Mahajan Satish M, Nguyen Chantal, Bui Justin, Kunde Enomwoyi, Abbott Bruce T, Mahajan Amey S
Research & Innovation, Patient Care Services, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Research & Innovation, Patient Care Services, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.
Arthroplast Today. 2020 Jun 17;6(3):390-404. doi: 10.1016/j.artd.2020.04.017. eCollection 2020 Sep.
An increase in the aging yet active US population will continue to make total knee arthroplasty (TKA) procedures routine in the coming decades. For such joint procedures, the Centers for Medicare and Medicaid Services introduced programs such as the Comprehensive Care for Joint Replacement to emphasize accountable and efficient transitions of care. Accordingly, many studies have proposed models using risk factors for predicting readmissions after the procedure. We performed a systematic review of TKA literature to identify such models and risk factors therein using a reliable appraisal tool for their quality assessment.
Five databases were searched to identify studies that examined correlations between post-TKA readmission and risk factors using multivariate models. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis criteria established for quality assessment of prognostic studies.
Of 29 models in the final selection, 6 models reported performance using a C-statistic, ranging from 0.51 to 0.76, and 2 studies used a validation cohort for assessment. The average 30-day and 90-day readmission rates across the studies were 5.33% and 7.12%, respectively. Three new significant risk factors were discovered.
Current models for TKA readmissions lack in performance measurement and reporting when assessed with established criteria. In addition to using new techniques for better performance, work is needed to build models that follow the systematic process of calibration, external validation, and reporting for pursuing their deployment in clinical settings.
在未来几十年里,美国老龄化但仍活跃的人口数量增加将继续使全膝关节置换术(TKA)成为常规手术。对于此类关节手术,医疗保险和医疗补助服务中心推出了诸如关节置换综合护理等项目,以强调可问责且高效的护理过渡。因此,许多研究提出了利用风险因素预测术后再入院情况的模型。我们对TKA文献进行了系统综述,以使用可靠的评估工具对其质量评估来识别此类模型及其中的风险因素。
检索了五个数据库,以识别使用多变量模型研究TKA术后再入院与风险因素之间相关性的研究。我们遵循系统评价和Meta分析的首选报告项目方法以及为预后研究质量评估制定的个体预后或诊断多变量预测模型的透明报告标准。
在最终入选的29个模型中,6个模型报告了使用C统计量的表现,范围为0.51至0.76,2项研究使用了验证队列进行评估。各研究的平均30天和90天再入院率分别为5.33%和7.12%。发现了三个新的显著风险因素。
以既定标准评估时,当前TKA再入院模型在性能测量和报告方面存在不足。除了使用新技术以获得更好的性能外,还需要开展工作来构建遵循校准、外部验证和报告的系统流程的模型,以便在临床环境中进行应用。