Rezapour Mostafa, Seymour Rachel B, Medda Suman, Sims Stephen H, Karunakar Madhav A, Habet Nahir, Gurcan Metin Nafi
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.
Department of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.
Bioengineering (Basel). 2025 Jan 14;12(1):67. doi: 10.3390/bioengineering12010067.
In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we focused on 12 key gait variables, including Mean Leg Lift Acceleration, Stance Time, and Body Orientation. We employed a linear mixed model (LMM) to analyze these variables over time, incorporating both fixed and random effects to account for individual differences and the time since injury. This model also adjusted for varying intervals between assessments. Our study provided insights into gait recovery across different fracture types using data from 318 patients who experienced no complications or readmissions during their recovery. Through LMM analysis, we found that Tibia-Distal fractures demonstrated the fastest recovery, particularly in terms of mobility and strength. Tibia-Proximal fractures showed balanced improvements in both mobility and stability, suggesting that rehabilitation should target both strength and balance. Femur fractures exhibited varied recovery, with Diaphyseal fractures showing clear improvements in stability, while Distal fractures reflected gains in limb strength but with some variability in stability. To examine patients with readmissions, we conducted a Chi-squared test of independence to determine whether there was a relationship between fracture type and readmission rates, revealing a significant association ( < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes.
在一项前瞻性研究中,我们检查了下肢骨折患者的恢复轨迹,以更好地了解无并发症情况下的愈合过程。使用胸部佩戴的惯性测量单元(IMU)设备进行步态分析并收集患者报告的结局指标,我们重点关注12个关键步态变量,包括平均腿部抬起加速度、站立时间和身体方位。我们采用线性混合模型(LMM)来分析这些变量随时间的变化,纳入固定效应和随机效应以考虑个体差异和受伤后的时间。该模型还针对评估之间的不同间隔进行了调整。我们的研究利用318例恢复过程中未出现并发症或再次入院的患者的数据,深入了解了不同骨折类型的步态恢复情况。通过LMM分析,我们发现胫骨干骺端骨折恢复最快,尤其是在 mobility 和 strength 方面。胫骨近端骨折在 mobility 和稳定性方面均有均衡改善,这表明康复应针对 strength 和 balance 。股骨骨折的恢复情况各不相同,骨干骨折在稳定性方面有明显改善,而远端骨折则显示肢体 strength 有所增加,但稳定性存在一定差异。为了检查再次入院的患者,我们进行了独立性卡方检验,以确定骨折类型与再次入院率之间是否存在关联,结果显示存在显著关联(<0.001)。骨盆骨折的再次入院率最高,而胫骨干和胫骨干骺端骨折更容易发生感染,这突出了加强感染控制策略的必要性。股骨骨折的再次入院率和感染率中等,表明风险状况较为复杂。总之,我们的研究结果强调了针对骨折的康复策略的重要性,并注重预防感染和制定个性化治疗方案,以优化恢复结果。