机器学习能否识别适合桡骨远端骨折门诊开放复位内固定术的患者？

Q3 Medicine

Journal of Hand Surgery Global Online Pub Date : 2024-11-01 DOI:10.1016/j.jhsg.2024.06.002

Alexander L. Hornung MD , Samuel S. Rudisill MD , Shelby Smith MD , John T. Streepy MS , Xavier C. Simcock MD

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引用次数: 0

摘要

本研究旨在确定哪些患者对门诊手术患者来说是 "不安全 "的，并确定导致桡骨远端骨折切开复位内固定术后风险的最具预测性的人口统计学和临床因素。方法使用美国外科学院国家外科质量改进计划数据库（2016 年至 2021 年）确定桡骨远端骨折并接受切开复位内固定术的成人患者（年龄≥18 岁）。如果患者需要入院治疗（住院时间为 1 天或 1 天以上），或出现任何并发症，或在指数手术后 7 天内需要再次入院治疗，则被视为 "不安全"，因此禁止在门诊接受桡骨远端骨折切开复位内固定术。根据接收者操作特征曲线下的面积和总体准确性，确定了性能最佳的模型。结果共有 2020 名符合条件的患者接受了桡骨远端骨折切开复位内固定术。大多数（78.6%）患者为女性，平均年龄为（57.5 ± 16.0）岁。其中，21.5%的患者出现了短期不良反应。梯度提升法是预测门诊手术 "不安全 "患者的最佳模型，其主要特征包括国际疾病分类第 10 版代码、术前白细胞计数、年龄、体重指数和西班牙裔。这项研究的结果凸显了人工智能和机器学习在术前风险分层方面的预测价值，以及更好地为共同决策提供信息和指导个性化骨折护理的潜力。

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Can Machine Learning Identify Patients Who are Appropriate for Outpatient Open Reduction and Internal Fixation of Distal Radius Fractures?

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.