PDF(Pubmed)
Abstract：
UNASSIGNED: Proximal humerus fractures are a common injury, predominantly affecting older adults. This study aimed to develop risk-prediction models for prolonged length of hospital stay (LOS), serious adverse complications, and readmission within 30 days of surgically treated proximal humerus fractures using machine learning (ML) techniques.
UNASSIGNED: Adult patients (age >18) who underwent open reduction internal fixation (ORIF), hemiarthroplasty, or total shoulder arthroplasty for proximal humerus fracture between 2016 and 2021 were included. Preoperative demographic and clinical variables were collected for all patients and used to establish ML-based algorithms. The model with optimal performance was selected according to area under the curve (AUC) on the receiver operating curve (ROC) curve and overall accuracy, and the specific predictive features most important to model derivation were identified.
UNASSIGNED: A total of 7473 patients were included (72.1% male, mean age 66.2 ± 13.7 years). Models produced via gradient boosting performed best for predicting prolonged LOS and complications. The model predicting prolonged LOS demonstrated good discrimination and performance, as indicated by (Mean: 0.700, SE: 0.017), recall (Mean: 0.551, SE: 0.017), accuracy (Mean: 0.717, SE: 0.010), F1-score (Mean: 0.616, SE: 0.014), AUC (Mean: 0.779, SE: 0.010), and Brier score (Mean: 0.283, SE: 0.010) Preoperative hematocrit, preoperative platelet count, and patient age were considered the strongest predictive features. The model predicting serious adverse complications exhibited comparable discrimination [precision (Mean: 0.226, SE: 0.024), recall (Mean: 0.697, SE: 0.048), accuracy (Mean: 0.811, SE: 0.010), F1-score (Mean: 0.341, SE: 0.031)] and superior performance relative to the LOS model [AUC (Mean: 0.806, SE: 0.024), Brier score (Mean: 0.189, SE: 0.010), noting preoperative hematocrit, operative time, and patient age to be most influential. However, the 30-day readmission model achieved the weakest relative performance, displaying low measures of precision (Mean: 0.070, SE: 0.012) and recall (Mean: 0.389, SE: 0.053), despite good accuracy (Mean: 0.791, SE: 0.009).
UNASSIGNED: Predictive models constructed using ML techniques demonstrated favorable discrimination and satisfactory-to-excellent performance in forecasting prolonged LOS and serious adverse complications occurring within 30 days of surgical intervention for proximal humerus fracture. Modifiable preoperative factors such as hematocrit and platelet count were identified as significant predictive features, suggesting that clinicians could address these factors during preoperative patient optimization to enhance outcomes. Overall, these findings highlight the potential for ML techniques to enhance preoperative management, facilitate shared decision-making, and enable more effective and personalized orthopedic care by exploring alternative approaches to risk stratification.
UNASSIGNED: Adult patients (age >18) who underwent open reduction internal fixation (ORIF), hemiarthroplasty, or total shoulder arthroplasty for proximal humerus fracture between 2016 and 2021 were included. Preoperative demographic and clinical variables were collected for all patients and used to establish ML-based algorithms. The model with optimal performance was selected according to area under the curve (AUC) on the receiver operating curve (ROC) curve and overall accuracy, and the specific predictive features most important to model derivation were identified.
UNASSIGNED: A total of 7473 patients were included (72.1% male, mean age 66.2 ± 13.7 years). Models produced via gradient boosting performed best for predicting prolonged LOS and complications. The model predicting prolonged LOS demonstrated good discrimination and performance, as indicated by (Mean: 0.700, SE: 0.017), recall (Mean: 0.551, SE: 0.017), accuracy (Mean: 0.717, SE: 0.010), F1-score (Mean: 0.616, SE: 0.014), AUC (Mean: 0.779, SE: 0.010), and Brier score (Mean: 0.283, SE: 0.010) Preoperative hematocrit, preoperative platelet count, and patient age were considered the strongest predictive features. The model predicting serious adverse complications exhibited comparable discrimination [precision (Mean: 0.226, SE: 0.024), recall (Mean: 0.697, SE: 0.048), accuracy (Mean: 0.811, SE: 0.010), F1-score (Mean: 0.341, SE: 0.031)] and superior performance relative to the LOS model [AUC (Mean: 0.806, SE: 0.024), Brier score (Mean: 0.189, SE: 0.010), noting preoperative hematocrit, operative time, and patient age to be most influential. However, the 30-day readmission model achieved the weakest relative performance, displaying low measures of precision (Mean: 0.070, SE: 0.012) and recall (Mean: 0.389, SE: 0.053), despite good accuracy (Mean: 0.791, SE: 0.009).
UNASSIGNED: Predictive models constructed using ML techniques demonstrated favorable discrimination and satisfactory-to-excellent performance in forecasting prolonged LOS and serious adverse complications occurring within 30 days of surgical intervention for proximal humerus fracture. Modifiable preoperative factors such as hematocrit and platelet count were identified as significant predictive features, suggesting that clinicians could address these factors during preoperative patient optimization to enhance outcomes. Overall, these findings highlight the potential for ML techniques to enhance preoperative management, facilitate shared decision-making, and enable more effective and personalized orthopedic care by exploring alternative approaches to risk stratification.
摘要：
■肱骨近端骨折是一种常见的损伤，主要影响老年人。本研究旨在开发延长住院时间(LOS)的风险预测模型，严重的不良并发症，使用机器学习（ML）技术手术治疗肱骨近端骨折30天内再入院。
■接受切开复位内固定（ORIF）的成年患者（年龄>18岁），半髋关节置换术,纳入2016年至2021年治疗肱骨近端骨折的全肩关节置换术.收集所有患者的术前人口统计学和临床变量，并用于建立基于ML的算法。根据受试者工作曲线(ROC)曲线上的曲线下面积(AUC)和总体精度选择性能最优的模型,并确定了对模型推导最重要的特定预测特征。
■共纳入7473例患者（72.1%为男性，平均年龄66.2±13.7岁)。通过梯度增强产生的模型对于预测延长的LOS和并发症表现最佳。预测长期LOS的模型表现出良好的辨别力和性能，如（平均值：0.700，SE：0.017）所示，召回(平均值：0.551，SE：0.017)，准确度（平均值：0.717，SE：0.010），F1分数（平均值：0.616，SE：0.014），AUC（平均值：0.779，SE：0.010），和Brier评分（平均值：0.283，SE：0.010）术前血细胞比容，术前血小板计数,和患者年龄被认为是最强的预测特征。预测严重不良并发症的模型表现出可比的判别[精度（平均值：0.226，SE：0.024），召回(平均值：0.697，SE：0.048)，准确度（平均值：0.811，SE：0.010），F1分数（平均值：0.341，SE：0.031）]和相对于LOS模型的卓越性能[AUC（平均值：0.806，SE：0.024），Brier评分（平均值：0.189，SE：0.010），注意术前血细胞比容，手术时间,和患者年龄最有影响力。然而,30天再接纳模型取得了最弱的相对表现，显示低精度度量（平均值：0.070，SE：0.012）和召回率（平均值：0.389，SE：0.053），尽管准确性很好（平均值：0.791，SE：0.009）。
■使用ML技术构建的预测模型在预测肱骨近端骨折手术干预后30天内发生的延长LOS和严重不良并发症方面表现出良好的区分度和令人满意至优异的性能。可改变的术前因素如血细胞比容和血小板计数被确定为显著的预测特征。提示临床医生可以在术前患者优化过程中解决这些因素,以提高预后.总的来说,这些发现凸显了ML技术增强术前管理的潜力，促进共同决策，通过探索风险分层的替代方法，实现更有效和个性化的骨科护理。
■接受切开复位内固定（ORIF）的成年患者（年龄>18岁），半髋关节置换术,纳入2016年至2021年治疗肱骨近端骨折的全肩关节置换术.收集所有患者的术前人口统计学和临床变量，并用于建立基于ML的算法。根据受试者工作曲线(ROC)曲线上的曲线下面积(AUC)和总体精度选择性能最优的模型,并确定了对模型推导最重要的特定预测特征。
■共纳入7473例患者（72.1%为男性，平均年龄66.2±13.7岁)。通过梯度增强产生的模型对于预测延长的LOS和并发症表现最佳。预测长期LOS的模型表现出良好的辨别力和性能，如（平均值：0.700，SE：0.017）所示，召回(平均值：0.551，SE：0.017)，准确度（平均值：0.717，SE：0.010），F1分数（平均值：0.616，SE：0.014），AUC（平均值：0.779，SE：0.010），和Brier评分（平均值：0.283，SE：0.010）术前血细胞比容，术前血小板计数,和患者年龄被认为是最强的预测特征。预测严重不良并发症的模型表现出可比的判别[精度（平均值：0.226，SE：0.024），召回(平均值：0.697，SE：0.048)，准确度（平均值：0.811，SE：0.010），F1分数（平均值：0.341，SE：0.031）]和相对于LOS模型的卓越性能[AUC（平均值：0.806，SE：0.024），Brier评分（平均值：0.189，SE：0.010），注意术前血细胞比容，手术时间,和患者年龄最有影响力。然而,30天再接纳模型取得了最弱的相对表现，显示低精度度量（平均值：0.070，SE：0.012）和召回率（平均值：0.389，SE：0.053），尽管准确性很好（平均值：0.791，SE：0.009）。
■使用ML技术构建的预测模型在预测肱骨近端骨折手术干预后30天内发生的延长LOS和严重不良并发症方面表现出良好的区分度和令人满意至优异的性能。可改变的术前因素如血细胞比容和血小板计数被确定为显著的预测特征。提示临床医生可以在术前患者优化过程中解决这些因素,以提高预后.总的来说,这些发现凸显了ML技术增强术前管理的潜力，促进共同决策，通过探索风险分层的替代方法，实现更有效和个性化的骨科护理。
