Shah Rohan M, Khazanchi Rushmin, Bajaj Anitesh, Rana Krishi, Malhotra Saaz, Wolf Jennifer Moriatis
Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Northwestern University, Evanston, IL, USA.
J Hand Microsurg. 2024 Sep 20;16(5):100156. doi: 10.1016/j.jham.2024.100156. eCollection 2024 Dec.
Thumb carpometacarpal (CMC) joint osteoarthritis is among the most common degenerative hand diseases. Thumb CMC arthroplasty, or trapeziectomy with or without tendon augmentation, is the most frequently performed surgical treatment and has a strong safety profile. Though adverse outcomes are infrequent, the ability to predict risk for complications has substantial clinical benefits. In the present study, we evaluated a well-known surgical database with machine learning (ML) techniques to predict short-term complications and reoperations after thumb CMC arthroplasty.
A retrospective study was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005-2020. Outcomes were 30-day wound and medical complications and 30-day return to the operating room. We used three ML algorithms - a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tree (XGBoost), and a deep learning Neural Network (NN). Feature importance analysis was performed in the highest performing model for each outcome to identify predictors with the greatest contributions.
We included a total of 7711 cases. The RF was the best performing algorithm for all outcomes, with an AUC score of 0.61±0.03 for reoperations, 0.55±0.04 for medical complications, and 0.59±0.03 for wound complications. On feature importance analysis, procedure duration was the highest weighted predictor for reoperations. In all outcomes, procedure duration, older age, and female sex were consistently among the top five predictors.
We successfully developed ML algorithms to predict reoperations, wound complications, and medical complications. RF models had the highest performance in all outcomes.
拇指腕掌（CMC）关节骨关节炎是最常见的手部退行性疾病之一。拇指CMC关节成形术，即带或不带肌腱增强的大多角骨切除术，是最常实施的外科治疗方法，且安全性良好。尽管不良后果并不常见，但预测并发症风险的能力具有重大临床意义。在本研究中，我们使用机器学习（ML）技术评估了一个知名的外科手术数据库，以预测拇指CMC关节成形术后的短期并发症和再次手术情况。
利用美国外科医师学会国家外科质量改进计划（ACS-NSQIP）2005年至2020年的数据进行了一项回顾性研究。结局指标为30天伤口及医疗并发症以及30天返回手术室的情况。我们使用了三种ML算法——随机森林（RF）、弹性网络回归（ENet）和极端梯度提升树（XGBoost），以及一种深度学习神经网络（NN）。对每个结局表现最佳的模型进行特征重要性分析，以确定贡献最大的预测因素。
我们共纳入7711例病例。RF是所有结局表现最佳的算法，再次手术的AUC评分为0.61±0.03，医疗并发症为0.55±0.04，伤口并发症为0.59±0.03。在特征重要性分析中，手术持续时间是再次手术加权最高的预测因素。在所有结局中，手术持续时间、年龄较大和女性性别始终位列前五大预测因素之中。
我们成功开发了ML算法来预测再次手术、伤口并发症和医疗并发症。RF模型在所有结局中表现最佳。
