Cuthbert Alana R, Graves Stephen E, Giles Lynne C, Glonek Gary, Pratt Nicole
A. R. Cuthbert, S. E. Graves, N. Pratt, Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
S. E. Graves, Australian Orthopaedic Association National Joint Replacement Registry, Adelaide, Australia.
Clin Orthop Relat Res. 2021 Feb 1;479(2):392-403. doi: 10.1097/CORR.0000000000001533.
There is increasing interest in the development of statistical models that can be used to estimate risk of adverse patient outcomes after joint arthroplasty. Competing risk approaches have been recommended to estimate risk of longer-term revision, which is often likely to be precluded by the competing risk of death. However, a common approach is to ignore the competing risk by treating death as a censoring event and using standard survival models such as Cox regression. It is well-known that this approach can overestimate the event risk for population-level estimates, but the impact on the estimation of a patient's individualized risk after joint arthroplasty has not been explored.
QUESTIONS/PURPOSES: We performed this study to (1) determine whether using a competing risk or noncompeting risk method affects the accuracy of predictive models for joint arthroplasty revision and (2) determine the magnitude of difference that using a competing risks versus noncompeting risks approach will make to predicted risks for individual patients.
The predictive performance of a standard Cox model, with competing risks treated as censoring events, was compared with the performance of two competing risks approaches, the cause-specific Cox model and Fine-Gray model. Models were trained and tested using data pertaining to 531,304 TKAs and 274,618 THAs recorded in the Australian Orthopaedic Association National Joint Replacement Registry between January 1, 2003 and December 31, 2017. The registry is a large database with near-complete capture and follow-up of all hip and knee joint arthroplasty in Australia from 2003 onwards, making it an ideal setting for this study. The performance of the three modeling approaches was compared in two different prediction settings: prediction of the 10-year risk of all-cause revision after TKA and prediction of revision for periprosthetic fracture after THA. The calibration and discrimination of each approach were compared using the concordance index, integrated Brier scores, and calibration plots. Calibration of 10-year risk estimates was further assessed within subgroups of age by comparing the observed and predicted proportion of events. Estimated 10-year risks from each model were also compared in three hypothetical patients with different risk profiles to determine whether differences in population-level performance metrics would translate into a meaningful difference for individual patient predictions.
The standard Cox and two competing risks models showed near-identical ability to distinguish between high-risk and low-risk patients (c-index 0.64 [95% CI, 0.64 to 0.64] for all three modeling approaches for TKAs and 0.66 [95% CI 0.66 to 0.66] for THA). All models performed similarly in patients younger than 75 years, but for patients aged 75 years and older, the standard Cox model overestimated the risk of revision more than the cause-specific Cox and Fine-Gray model did. These results were echoed when predictions were made for hypothetical individual patients. For patients with a low competing risk of mortality, the 10-year predicted risks from the standard Cox, cause-specific Cox, and Fine-Gray models were similar for TKAs and THAs. However, a larger difference was observed for hypothetical 89-year-old patients with increased mortality risk. In TKAs, the revision risk for an 89-year-old patient was so low that this difference was negligible (0.83% from the cause-specific Cox model versus 1.1% from the standard Cox model). However, for THAs, where older age is a risk factor for both death and revision for periprosthetic fracture, a larger difference was observed in the 10-year predicted risks for a hypothetical 89-year-old patient (3.4% from the cause-specific Cox model versus 5.2% from the standard Cox model).
When developing models to predict longer-term revision of joint arthroplasty, failing to use a competing risks modeling approach will result in overestimating the revision risk for patients with a high risk of mortality during the surveillance period. However, even in an extreme instance, where both the frequency of the event of interest and the competing risk of death are high, the largest absolute difference in predicted 10-year risk for an individual patient was just 1.8%, which may not be of consequence to an individual. Despite these findings, when developing or using risk prediction models, researchers and clinicians should be aware of how competing risks were handled in the modeling process, particularly if the model is intended for use populations where the mortality risk is high.
Level III, therapeutic study.
人们对开发可用于估计关节置换术后患者不良结局风险的统计模型越来越感兴趣。推荐使用竞争风险方法来估计长期翻修风险，而死亡这一竞争风险往往可能会排除长期翻修风险。然而，一种常见的方法是将死亡视为删失事件，通过使用Cox回归等标准生存模型来忽略竞争风险。众所周知，这种方法在总体水平估计中会高估事件风险，但尚未探讨其对关节置换术后患者个体风险估计的影响。
问题/目的：我们开展这项研究是为了（1）确定使用竞争风险或非竞争风险方法是否会影响关节置换翻修预测模型的准确性，以及（2）确定使用竞争风险与非竞争风险方法对个体患者预测风险的差异程度。
将标准Cox模型（将竞争风险视为删失事件）的预测性能与两种竞争风险方法（特定病因Cox模型和Fine-Gray模型）的性能进行比较。使用2003年1月1日至2017年12月31日澳大利亚骨科协会国家关节置换登记处记录的531,304例全膝关节置换术（TKA）和274,618例全髋关节置换术（THA）的数据对模型进行训练和测试。该登记处是一个大型数据库，几乎完整记录并随访了2003年以来澳大利亚所有的髋膝关节置换术，使其成为本研究的理想环境。在两种不同的预测设置中比较三种建模方法的性能：TKA后全因翻修10年风险的预测以及THA后假体周围骨折翻修的预测。使用一致性指数、综合Brier评分和校准图比较每种方法的校准和区分度。通过比较观察到的和预测的事件比例，在年龄亚组内进一步评估10年风险估计的校准情况。还在三名具有不同风险特征的假设患者中比较了每个模型估计的10年风险，以确定总体水平性能指标的差异是否会转化为个体患者预测中有意义的差异。
标准Cox模型和两种竞争风险模型在区分高风险和低风险患者方面表现出几乎相同的能力（TKA的所有三种建模方法的c指数为0.64 [95% CI，0.64至0.64]，THA为0.66 [95% CI 0.66至0.66]）。所有模型在75岁以下患者中表现相似，但对于75岁及以上患者，标准Cox模型比特定病因Cox模型和Fine-Gray模型更高估翻修风险。对假设个体患者进行预测时也得到了类似结果。对于死亡竞争风险较低的患者，标准Cox模型、特定病因Cox模型和Fine-Gray模型对TKA和THA的10年预测风险相似。然而，在假设的89岁死亡风险增加的患者中观察到了更大差异。在TKA中，89岁患者的翻修风险非常低，以至于这种差异可以忽略不计（特定病因Cox模型为0.83%，标准Cox模型为1.1%）。然而，对于THA，年龄是死亡和假体周围骨折翻修的危险因素，在假设的89岁患者中，10年预测风险观察到更大差异（特定病因Cox模型为3.4%，标准Cox模型为5.2%）。
在开发预测关节置换长期翻修的模型时，不使用竞争风险建模方法将导致高估监测期内死亡风险高的患者的翻修风险。然而，即使在极端情况下，即感兴趣事件的频率和死亡竞争风险都很高时，个体患者预测的10年风险的最大绝对差异也仅为1.8%，这对个体可能无关紧要。尽管有这些发现，但在开发或使用风险预测模型时，研究人员和临床医生应了解在建模过程中如何处理竞争风险，特别是如果该模型旨在用于死亡风险高的人群。
III级，治疗性研究。
