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Open Access Publications from the University of California

Subchondral insufficiency fractures of the femoral head: associated imaging findings and predictors of clinical progression.

  • Author(s): Hackney, Lauren A
  • Lee, Min Hee
  • Joseph, Gabby B
  • Vail, Thomas P
  • Link, Thomas M
  • et al.

OBJECTIVES:To characterize the morphology and imaging findings of femoral head subchondral insufficiency fractures (SIF), and to investigate clinical outcomes in relation to imaging findings. METHODS:Fifty-one patients with hip/pelvis magnetic resonance (MR) images and typical SIF characteristics were identified and reviewed by two radiologists. Thirty-five patients had follow-up documentation allowing assessment of clinical outcome. Subgroup comparisons were performed using regression models adjusted for age and body mass index. RESULTS:SIF were frequently associated with cartilage loss (35/47, 74.5 %), effusion (33/42, 78.6 %), synovitis (29/44, 66 %), and bone marrow oedema pattern (BMEP) (average cross-sectional area 885.7 ± 730.2 mm(2)). Total hip arthroplasty (THA) was required in 16/35 patients, at an average of 6 months post-MRI. Compared to the THA cohort, the non-THA group had significantly (p < 0.05) smaller overlying cartilage defect size (10 mm vs. 29 mm), smaller band length ratio and fracture diameters, and greater incidence of parallel fracture morphology (p < 0.05). Male gender and increased age were significantly associated with progression, p < 0.05. CONCLUSIONS:SIF were associated with synovitis, cartilage loss, effusion, and BMEP. Male gender and increased age had a significant association with progression to THA, as did band length ratio, fracture diameter, cartilage defect size, and fracture deformity/morphology. KEY POINTS:• Femoral head subchondral insufficiency fractures (SIF) frequently require total hip arthroplasty (THA). • SIF frequently coexist with synovitis, cartilage loss, and bone marrow oedema pattern. • SIF cartilage defect size, band length ratio, and fracture diameter/morphology can predict progression risk.

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