School of Engineering, The University of Newcastle, Callaghan, New South Wales, Australia; Centre for Rehab Innovations, The University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, New Lambton Heights, New South Wales, Australia.
School of Humanities and Social Science, The University of Newcastle, Callaghan, New South Wales, Australia.
J Arthroplasty. 2021 May;36(5):1832-1845.e1. doi: 10.1016/j.arth.2020.10.053. Epub 2020 Nov 6.
Total knee arthroplasty is the standard surgical treatment for end-stage osteoarthritis. Although widely accepted as a successful procedure, approximately 30% of patients are not satisfied due to non-optimal postoperative outcomes. Clinical decision support tools that are able to accurately predict post-surgery outcomes would assist in providing individualized advice or services to help alleviate possible issues, resulting in significant benefits to both the healthcare system and individuals.
Five databases (Ovid Medline, Ovid EMBASE, CINAHL complete, Cochrane Library, and Scopus) were searched for the key phrases "knee replacement" or "knee arthroplasty" and "decision support tool," "decision tool," "predict∗ tool," "predict∗ model," "algorithm" or "nomogram." Searches were limited to peer-reviewed journal articles published between January 2000 and June 2019. Reference lists of included articles were examined. Authors came to a consensus on the final list of included articles.
Eighteen articles were included for review. Most models reported low predictive success and inability to externally validate. Both candidate and final predictor variables were inconsistent between studies. Only 1 model was considered strongly predictive (AUROC >0.8), and only 2 studies were able to externally validate their developed model. In general, models that performed well used large patient numbers, were tested on similar demographics, and used either nonlinear input transformations or a completely nonlinear model.
Some models do show promise; however, there remains the question of whether the reported predictive success can continue to be replicated. Furthermore, clinical applicability and interpretation of predictive tools should be considered during development.
全膝关节置换术是治疗晚期骨关节炎的标准手术治疗方法。尽管被广泛认为是一种成功的手术，但约有 30%的患者由于术后效果不理想而不满意。能够准确预测术后结果的临床决策支持工具将有助于提供个性化的建议或服务，以帮助缓解可能出现的问题，从而使医疗保健系统和个人都受益。
在 Ovid Medline、Ovid EMBASE、CINAHL complete、Cochrane Library 和 Scopus 五个数据库中搜索了“膝关节置换术”或“膝关节置换术”和“决策支持工具”、“决策工具”、“预测工具”、“预测模型”、“算法”或“列线图”的关键词。检索范围限于 2000 年 1 月至 2019 年 6 月发表的同行评议期刊文章。检查了纳入文章的参考文献列表。作者就最终纳入文章的清单达成一致。
纳入了 18 篇文章进行综述。大多数模型报告的预测成功率低，无法进行外部验证。研究之间候选和最终预测变量不一致。只有 1 个模型被认为具有很强的预测能力（AUROC>0.8），只有 2 项研究能够对其开发的模型进行外部验证。一般来说，表现良好的模型使用了大量患者，在相似的人口统计学数据上进行了测试，并且使用了非线性输入转换或完全非线性模型。
一些模型确实有希望；但是，仍然存在所报告的预测成功率是否可以继续复制的问题。此外，在开发过程中应考虑预测工具的临床适用性和解释。
