Marigi Erick M, Oeding Jacob F, Nieboer Micah, Marigi Ian M, Wahlig Brian, Barlow Jonathan D, Sanchez-Sotelo Joaquin, Sperling John W
Department of Orthopedic Surgery, Mayo Clinic, Jacksonville, FL, USA.
School of Medicine, Mayo Clinic Alix School of Medicine, Rochester, MN, USA.
J Shoulder Elbow Surg. 2025 Feb;34(2):462-472. doi: 10.1016/j.jse.2024.04.022. Epub 2024 Jun 8.
Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance of reverse shoulder arthroplasty (rTSA). Despite this, there remains a paucity of literature correlating prosthetic features and clinical outcomes. This investigation utilized a machine learning approach to evaluate the effect of select implant design features and patient-related factors on surgical complications after rTSA.
Over a 16-year period (2004-2020), all primary rTSA performed at a single institution for elective and traumatic indications with a minimum follow-up of 2 years were identified. Parameters related to implant design evaluated in this study included inlay vs. onlay humeral bearing design, glenoid lateralization (medialized or lateralized), humeral lateralization (medialized, minimally lateralized, or lateralized), global lateralization (medialized, minimally lateralized, lateralized, highly lateralized, or very highly lateralized), stem to metallic bearing neck shaft angle, and polyethylene neck shaft angle. Machine learning models predicting surgical complications were constructed for each patient and Shapley additive explanation values were calculated to quantify feature importance.
A total of 3837 rTSA were identified, of which 472 (12.3%) experienced a surgical complication. Those experiencing a surgical complication were more likely to be current smokers (Odds ratio [OR] = 1.71; P = .003), have prior surgery (OR = 1.60; P < .001), have an underlying diagnosis of sequalae of instability (OR = 4.59; P < .001) or nonunion (OR = 3.09; P < .001), and required longer OR times (98 vs. 86 minutes; P < .001). Notable implant design features at an increased odds for complications included an inlay humeral component (OR = 1.67; P < .001), medialized glenoid (OR = 1.43; P = .001), medialized humerus (OR = 1.48; P = .004), a minimally lateralized global construct (OR = 1.51; P < .001), and glenohumeral constructs consisting of a medialized glenoid and minimally lateralized humerus (OR = 1.59; P < .001), and a lateralized glenoid and medialized humerus (OR = 2.68; P < .001). Based on patient- and implant-specific features, the machine learning model predicted complications after rTSA with an area under the receiver operating characteristic curve of 0.61.
This study demonstrated that patient-specific risk factors had a more substantial effect than implant design configurations on the predictive ability of a machine learning model on surgical complications after rTSA. However, certain implant features appeared to be associated with a higher odd of surgical complications.
植入物设计和手术技术的技术进步一直致力于减少并发症并优化反肩关节置换术（rTSA）的性能。尽管如此，将假体特征与临床结果相关联的文献仍然匮乏。本研究采用机器学习方法来评估选定的植入物设计特征和患者相关因素对rTSA术后手术并发症的影响。
在16年期间（2004 - 2020年），确定了在单一机构进行的所有原发性rTSA，用于择期和创伤性适应症，且至少随访2年。本研究中评估的与植入物设计相关的参数包括嵌体式与覆盖式肱骨承重设计、肩胛盂外展（内移或外展）、肱骨外展（内移、最小程度外展或外展）、整体外展（内移、最小程度外展、外展、高度外展或非常高度外展）、柄至金属承重颈部干骺端角度以及聚乙烯颈部干骺端角度。为每位患者构建预测手术并发症的机器学习模型，并计算Shapley加性解释值以量化特征重要性。
共确定了3837例rTSA，其中472例（12.3%）发生了手术并发症。发生手术并发症的患者更可能是当前吸烟者（比值比[OR] = 1.71；P = 0.003）、有既往手术史（OR = 1.60；P < 0.001）、有不稳定后遗症（OR = 4.59；P < 0.001）或骨不连（OR = 3.09；P < 0.001）的潜在诊断，并且需要更长的手术时间（98分钟对86分钟；P < 0.001）。并发症发生几率增加的显著植入物设计特征包括嵌体式肱骨组件（OR = 1.67；P < 0.001）、内移肩胛盂（OR = 1.43；P = 0.001）、内移肱骨（OR = 1.48；P = 0.004）、最小程度外展的整体结构（OR = 1.51；P < 0.001）以及由内移肩胛盂和最小程度外展肱骨组成的盂肱结构（OR = 1.59；P < 0.001），还有外展肩胛盂和内移肱骨（OR = 2.68；P < 0.001）。基于患者和植入物特定特征，机器学习模型预测rTSA术后并发症的受试者工作特征曲线下面积为0.61。
本研究表明，在机器学习模型对rTSA术后手术并发症的预测能力方面，患者特定风险因素的影响比植入物设计配置更为显著。然而，某些植入物特征似乎与手术并发症的较高几率相关。
