Abstract
BackgroundLow back pain is a common and disabling condition leading to large health service and societal costs. Although there are several treatment options for back pain little is known about how to improve patient choice in treatment selection. The purpose of this study was to pilot a decision support package to help people choose between low back pain treatments.MethodsThis was a single-centred pilot cluster randomised controlled trial conducted in a community physiotherapy service. We included adults with non-specific low back pain referred for physiotherapy. Intervention participants were sent an information booklet prior to their first consultation. Intervention physiotherapists were trained to enhance their skills in shared informed decision making. Those in the control arm received care as usual. The primary outcome was satisfaction with the treatment received at four months using a five-point Likert Scale dichotomised into “satisfaction” (very satisfied or somewhat satisfied) and “non-satisfaction” (neither satisfied nor dissatisfied, somewhat dissatisfied or very dissatisfied).ResultsWe recruited 148 participants. In the control arm 67% of participants were satisfied with their treatment and in the intervention arm 53%. The adjusted relative risk of being satisfied was 1.28 (95% confidence interval 0.79 to 2.09). For most secondary outcomes the trend was towards worse outcomes in the intervention group. For one measure; the Roland Morris Disability Questionnaire, this difference was clinically important (2.27, 95% confidence interval 0.08 to 4.47). Mean healthcare costs were slightly lower (£38 saving per patient) within the intervention arm but health outcomes were also less favourable (0.02 fewer QALYs); the estimated probability that the intervention would be cost-effective at an incremental threshold of £20,000 per QALY was 16%.ConclusionWe did not find that this decision support package improved satisfaction with treatment; it may have had a substantial negative effect on clinical outcome, and is very unlikely to prove cost-effective. That a decision support package might have a clinically important detrimental effect is of concern. To our knowledge this has not been observed previously. Decision support packages should be formally tested for clinical and cost-effectiveness, and safety before implementation.Trial registrationCurrent Controlled Trials ISRCTN46035546 registered on 11/02/10.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2474-15-282) contains supplementary material, which is available to authorized users.
Highlights
Low back pain is a common and disabling condition leading to large health service and societal costs
Randomised controlled trials investigating the effectiveness of patient decision aids have shown positive effects on patient satisfaction with the decision-making process, enhanced knowledge acquisition and less decisional conflict or anxiety when making their decision on treatment preference [7,8,9,10]
The point estimate of the dis-benefit from the decision support package at 2.3 points is larger than the effect size found in the definitive trials supporting the use of the back pain treatments included in the decision support package
Summary
Low back pain is a common and disabling condition leading to large health service and societal costs. The purpose of this study was to pilot a decision support package to help people choose between low back pain treatments. To encourage and implement informed shared decision making, decision aids for patients have been developed to support treatment choice in many areas including breast cancer [3,4], hypertension [5] and hormonal replacement therapy [6]. These patient decision aids provide information, evidence and guidance to help patients make decisions where there is some choice and where the decision may be influenced by the patient’s values. The few trials that report cost outcomes provide insufficient evidence to conclude that these interventions are cost saving; none report a cost-effectiveness analysis [13]
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