Shah Akash A, Devana Sai K, Lee Changhee, Kianian Reza, van der Schaar Mihaela, SooHoo Nelson F
Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA.
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA.
J Arthroplasty. 2021 May;36(5):1655-1662.e1. doi: 10.1016/j.arth.2020.12.040. Epub 2020 Dec 30.
As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods.
This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration.
There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease).
We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.
随着髋骨关节炎患病率的增加，全髋关节置换术（THA）的手术数量预计也会上升。鉴于围手术期并发症的发生会带来巨大的成本和发病率，准确地对接受THA的患者进行风险分层将非常有用。我们旨在开发一种基于机器学习（ML）的新型集成算法，用于预测THA术后的主要并发症，并将其性能与标准的基准ML方法进行比较。
这是一项对2015年至2017年间在加利福尼亚州任何一家持牌医院接受初次THA的89986名成年人进行的回顾性队列研究。主要结局是主要并发症（如感染、静脉血栓栓塞、心脏并发症、肺部并发症）。我们使用AutoPrognosis开发了一个预测并发症风险的模型，AutoPrognosis是一个自动化的ML框架，可配置性能最佳的基于ML的预后模型集成。我们将我们的模型与逻辑回归和标准基准ML模型进行比较，评估辨别力和校准情况。
有545名患者发生了主要并发症（0.61%）。我们的新算法校准良好，与逻辑回归相比，改善了风险预测，并且优于其他四种标准基准ML算法。对AutoPrognosis最重要的变量（如营养不良、痴呆、癌症）与对逻辑回归最重要的变量（如慢性动脉粥样硬化、肾衰竭、慢性阻塞性肺疾病）不同。
我们报告了一种用于预测THA术后主要并发症的新型集成ML算法。与逻辑回归和其他标准ML基准算法相比，它显示出卓越的风险预测能力。通过提供准确的预后信息，该算法可能有助于在术前做出更明智的共同决策。
