Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Yuk Choi Rd 11, 999077, Hong Kong SAR, China.
Arch Orthop Trauma Surg. 2024 Sep;144(9):4411-4420. doi: 10.1007/s00402-024-05542-9. Epub 2024 Sep 19.
Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository.
We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility.
The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age.
Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.
翻修全髋关节置换术（THA）后住院时间延长（LOS）可能导致医疗保健成本增加、再入院率升高和患者满意度降低。在这项研究中，我们使用来自全国规模患者资料库的患者数据，研究了机器学习（ML）模型对翻修 THA 后 LOS 延长的预测能力。
我们从美国外科医师学会国家手术质量改进计划数据库中确定了 11737 例翻修 THA 病例，这些病例来自 2013 年至 2020 年。LOS 延长定义为超过研究队列中所有 LOS 值的第 75 个值。我们开发了四种 ML 模型：人工神经网络（ANN）、随机森林、基于直方图的梯度提升和 K 最近邻，以预测翻修 THA 后 LOS 延长。在训练和测试过程中，每个模型的性能都根据判别能力、校准和临床实用性进行评估。
ANN 模型在测试期间的 AUC 为 0.82，校准斜率为 0.90，校准截距为 0.02，Brier 分数为 0.140，最准确，表明该模型在最小预测误差下区分 LOS 延长患者的能力。所有模型在决策曲线分析中产生净收益，均表现出临床实用性。LOS 延长的最重要预测因素是术前血液检查（红细胞压积、血小板计数和白细胞计数）、术前输血、手术时间、翻修 THA 的指征（感染）和年龄。
我们的研究表明，ML 模型准确预测了翻修 THA 后 LOS 延长。结果强调了手术指征在确定 LOS 延长风险方面的重要性。有了该模型的帮助，临床医生可以根据关键因素对个体患者进行分层，为那些有 LOS 延长风险的患者改善护理协调和出院计划，并提高成本效率。
