基于骨折闭合复位技术的新型骨盆骨折数字化分型系统的提出与验证

A digital classification system of pelvic fractures based on close reduction techniques

目的提出一种基于闭合复位技术的新型骨盆骨折数字化分型系统(JST分型),并探讨该分型的一致性和可行性。方法回顾性收集2021年3月至2023年3月期间北京积水潭医院创伤骨科根据JST分型手术治疗的63例骨盆骨折患者资料。根据骨盆骨折的部位和移位情况对骨盆骨折进行数字化归纳,首先将骨盆分为4个部分:左半骨盆、右半骨盆和骶骨DenisⅢ区、耻骨联合,其中左、右对称的骶骨DenisⅠ区和DenisⅡ区也被归入左/右半骨盆。随后半骨盆被分为4个区域并用大写的英文字母分别标记:骶骨区(sacrum area,包括DenisⅠ区和DenisⅡ区,记作S)、骶髂关节区(sacroiliac joint area,记作J)、髂骨区(iliac area,记作I)、耻骨区(pubic area,记作P),为区分右/左侧采用R和L作为前缀;不对称的2个部分同样以英文字母标记:骶骨DenisⅢ区(记作Sac)、耻骨联合(pubic symphysis,记作C)。之后用数字对各区域的骨折线形态和移位情况进行标记,形成完整的JST分型。JST分型系统的观察者间和观察者内可靠性(Fleiss'和Cohen's Kappa系数)由3名具有10年以上骨盆骨折治疗经验的观察者进行测试。结果分型结果的一致性分析中,组间差异Kappa为0.818(0.658~0.946,P均<0.001),组内差异Kappa为0.873(0.674~1.000,P均<0.001),均呈现强一致性。根据分型结果,术中在罗森万相智能化骨科手术机器人系统的辅助下成功闭合复位59例,成功率93.7%(59/63)。结论本研究建立的JST分型组间和组内的一致性强。由于对各个骨折部位和关键骨块进行标记,对智能化骨折机器人的深度学习和术中操作功能有重要的意义。

Objective To explore the feasibility and consistency of a new digital classification system of pelvic fractures named as JST classification based on close reduction techniques.Methods A retrospective collection was conducted of the data from the 63 patients with pelvic fracture who had undergone surgical treatment after JST classification at Department of Orthopaedics and Traumatology,Beijing Jishuitan Hospital from March 2021 to March 2023.Digital classification of the pelvic fractures was performed based on their locations and displacements.The classification first divides the pelvis into 4 parts:left half pelvis and right half pelvis;sacral DenisⅢarea and pubic symphysis.The symmetrical left and right sacral DenisⅠand DenisⅡareas are also included in the left/right half pelvis.Subsequently,the left half pelvis and right half pelvis are divided into 4 regions and marked by capitalized English letters:Sacrum Area(including Denis Ⅰ and Denis Ⅱ,denoted as S),Sacroiliac Joint Area(denoted as J),Iliac Area(denoted as I),and Pubic Area(denoted as P);to distinguish right/left,R and L are used as prefixes.The 2 asymmetric parts are also marked with English letters:Denis Ⅲ area of the sacrum(denoted as Sac),and pubic symphysis(denoted as C).Afterwards,the fracture line morphology and displacement in each region are marked digitally to form a complete JST classification system.The inter-and intra-observer reliabilities(Fleiss'and Cohen's Kappa)of the JST classification system were tested by 3 observers with more than 10 years of experience in pelvic fracture treatment.Results Consistency analysis of the JST classification results showed that the mean κ value of the intra-observer reliability was 0.818(from 0.658 to 0.946,P<0.001)and the inter-observer reliability 0.873(from 0.674 to 1.000,P<0.001),both indicating excellent agreement.Of the 63 patients,59 obtained successful closed reduction with the assistance of the Rossum Robot R-Universal intelligent orthopedic surgical robot system after fracture classification by the JST system,yielding a success rate of 93.7%(59/63).Conclusions The new JST classification system for pelvic fractures demonstrates strong intra and inter-observer reliabilities compared with traditional classification systems.As JST classification system labels each fracture site and key bones,it is of great significance for the deep learning and intraoperative operations of intelligent fracture robots.