
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