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01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Musculoskeletal Disorders 1/2015

Identification of patients with suboptimal results after hip arthroplasty: development of a preliminary prediction algorithm

Zeitschrift:
BMC Musculoskeletal Disorders > Ausgabe 1/2015
Autoren:
Eugen Lungu, Pascal-André Vendittoli, François Desmeules
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12891-015-0720-1) contains supplementary material, which is available to authorized users.

Competing interest

The authors declare that they have no competing interests.

Authors’ contributions

EL led the design, the coordination, the collection of data, the statistical analysis, the interpretation of results and the writing of the manuscript. PAV participated in the design, the coordination, the collection of data, the interpretation of results and the writing of the manuscript. All authors read and approved the final manuscript. FD participated in the design, the coordination, the statistical analysis, the interpretation of results and the writing of the manuscript.

Abstract

Background

The ability to predict preoperatively the identity of patients undergoing hip arthroplasty at risk of suboptimal outcomes could help implement interventions targeted at improving surgical results. The objective was to develop a preliminary prediction algorithm (PA) allowing the identification of patients at risk of unsatisfactory outcomes one to two years following hip arthroplasty.

Methods

Retrospective data on a cohort of 265 patients having undergone primary unilateral hip replacement (188 total arthroplasties and 77 resurfacing arthroplasties) from 2004 to 2010 were collected from our arthroplasty database. Hip pain and function, as measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were collected, as well as self-reported hip joint perception after surgery. Demographic and clinical variables recorded at the time of the surgery were considered as potential predictors. Patients were considered as having a suboptimal surgical outcome if they were in the worst quartile of the postoperative total WOMAC score and perceived their operated hip as artificial with minimal or major limitations. The PA was developed using recursive partitioning.

Results

Mean postoperative surgical follow-up was 446 ± 171 days. Forty patients (15.1 %) had a postoperative total WOMAC score in the worst quartile (>11.5/100) and perceived their joint as artificial with minimal or major restrictions. A PA consisting of the following variables achieved the most acceptable level of prediction: gender, age at the time of surgery, body mass index (BMI), and three items of the preoperative WOMAC (degree of pain with walking on a flat surface and during the night as well as degree of difficulty with putting socks or stockings). The rule had a sensitivity of 75.0 % (95 % CI: 59.8-85.8), a specificity of 77.8 % (95 % CI: 71.9–82.7), a positive predictive value of 37.5 % (95 % CI: 27.7–48.5), a negative predictive value of 94.6 % (95 % CI: 90.3–97.0) and positive and negative likelihood ratios of 3.38 (95 % CI: 2.49–4.57) and 0.34 (95 % CI: 0.19–0.55) respectively.

Conclusions

The preliminary PA shows promising results at identifying patients at risk of significant functional limitations, increased pain and inadequate joint perception after hip arthroplasty. Clinical use should not be implemented before additional validation and refining.
Zusatzmaterial
Additional file 1: STROBE Statement Checklist of items that should be included in reports ofcohort studies. (PDF 95 kb)
12891_2015_720_MOESM1_ESM.pdf
Additional file 2: Appendix contains six prediction algorithms that were also considered, along with their respective two by two tables and validity measures. (DOCX 1064 kb)
12891_2015_720_MOESM2_ESM.docx
Literatur
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