Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
复制标题
DOI：
10.1016/j.arth.2019.05.034
复制
发表时间：
2019-10-01
影响因子：
3.5
通讯作者：
Patterson, Brendan M.
中科院分区：
文献类型：
--
作者：
Ramkumar, Prem N.;Karnuta, Jaret M.;Patterson, Brendan M.
背景：目的是开发和验证人工神经网络(ANN)来学习和预测初次全膝关节置换术(TKA)前的住院时间(LOS)、住院费用和出院处置。第二个目标是应用神经网络提出一个基于风险的、与病例复杂性相适应的患者特定支付模型(PSPM)。方法：使用来自国家住院患者样本和机构数据库的175,042个初级TKA的数据，建立一个神经网络来预测LOS、费用和处置，使用15个术前变量。结果指标包括受试者工作特性曲线的准确性和曲线下面积。在建立基于风险的PSPM时，模型不确定性由所有患者精化的共病指数分层。结果：动态模型在前30轮训练中表现为“学习”，曲线下面积分别为74.8%、82.8%和76.1%。PSPM显示，随着患者合并症的增加，中度、重度和重度合并症的风险分别增加了2.0%、21.8%和82.6%。结论：我们的深度学习模型在预测价值指标方面表现出了可接受的有效性、可靠性和响应性，提供了术前计划TKA护理事件的能力。该模型可应用于提出分级补偿的PSPM，以反映案件的复杂性。(C)2019 Elsevier Inc.保留所有权利。
Background: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific payment model (PSPM) commensurate with case complexity.Methods: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables. Outcome metrics included accuracy and area under the curve for a receiver operating characteristic curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a riskbased PSPM.Results: The dynamic model demonstrated "learning" in the first 30 training rounds with areas under the curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate, major, and severe comorbidities, respectively.Conclusion: Our deep learning model demonstrated "learning" with acceptable validity, reliability, and responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity. (C) 2019 Elsevier Inc. All rights reserved.