Prediction of persistent shoulder pain in general practice: Comparing clinical consensus from a Delphi procedure with a statistical scoring system

<p>Abstract</p> <p>Background</p> <p>In prognostic research, prediction rules are generally statistically derived. However the composition and performance of these statistical models may strongly depend on the characteristics of the derivation sample. The purpose of this study was to establish consensus among clinicians and experts on key predictors for persistent shoulder pain three months after initial consultation in primary care and assess the predictive performance of a model based on clinical expertise compared to a statistically derived model.</p> <p>Methods</p> <p>A Delphi poll involving 3 rounds of data collection was used to reach consensus among health care professionals involved in the assessment and management of shoulder pain.</p> <p>Results</p> <p>Predictors selected by the expert panel were: symptom duration, pain catastrophizing, symptom history, fear-avoidance beliefs, coexisting neck pain, severity of shoulder disability, multisite pain, age, shoulder pain intensity and illness perceptions. When tested in a sample of 587 primary care patients consulting with shoulder pain the predictive performance of the two prognostic models based on clinical expertise were lower compared to that of a statistically derived model (Area Under the Curve, AUC, expert-based dichotomous predictors 0.656, expert-based continuous predictors 0.679 vs. 0.702 statistical model).</p> <p>Conclusions</p> <p>The three models were different in terms of composition, but all confirmed the prognostic importance of symptom duration, baseline level of shoulder disability and multisite pain. External validation in other populations of shoulder pain patients should confirm whether statistically derived models indeed perform better compared to models based on clinical expertise.</p

A prediction rule for shoulder pain related sick leave: a prospective cohort study

BACKGROUND: Shoulder pain is common in primary care, and has an unfavourable outcome in many patients. Information about predictors of shoulder pain related sick leave in workers is scarce and inconsistent. The objective was to develop a clinical prediction rule for calculating the risk of shoulder pain related sick leave for individual workers, during the 6 months following first consultation in general practice. METHODS: A prospective cohort study with 6 months follow-up was conducted among 350 workers with a new episode of shoulder pain. Potential predictors included the results of a physical examination, sociodemographic variables, disease characteristics (duration of symptoms, sick leave in the 2 months prior to consultation, pain intensity, disability, comorbidity), physical activity, physical work load, psychological factors, and the psychosocial work environment. The main outcome measure was sick leave during 6 months following first consultation in general practice. RESULTS: Response rate to the follow-up questionnaire at 6 months was 85%. During the 6 months after first consultation 30% (89/298) of the workers reported sick leave. 16% (47) reported 10 days sick leave or more. Sick leave during this period was predicted in a multivariable model by a longer duration of sick leave prior to consultation, more shoulder pain, a perceived cause of strain or overuse during regular activities, and co-existing psychological complaints. The discriminative ability of the prediction model was satisfactory with an area under the curve of 0.70 (95% CI 0.64–0.76). CONCLUSION: Although 30% of all workers with shoulder pain reported sick leave during follow-up, the duration of sick leave was limited to a few days in most workers. We developed a prediction rule and a score chart that can be used by general practitioners and occupational health care providers to calculate the absolute risk of sick leave in individual workers with shoulder pain, which may help to identify workers who need additional attention. The performance and applicability of our model needs to be tested in other working populations with shoulder pain to enable valid and reliable use of the score chart in everyday practice

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

Health care provider's attitudes and beliefs towards chronic low back pain: the development of a questionnaire

Attitudes and beliefs, or the treatment orientation, of health care providers appear to be important in the management of non-specific chronic low back pain (CLBP). The aims of the current study were two-fold: First of all, the physiotherapists' opinion towards various aspects of the management of CLBP was surveyed. Secondly, in a principal factor analysis, it was investigated whether underlying dimensions could be identified in order to develop the Pain Attitudes and Beliefs Scale for Physiotherapists (PABS_PT). In total, 421 physiotherapists (response rate 62.3%) participated in this study. The results suggested that the majority of physiotherapists hold the opinion that CLBP is not a dangerous condition, that sport should not be discouraged and that patients should not refrain from all physical activity. Moreover physiotherapists seem to hold the opinion that the way patients view their pain influences the progress of symptoms. Finally, physiotherapists seem to hold the opinion that therapy can completely alleviate the functional symptoms and that therapy may have been successful even if pain remains. The principal factor analysis (PAF) yielded an interpretable 2-factor model. Based on highest loading items, factor 1 was labelled 'biomedical orientation', whereas factor 2 was labelled 'behavioural orientation'. The internal consistency (Cronbach's Alpha) of factor 1 was 0.84 and for factor 2, 0.54 explaining 25.2% and 8.2%, respectively, of the total variance. Assessment of the effect of the physiotherapists' characteristics on scores on the different scales was encouraging as results pointed in the directions one would expect. Physiotherapists who attended biopsychosocial education courses had statistically significantly higher scores on the 'behavioural orientation' factor and vice versa. Biomedical specialists scored statistically significantly higher on the 'biomedical orientation' factor. Furthermore, the findings suggested that the PABS_PT discriminates between physiotherapists with a 'behavioural orientation' vs those with a 'biomedical orientation'. To examine the influence of these different treatment orientations with regard to CLBP on patient outcome is a challenge for the near future