Digital Public Defence: Birgitte Lawaetz Myhrvold
MSc Birgitte Lawaetz Myhrvold at Institute of Health and Society will be defending the thesis Prognostic models for neck pain for the degree of PhD (Philosophiae Doctor).
The public defence will be held as a video conference over Zoom.
The defence will follow regular procedure as far as possible, hence it will be open to the public and the audience can ask ex auditorio questions when invited to do so.
Due to copyright reasons, an electronic copy of the thesis must be ordered from the faculty. In order for the faculty to have time to process the order, it must be received by the faculty no later than 2 days prior to the public defence. Orders received later than 2 days before the defence will not be processed. Inquiries regarding the thesis after the public defence must be addressed to the candidate.
Digital Trial Lecture – time and place
- First opponent: Professor Kate Dunn, Keele University
- Second opponent: Senior Researcher Cecilia Bergström, Umeå universitet
- Third member and chair of the evaluation committee: Professor II John Anker Zwart, University of Oslo
Chair of the Defence
Associate Professor Line Kildal Bragstad, University of Oslo
Professor Nina K. Vøllestad, University of Oslo
Neck pain is common and causes pain and disability that interfere with daily activities and quality of life. People with neck pain commonly seek chiropractic care. Being able to early identify those at risk for long-term pain at an early stage, is important for optimal management.
Many prognostic models have been developed for the purpose of predicting individual prognosis, but few studies have examined the validity of these models in external data. Thus, no model is currently recommended to predict neck pain outcome in clinical practice. One existing prognostic model developed in 2010 seems promising but it includes many predictors and therefore may be challenging for use in clinical practice.
Our aims were to externally validate the existing prognostic model, to update the original model within current knowledge and to test the updated model’s predictive capacity across relevant outcome measures. Previous pain history and expectations for the future are two important prognostic factors for prognosis. In this thesis, to obtain information about both the pain history and expectations for the future, we used a novel method that captures that neck pains are episodic and fluctuating (Previous and Expected visual trajectory patterns). Thus, an additionally aim was to describe patient characteristics of the distinct Visual trajectory patterns.
We conducted a prospective observational study, recruiting patients with neck pain from chiropractic practice with follow-up at 12 weeks. In total, 1313 eligible patients from 18 years and up with neck pain were included. Data were collected using self-reported questionnaires measuring a range of potential prognostic factors. Outcome measures were global perceived effect, disability, Health-Related Quality of Life and pain intensity.
The existing prognostic model developed for neck pain patients did not predict outcome accurately in our sample. By updating the model with other factors led to increased predictive capacity and reduced the number of predictors. The updated model includes the novel Previous and Expected visual trajectory patterns, Radiating pain to shoulder and/or elbow, Number of musculoskeletal pain-sites, Education level, Physical leisure activity and Consultation-type. The updated model was predictive across four outcomes although the predictive capacity differed. Persistently disability as outcome had the highest explained variance, while pain intensity was the outcome with the lowest. Of all predictors included in the updated model, the Previous and Expected visual trajectory patterns were individually the predictors with largest effect across all outcomes. We found that patients reporting a more severe visual pattern (i.e., less pain free periods) was associated with worse pain and disability, higher psychosocial distress, and worse prognosis. To conclude, the updated model is less complex with clearly better predictive capacity compared to the existing model, thus easier to use in clinical practice.
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