Suppr超能文献

开发膝关节置换术的个性化预后预测工具。

Developing a personalized outcome prediction tool for knee arthroplasty.

机构信息

Department of Orthopaedic Surgery, Cleveland Clinic, Cleveland, Ohio, USA.

出版信息

Bone Joint J. 2020 Sep;102-B(9):1183-1193. doi: 10.1302/0301-620X.102B9.BJJ-2019-1642.R1.

Abstract

AIMS

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

METHODS

Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.

RESULTS

Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores.

CONCLUSION

The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: 2020;102-B(9):1183-1193.

摘要

目的

本研究旨在开发一种个性化的预后预测工具,用于膝关节置换术患者,该工具可个体化预测结果(住院时间 (LOS)、90 天再入院率和一年患者报告的预后指标 (PROM)),并允许对动态可修改的危险因素进行预测。

方法

前瞻性收集了 2015 年 7 月至 2018 年 6 月间所有接受全膝关节置换术或单髁膝关节置换术的患者的数据。队列 1(n=5958)用于开发 LOS 和 90 天再入院率的模型。队列 2(n=2391,手术日期为 2015 年至 2017 年)用于开发一年后膝关节损伤和骨关节炎结果评分(KOOS)疼痛评分、KOOS 功能评分和 KOOS 生活质量(QOL)评分改善的模型。通过交叉验证,使用均方根误差 (RMSE) 和平均绝对误差 (MAE) 评估缺失数据集中的 LOS 和 PROM 模型的准确性,并使用预测准确性指数 (IPA) 和曲线下面积 (AUC) 评估再入院模型的准确性。使用 AUC 评估新患者数据集的模型准确性。

结果

在缺失数据集中,LOS(RMSE 1.161)和 PROM 模型(RMSE 分别为 15.775、11.056、21.680,用于 KOOS 疼痛、功能和 QOL)的准确性均较好。对于所有模型,在新患者组中预测结果的准确性与整体交叉验证准确性一致。在使用新患者数据集进行验证时,LOS 和再入院模型的准确性较高(分别为 71.5%和 65.0%)。同样,一年后 PROM 改善模型在预测 KOOS 疼痛(72.1%)、功能(72.9%)和 QOL(70.8%)评分提高 10 分方面具有较高的准确性。

结论

本研究中开发的数据驱动模型提供了可扩展的预测工具,可准确估计膝关节置换术后一年疼痛、功能和生活质量改善的可能性,以及 LOS 和 90 天再入院率。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验