| 南少奎,李铁键,肖龙涛,等.机器人辅助膝关节单髁置换可以获得良好的术后软组织平衡:一项单间室压力传感器研究.骨科,2024,15(5): 388-393. |
| 机器人辅助膝关节单髁置换可以获得良好的术后软组织平衡:一项单间室压力传感器研究 |
| Robotic Assisted Unicompartmental Knee Arthroplasty Results in Good Postoperative Soft Tissue Balance: A Unicompartmental Pressure Sensor Study |
| 投稿时间：2024-08-27 |
| DOI：10.3969/j.issn.1674-8573.2024.05.002 |
| 中文关键词: 机器人辅助手术 膝单髁置换术 压力传感器 软组织平衡 临床结果 |
| 英文关键词: Robot-assisted surgery Unicompartmental knee arthroplasty Pressure sensor Soft-tissue balancing Clinical outcomes |
| 基金项目:国家杰出青年科学基金项目（82425036）；北京市科技计划（Z221100003522014） |
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| 中文摘要: |
| 目的 使用一种新型单间室压力传感器测量术中膝关节内侧间室软组织张力，验证机器人软组织动态预平衡功能的有效性，为进一步探索更加智能且精准的手术方式提供临床经验和理论指导。方法 本研究前瞻性纳入中国人民解放军总医院第四医学中心骨科医学部2018年1月至2023年6月收治的35例（35膝）MAKO机器人辅助内侧单髁置换手术病人，男8例，女27例，年龄为（66.3±7.7）岁，身体质量指数为（24.5±6.3） kg/m2。所有手术均由同一位经验丰富的外科医生完成。手术中使用机器人的软组织动态平衡功能对屈伸间隙进行预评估，并对假体的计划进行调整，使伸直和屈曲的软组织张力平衡，截骨完成后使用单髁压力传感器对内侧间室软组织张力评估，记录屈膝10°、45°、90°时内侧间室股骨和胫骨假体之间的压力。比较病人术前及术后末次随访时的疼痛视觉模拟量表（visual analogue scale，VAS）、牛津膝关节评分（Oxford knee score，OKS）、关节活动度（range of motion，ROM）以评估手术效果。结果 病人均获得规律随访，随访时间为（31.8±14.2）个月。术中记录压力在屈膝10°时为（47.3±20.8） N，屈膝45°时为（40.1±18.2） N，屈膝90°时为（42.7±19.8） N。病人术后OKS评分、VAS评分及ROM较术前均有明显改善，差异均有统计学意义（P＜0.05）。结论 本研究使用的新型单间室压力传感器对机器人辅助膝关节单髁置换软组织张力有着准确的评估效果。机器人软组织动态预平衡功能可以在膝关节单髁置换术截骨前对软组织张力进行准确评估并精准调整假体规划，帮助膝关节获得良好的术后软组织平衡。 |
| 英文摘要: |
| Objective A novel unicompartmental pressure sensor was used to measure the intraoperative soft tissue tension in the medial compartment of the knee to validate the effectiveness of the robot's dynamic joint pre-balancing function and to provide clinical experience and theoretical guidance for exploring smarter and more precise surgical methods. Methods Totally, 35 patients with MAKO robot-assisted medial unicompartmental knee arthroplasty (UKA) admitted to our hospital between January 2018 and June 2023 were prospectively included in this study, including 8 males and 27 females, for a total of 35 cases and 35 knees. The age was (66.3±7.7) years old, and the body mass index was (24.5±6.3) kg/m2. All procedures were performed by the same experienced surgeon. The flexion and extension gaps were pre-assessed during surgery using the robot's soft tissue balancing technique and the surgical plan was adjusted to balance the soft tissue tensions in extension and flexion, and after completion of the osteotomy the soft tissue tensions in the medial compartment between the femoral and tibial component were assessed using a pressure sensor and the pressures were recorded at 10°, 45°, and 90° of knee flexion. The patients' visual analogue scores (VAS), Oxford Knee Score (OKS), and joint range of motion (ROM) were compared preoperatively and at the final postoperative follow-up to assess the surgical outcome. Results Patients were regularly followed up for (38.1±14.2) months. Intraoperative recorded pressures were (47.3±20.8) N at 10° of knee flexion, (40.1±18.2) N at 45° and (42.7±19.8) N at 90°. The patients' postoperative OKS scores, VAS scores and ROM were significantly improved as compared with the preoperative period, and the differences were statistically significant (P＜0.05). Conclusion This novel unicompartmental pressure sensor we used has an accurate assessment of soft tissue tension in UKA. The robotic soft tissue pre-balancing function can accurately assess the soft tissue tension before osteotomy and accurately adjust the surgical planning, which can help to obtain a good soft tissue balance of the knee. |
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