ArticleOrthopadie (Heidelberg, Germany)2026
[Identification of joint-specific risk factors for risk adjustment in total knee arthroplasty-A modified Delphi process].
Article in Orthopadie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Joint-specific measures improve risk adjustment in total knee arthroplasty: A machine learning approach.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
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18 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundValid risk adjustment is a prerequisite in knee arthroplasty to enable fair comparisons of treatment quality across hospitals and surgeons. Existing models mainly consider patient-related factors such as age or comorbidities. Consensus on joint-specific risk factors for postoperative complications is currently lacking.
objectivesThe aim of this study was to establish expert consensus on clinically relevant joint-specific risk factors in total knee arthroplasty (TKA).
methodsA three-stage modified Delphi process was conducted with 14 German experts in the field of TKA. Based on a systematic literature search, 120 potential risk factors were identified. In the first round, all factors were rated; in the second round, the focus was narrowed to 70 joint-specific factors. In a final virtual consensus meeting (third round), the most relevant factors were refined and prioritized.
resultsTwelve joint-specific risk factors were identified and ranked: prior septic surgery, large bone defects, implanted foreign material, valgus malalignment > 10°, varus malalignment > 15°, extension deficit > 10°, flexion < 70°, Kellgren-Lawrence score < 3°, patella baja, prior bone surgery, genu recurvatum > 10°, and prior ligament reconstruction.
conclusionsThrough a structured Delphi consensus process, 12 joint-specific risk factors were defined by a panel of German arthroplasty experts. These factors may meaningfully complement existing risk adjustment models. Future research should evaluate their predictive validity and integration into routine registries (e.g., EPRD) as well as quality assurance instruments.
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