The usefulness of the STarT back screening tool and single-item general health measures when predicting future disability in patients with low back pain treated in Danish primary care physiotherapy.

INTRODUCTION: The extent to which disease specific screening tools or other health measures add to the predictive value of common clinical factors (pain, disability and socio-demographics) has been sparsely investigated. The aim of this study was to investigate whether a disease specific screening tool and a single-item general health measure adds predictive value to basic information collected in primary physiotherapy care when predicting future disability in patients with low back pain. MATERIAL AND METHODS: This longitudinal cohort study included 354 patients with low back pain from Danish primary care physiotherapy. Information was collected on socio-demographics, common clinical factors, The STarT Back Screening Tool (SBT) and general health perceptions measured as a single item from the SF-36 (GH-1). Disability at 6-month follow-up, measured by the Roland-Morris Disability Questionnaire, was predicted using multiple linear regression models. RESULTS: Clinical factors and baseline disability level explained 28.3% of the variance in 6-month disability scores. With SBT and GH-1 added separately to the baseline model, the explained variance increased by 2.1% (p = 0.01) and 3.6% (p < 0.001), respectively. CONCLUSION: The added value of the disease specific screening tools or the single-item general measure when predicting disability in patients with low back pain was generally small. Moreover, the predictive value of the single-item general measure seems comparable to and slightly better than the disease specific screening tool. Overall these findings may question the clinical utility of such measures.

Predicting change in symptoms and function in patients with persistent shoulder pain: a prognostic model development study.

BACKGROUND: Persistent shoulder pain causes considerable disruption of the individual's life and imposes high costs on healthcare and society. Well-informed treatment and referral pathways are crucial as unsuccessful interventions and longer duration of symptoms minimizes the likelihood of success in future interventions. Although physiotherapy is generally recommended as first line treatment, no prognostic model or clinical prediction rules exists to help guide the treatment of patients with persistent shoulder pain undergoing physiotherapy. Thus, the objective of this study was to develop a prognostic model to inform clinical decision making and predict change in symptoms and function in patients with persistent shoulder pain. METHODS: This was a prospective cohort study of 243 patients with persistent shoulder pain referred to outpatient physiotherapy rehabilitation centres. Data was collected at baseline and six-month follow-up. The outcome was change in shoulder symptoms and function as measured by the shortened version of the Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) from baseline to 6 months follow up. Potential predictors were included in a multivariable linear regression model which was pruned using modified stepwise backwards elimination. RESULTS: The final model consisted of seven predictors; baseline QuickDASH score, employment status, educational level, movement impairment classification, self-rated ability to cope with the pain, health-related quality of life and pain catastrophizing. Together these variables explained 33% of the variance in QuickDASH-change scores with a model root mean squared error of 17 points. CONCLUSION: The final prediction model explained 33% of the variance in QuickDASH change-scores at 6 months. The root mean squared error (model SD) was relatively large meaning that the prediction of individual change scores was quite imprecise. Thus, the clinical utility of the prediction model is limited in its current form. Further work needs be done in order to improve the performance and precision of the model before external validity can be examined along with the potential impact of the model in clinical practice. Two of the included predictors were novel and could be examined in future studies; movement impairment classification based on diagnosis and health-related quality of life.