利用人工神经网络预测全膝关节置换术后 90 天内的非计划性再入院。
The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty.
机构信息
Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
出版信息
Arch Orthop Trauma Surg. 2023 Jun;143(6):3279-3289. doi: 10.1007/s00402-022-04566-3. Epub 2022 Aug 7.
BACKGROUND
A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.
METHODS
A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis.
RESULTS
Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis.
CONCLUSION
This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission.
LEVEL OF EVIDENCE
Level III, case-control retrospective analysis.
背景
可靠的预测工具可以预测非计划性再入院，通过针对可改变的危险因素进行术前咨询和干预，有可能降低再入院率。本研究旨在开发和内部验证用于预测全膝关节置换术后 90 天非计划性再入院的机器学习模型。
方法
共有 10021 例连续患者接受了全膝关节置换术。对患者的病历进行人工复查，以确定可能与 90 天非计划性住院再入院相关的患者人口统计学和手术变量。开发了四种机器学习算法（人工神经网络、支持向量机、k-最近邻和弹性网惩罚逻辑回归）来预测全膝关节置换术后 90 天非计划性再入院，并使用 ROC AUC 统计数据以及校准和决策曲线分析来评估这些模型。
结果
在研究队列中，644 例患者（6.4%）在 90 天内再次入院。与 90 天非计划性医院再入院最显著相关的因素包括药物滥用、手术操作时间和美国麻醉医师协会身体状况评分（ASA）。机器学习模型在所有四个模型的区分度（AUC>0.82）、校准和决策曲线分析方面均表现出色。
结论
本研究开发了四种机器学习模型，用于预测全膝关节置换术后患者 90 天非计划性医院再入院。非计划性医院再入院的最强预测因素是药物滥用、手术操作时间和 ASA 评分。研究结果表明所有四个模型的性能均非常出色，突出了这些模型在手术前识别高危患者的潜力，对于协调护理工作可能降低随后的医院再入院风险具有重要意义。
证据水平
三级，病例对照回顾性分析。
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