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Development and validation of a shared decision-making instrument for health-related quality of life one year after total hip replacement based on quality registries data

Artikel i vetenskaplig tidskrift
Författare Szilard Nemes
Ola Rolfson
Göran Garellick
Publicerad i Journal of Evaluation in Clinical Practice
Volym 24
Nummer/häfte 1
Sidor 13-21
ISSN 1356-1294
Publiceringsår 2018
Publicerad vid Institutionen för kliniska vetenskaper, Avdelningen för ortopedi
Sidor 13-21
Språk en
Länkar doi.org/10.1111/jep.12603
Ämnesord healthcare, health services research, medical informatics, public health, patient-reported outcomes, total knee replacement, total joint, arthroplasty, osteoarthritis, euroqol, impact, satisfaction, management, regression, improve
Ämneskategorier Ortopedi


Rationale, aims and objectivesClinicians considering improvements in health-related quality of life (HRQoL) after total hip replacement (THR) must account for multiple pieces of information. Evidence-based decisions are important to best assess the effect of THR on HRQoL. This work aims at constructing a shared decision-making tool that helps clinicians assessing the future benefits of THR by offering predictions of 1-year postoperative HRQoL of THR patients. MethodsWe used data from the Swedish Hip Arthroplasty Register. Data from 2008 were used as training set and data from 2009 to 2012 as validation set. We adopted two approaches. First, we assumed a continuous distribution for the EQ-5D index and modelled the postoperative EQ-5D index with regression models. Second, we modelled the five dimensions of the EQ-5D and weighted together the predictions using the UK Time Trade-Off value set. As predictors, we used preoperative EQ-5D dimensions and the EQ-5D index, EQ visual analogue scale, visual analogue scale pain, Charnley classification, age, gender, body mass index, American Society of Anesthesiologists, surgical approach and prosthesis type. Additionally, the tested algorithms were combined in a single predictive tool by stacking. ResultsBest predictive power was obtained by the multivariate adaptive regression splines (R-2=0.158). However, this was not significantly better than the predictive power of linear regressions (R-2=0.157). The stacked model had a predictive power of 17%. ConclusionsSuccessful implementation of a shared decision-making tool that can aid clinicians and patients in understanding expected improvement in HRQoL following THR would require higher predictive power than we achieved. For a shared decision-making tool to succeed, further variables, such as socioeconomics, need to be considered.

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