Objectives: To present a novel single anterior-lateral approach for the treatment of distal tibia and fibula fracture via anatomical study and primary clinical application in order to minimize soft tissue complications.
Design: Both a gross anatomic cadaver and retrospective studies of the single-incision technique in patients recruited between June 2004 and January 2010.
Setting: Level I trauma center.
Patients/participants: Twenty-six legs of 14 adult human cadavers and clinical recruitment of 49 patients (29 males, 20 females) with a mean age of 37.6 years (range 11-68) with fracture of distal 1/3 tibia and fibula.
Intervention: A single anterior-lateral incision technique for open reduction and internal fixations of distal tibia and fibula fractures.
Main Outcome Measures: To identify the anatomic structures at risk in the anterolateral aspect of the lower leg and explicit the safe dissection distance from the extensor digitorum longus (EDL) to tibia and fibula, 26 legs of 14 adult human embalmed specimens were recruited in the anatomical study with the distance between the EDL and the anterior edge of the distal thirds of the tibia, as well as the distance between the EDL and the anterior edge of the distal thirds of the fibula were measured, and their mutual relationships to the surrounding anatomical structures described. Mean average standard deviations were also calculated. As for the clinical study, the quality of bone union and soft tissue healing were noted.
Results: The mean distances between the distal tibia and the EDL were measured to be 2.96 ± 0.46 cm (proximal), 1.85 ± 0.25 cm (middle), and 2.15 ± 0.30 cm (distal), and that between the fibula and the EDL were 1.82 ± 0.28 cm (proximal), 2.09 ± 0.31 cm (middle), and 2.30 ± 0.27 cm (distal), which means the safe gap from the distal tibia to EDL was 1.6-3.4 cm and from the EDL to fibula was 1.5-2.6 cm. The anterior tibial vein and artery and the deep fibular nerve lie on the anterior interosseous membrane over the lateral surface of the distal tibia were excellently visualized. Review of clinical outcomes in 49 patients with combined distal tibial and fibular fractures who underwent reduction and fixation with the single-incision technique, revealed uneventful fracture healings in 47 patients; and two cases of superficial wound necrosis which were treated and healed in 4 months. There was no case of delayed union or non-union.
Conclusion: Distal fibula fracture occurring with distal tibia fracture poses a challenge for stable fixation. This has necessitated the need for dual incisions on the distal leg to approach each fracture for reduction and fixation. However, a single anterolateral incision enables the safe approach to the lateral aspects of the distal tibia and fibula thus eliminating the need for two separate incisions and minimizing the soft tissue complication to some extent. Meanwhile, the neurovascular bundle at risk during operation, distal tibia and fibula is clearly exposed in the single anterior-lateral incision.
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http://dx.doi.org/10.1007/s00402-013-1856-9 | DOI Listing |
JSES Rev Rep Tech
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Department of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
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Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America.
High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site.
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