Evidence map›Paper›PMID 39033286›Full record

ArticleJournal of orthopaedic surgery and research2024

Applying 3D-printed prostheses to reconstruct critical-sized bone defects of tibial diaphysis (> 10 cm) caused by osteomyelitis and aseptic non-union.

Bingchuan Liu, Liwei Wang, Xingcai Li, Zhuo Chen, Guojin Hou, Fang Zhou, Caimei Wang, Yun Tian

Abstract read
In one paragraph

Article in Journal of orthopaedic surgery and research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Basic science and current management for fracture-related infections.OTA international : the open access journal of orthopaedic trauma · 2025
    Article
  6. Review
  7. Review
  8. 3D Bioprinting in Limb Salvage Surgery.Journal of functional biomaterials · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Bingchuan Liu *Department of Orthopaedics, Peking University Third Hospital, Beijing, China.
Liwei Wang *Department of Anesthesiology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, China.
Xingcai LiDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China.
Zhuo ChenDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China.
Guojin HouDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China.
Fang ZhouDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China.
Caimei WangBeijing AKEC Medical Co., Ltd, Beijing, China.
Yun TianDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China. tiany@bjmu.edu.cn.

Funding

Beijing Municipal Science & Technology Commission Z181100001718195Key Clinical Projects of Peking University Third Hospital BYSYZD2022031National Natural Science Foundation of China 82172065Peking University Medicine Sailing Program for Young Scholars' Scientific & Technological Innovation BMU2023YFJHPY015
6 · The paper itself

Abstract

backgroundClinical repair of critical-sized bone defects (CBDs) in the tibial diaphysis presents numerous challenges, including inadequate soft tissue coverage, limited blood supply, high load-bearing demands, and potential deformities. This study aimed to investigate the clinical feasibility and efficacy of employing 3D-printed prostheses for repairing CBDs exceeding 10 cm in the tibial diaphysis.

methodsThis retrospective study included 14 patients (11 males and 3 females) with an average age of 46.0 years. The etiologies of CBDs comprised chronic osteomyelitis (10 cases) and aseptic non-union (4 cases), with an average defect length of 16.9 cm. All patients underwent a two-stage surgical approach: (1) debridement, osteotomy, and cement spacer implantation; and (2) insertion of 3D-printed prostheses. The interval between the two stages ranged from 8 to 12 weeks, during which the 3D-printed prostheses and induced membranes were meticulously prepared. Subsequent to surgery, patients engaged in weight-bearing and functional exercises under specialized supervision. Follow-up assessments, including gross observation, imaging examinations, and administration of the Lower Extremity Functional Scale (LEFS), were conducted at 3, 6, and 12 months postoperatively, followed by annual evaluations thereafter.

resultsThe mean postoperative follow-up duration was 28.4 months, with an average waiting period between prosthesis implantation and weight-bearing of 10.4 days. At the latest follow-up, all patients demonstrated autonomous ambulation without assistance, and their LEFS scores exhibited a significant improvement compared to preoperative values (30.7 vs. 53.1, P < 0.001). Imaging assessments revealed progressive bone regeneration at the defect site, with new bone formation extending along the prosthesis. Complications included interlocking screw breakage in two patients, interlocking screw loosening in one patient, and nail breakage in another.

conclusionsUtilization of 3D-printed prostheses facilitates prompt restoration of CBDs in the tibial diaphysis, enabling early initiation of weight-bearing activities and recovery of ambulatory function. This efficacious surgical approach holds promise for practical application.

Indexed as

DiaphysesOsteomyelitisPrinting, Three-DimensionalTibiaAdultAgedFeasibility StudiesFemaleFollow-Up StudiesFractures, UnunitedHumansMaleMiddle AgedOsteotomyPlastic Surgery ProceduresProstheses and Implants3D printing technologyAseptic non-unionOsteomyelitisTibial critical-sized defect

Identifiers

PMID39033286
PMCPMC11264997

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.