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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Musculoskeletal Disorders 1/2018

Developing a risk prediction model for the functional outcome after hip arthroscopy

BMC Musculoskeletal Disorders > Ausgabe 1/2018
Patrick Stephan, Maarten A. Röling, Nina M. C. Mathijssen, Gerjon Hannink, Rolf M. Bloem



Hip arthroscopic treatment is not equally beneficial for every patient undergoing this procedure. Therefore, the purpose of this study was to develop a clinical prediction model for functional outcome after surgery based on preoperative factors.


Prospective data was collected on a cohort of 205 patients having undergone hip arthroscopy between 2011 and 2015. Demographic and clinical variables and patient reported outcome (PRO) scores were collected, and considered as potential predictors. Successful outcome was defined as either a Hip Outcome Score (HOS)-ADL score of over 80% or improvement of 23%, defined by the minimal clinical important difference, 1 year after surgery. The prediction model was developed using backward logistic regression. Regression coefficients were converted into an easy to use prediction rule.


The analysis included 203 patients, of which 74% had a successful outcome. Female gender (OR: 0.37 (95% CI 0.17–0.83); p = 0.02), pincer impingement (OR: 0.47 (95% CI 0.21–1.09); p = 0.08), labral tear (OR: 0.46 (95% CI 0.20–1.06); p = 0.07), HOS-ADL score (IQR OR: 2.01 (95% CI 0.99–4.08); p = 0.05), WHOQOL physical (IQR OR: 0.43 (95% CI 0.22–0.87); p = 0.02) and WHOQOL psychological (IQR OR: 2.40 (95% CI 1.38–4.18); p = < 0.01) were factors in the final prediction model of successful functional outcome 1 year after hip arthroscopy. The model’s discriminating accuracy turned out to be fair, as 71% (95% CI: 64–80%) of the patients were classified correctly.


The developed prediction model can predict the functional outcome of patients that are considered for a hip arthroscopic intervention, containing six easy accessible preoperative risk factors. The model can be further improved trough external validation and/or adding additional potential predictors.
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