SpineData – A Danish clinical registry of people with chronic back pain

Background: Large-scale clinical registries are increasingly recognized as important resources for quality assurance and research to inform clinical decision-making and health policy. We established a clinical registry (SpineData) in a conservative care setting where more than 10,000 new cases of spinal pain are assessed each year. This paper describes the SpineData registry, summarizes the characteristics of its clinical population and data, and signals the availability of these data as a resource for collaborative research projects. Methods: The SpineData registry is an Internet-based system that captures patient data electronically at the point of clinical contact. The setting is the government-funded Medical Department of the Spine Centre of Southern Denmark, Hospital Lillebaelt, where patients receive a multidisciplinary assessment of their chronic spinal pain. Results: Started in 2011, the database by early 2015 contained information on more than 36,300 baseline episodes of patient care, plus the available 6-month and 12-month follow-up data for these episodes. The baseline questionnaire completion rate has been 93%; 79% of people were presenting with low back pain as their main complaint, 6% with mid-back pain, and 15% with neck pain. Collectively, across the body regions and measurement time points, there are approximately 1,980 patient-related variables in the database across a broad range of biopsychosocial factors. To date, 36 research projects have used data from the SpineData registry, including collaborations with researchers from Denmark, Australia, the United Kingdom, and Brazil. Conclusion: We described the aims, development, structure, and content of the SpineData registry, and what is known about any attrition bias and cluster effects in the data. For epidemiology research, these data can be linked, at an individual patient level, to the Danish population-based registries and the national spinal surgery registry. SpineData also has potential for the conduct of cohort multiple randomized controlled trials. Collaborations with other researchers are welcome

Effectiveness of stratified treatment for back pain in Danish primary care: A randomized controlled trial

Background A randomized controlled trial (RCT) of stratified care demonstrated superior clinical outcomes and cost-effectiveness for low back pain (LBP) patients in UK primary care. This is the first study in Europe, outside of the original UK study, to investigate the clinical efficacy and cost-effectiveness of stratified care compared with current practice for patients with non-specific LBP. Methods The study was a two-armed RCT. Danish primary care patients with LBP were randomized to stratified care (n = 169) or current practice (n = 164). Primary outcomes at 3- and 12-months' follow-up were Roland Morris Disability Questionnaire (RDMQ), patient-reported global change and time off work. Secondary outcomes included pain intensity, patient satisfaction, healthcare resource utilization and quality-adjusted life years. Results Intention-to-treat analyses found no between-group difference in RMDQ scores at 3 months (0.5, 95% CI −1.8 to 0.9) or 12 months (0.4, −2.1 to 1.3). No overall differences were found between the arms at 3 and 12 months with respect to time off work or secondary outcomes. Stratified care intervention resulted in significantly fewer treatment sessions (3.5 [SD 3.1] vs. 4.5 [3.5]) and significantly lower total healthcare costs (€) (13.4 [529] vs. 228 [830], p = .002). There was no difference in cost-effectiveness (0.09, 0.05 to 0.13 vs. 0.10, 0.07–0.14, p = .70). Conclusions There was no significant difference in clinical outcomes between patients with non-specific LBP receiving stratified care and those receiving current practice. However, stratified care may reduce total healthcare costs if implemented in Danish primary care. Significance Stratified care for low back pain based on risk profile is recommended by recent evidence based clinical guidelines. This study is the first broad replication of the STarT Back Trial in Europe. Therefore, the study adds to the body of knowledge evaluating the effectiveness of stratified care for low back pain in primary care, and provides insight into the effects of stratification on clinical practice

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

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

A prediction model to identify hospitalised, older adults with reduced physical performance

Abstract Background Identifying older adults with reduced physical performance at the time of hospital admission can significantly affect patient management and trajectory. For example, such patients could receive targeted hospital interventions such as routine mobilisation. Furthermore, at the time of discharge, health systems could offer these patients additional therapy to maintain or improve health and prevent institutionalisation or readmission. The principle aim of this study was to identify predictors for persisting, reduced physical performance in older adults following acute hospitalisation. Methods This was a prospective cohort study that enrolled 117 medical patients, ages 65 or older, who were admitted to a short-stay unit in a Danish emergency department. Patients were included in the study if at the time of admission they performed ≤8 repetitions in the 30-s Chair-Stand Test (30s–CST). The primary outcome measure was the number of 30s–CST repetitions (≤ 8 or >8) performed at the time of follow-up, 34 days after admission. Potential predictors within the first 48 h of admission included: age, gender, ability to climb stairs and walk 400 m, difficulties with activities of daily living before admission, falls, physical activity level, self-rated health, use of a walking aid before admission, number of prescribed medications, 30s–CST, and the De Morton Mobility Index. Results A total of 78 (67%) patients improved in physical performance in the interval between admission and follow-up assessment, but 76 patients (65%) had persistent reduced physical performance when compared to their baseline (30s–CST ≤ 8). The number of potential predictors was reduced in order to create a simplified prediction model based on 4 variables, namely the use of a walking aid before hospitalisation (score = 1.5), a 30s–CST ≤ 5 (1.8), age > 85 (0.1), and female gender (0.6). A score > 1.8 identified 78% of the older adults who continued to have reduced physical performance following acute hospitalisation. Conclusion At the time of admission, the variables of age, gender, walking aid use, and a 30s–CST score ≤ 5 enabled clinicians to identify 78% of older adults who had persisting reduced physical performance following acute hospitalisation. Trial registration ClinicalTrials.gov Identifier: NCT02474277 . (12.10.2014)