Hornung Alexander L, Rudisill Samuel S, Smith Shelby, Streepy John T, Simcock Xavier C
Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL.
Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN.
J Hand Surg Glob Online. 2024 Aug 13;6(6):808-813. doi: 10.1016/j.jhsg.2024.06.002. eCollection 2024 Nov.
This study aimed to identify which patients were "unsafe" for outpatient surgery patients and determine the most predictive demographic and clinical factors contributing to postoperative risk following open reduction internal fixation for distal radius fractures.
Adult patients (aged ≥18 years) who presented with distal radius fracture and underwent open reduction internal fixation were identified using the American College of Surgeons National Surgical Quality Improvement Program database for years 2016 to 2021. Patients who were deemed "unsafe" therefore contraindicated for outpatient open reduction internal fixation of distal radius fracture if they required admission (length of stay of one or more days) or experienced any complication or required readmission within 7 days of the index operation. The model with optimal performance was determined according to area under the curve on the receiver operating characteristic curve and overall accuracy. Additional model metrics were also evaluated, and predictive factors (ie, features) that were most important to model derivation were identified.
A total of 2,020 eligible patients underwent open reduction and internal fixation for distal radius fractures. The majority (78.6%) were women, with a mean age of 57.5 ± 16.0 years. Of these patients, 21.5% experienced short-term adverse events. Gradient boosting was the optimal model for predicting patients who were "unsafe" for outpatient surgery, with key features including International Classification of Diseases, 10th Revision code, preoperative white blood cell count, age, body mass index, and Hispanic ethnicity.
Using machine learning techniques, a predictive model was developed, which demonstrated good discrimination and excellent performance in predicting which patients were "unsafe" for outpatient operative fixation of distal radius fracture. Findings of this study highlight the predictive value of artificial intelligence and machine learning for the purposes of preoperative risk stratification as well as its potential to better inform shared decision making and guide personalized fracture care.
LEVEL OF EVIDENCE/TYPE OF STUDY: Prognostic IV.
本研究旨在确定哪些患者对于门诊手术而言“不安全”，并确定在桡骨远端骨折切开复位内固定术后导致术后风险的最具预测性的人口统计学和临床因素。
利用美国外科医师学会国家外科质量改进计划数据库，识别出2016年至2021年期间出现桡骨远端骨折并接受切开复位内固定的成年患者（年龄≥18岁）。如果患者需要住院（住院时间为一天或更长时间），或者在初次手术后7天内出现任何并发症或需要再次入院，则被视为“不安全”，因此禁忌进行桡骨远端骨折的门诊切开复位内固定。根据受试者工作特征曲线上的曲线下面积和总体准确性，确定性能最佳的模型。还评估了其他模型指标，并确定了对模型推导最重要的预测因素（即特征）。
共有2020例符合条件的患者接受了桡骨远端骨折的切开复位内固定。大多数（78.6%）为女性，平均年龄为57.5±16.0岁。在这些患者中，21.5%经历了短期不良事件。梯度提升是预测门诊手术“不安全”患者的最佳模型，关键特征包括国际疾病分类第10版编码、术前白细胞计数、年龄、体重指数和西班牙裔种族。
利用机器学习技术开发了一个预测模型，该模型在预测哪些患者对于桡骨远端骨折的门诊手术固定“不安全”方面表现出良好的区分能力和出色的性能。本研究结果突出了人工智能和机器学习在术前风险分层方面的预测价值，以及其在更好地为共同决策提供信息和指导个性化骨折护理方面的潜力。
证据水平/研究类型：预后性IV级。
