Losciale Justin M, Truong Linda K, Ward Patrick, Collins Gary S, Bullock Garrett S
Physical Therapy University of British Columbia.
Arthritis Research Canada.
Int J Sports Phys Ther. 2024 Sep 1;19(9):1151-1164. doi: 10.26603/001c.122644. eCollection 2024.
Athlete injury risk assessment and management is an important, yet challenging task for sport and exercise medicine professionals. A common approach to injury risk screening is to stratify athletes into risk groups based on their performance on a test relative to a cut-off threshold. However, one potential reason for ineffective injury prevention efforts is the over-reliance on identifying these 'at-risk' groups using arbitrary cut-offs for these tests and measures. The purpose of this commentary is to discuss the conceptual and technical issues related to the use of a cut-off in both research and clinical practice.
How can we better assess and interpret clinical tests or measures to enable a more effective injury risk assessment in athletes?
Cut-offs typically lack strong biologic plausibility to support them; and are typically derived in a data-driven manner and thus not generalizable to other samples. When a cut-off is used in analyses, information is lost, leading to potentially misleading results and less accurate injury risk prediction. Dichotomizing a continuous variable using a cut-off should be avoided. Using continuous variables on its original scale is advantageous because information is not discarded, outcome prediction accuracy is not lost, and personalized medicine can be facilitated.
Researchers and clinicians are encouraged to analyze and interpret the results of tests and measures using continuous variables and avoid relying on singular cut-offs to guide decisions. Injury risk can be predicted more accurately when using continuous variables in their natural form. A more accurate risk prediction will facilitate personalized approaches to injury risk mitigation and may lead to a decline in injury rates.
对运动和运动医学专业人员而言，运动员损伤风险评估与管理是一项重要但具有挑战性的任务。损伤风险筛查的一种常见方法是根据运动员在某项测试中的表现相对于某个临界值，将其分为风险组。然而，预防损伤措施效果不佳的一个潜在原因是过度依赖使用这些测试和测量的任意临界值来识别这些“高风险”组。本评论的目的是讨论在研究和临床实践中与使用临界值相关的概念和技术问题。
我们如何能更好地评估和解释临床测试或测量，以便在运动员中进行更有效的损伤风险评估？
临界值通常缺乏有力的生物学合理性来支持它们；并且通常是以数据驱动的方式得出，因此无法推广到其他样本。当在分析中使用临界值时，信息会丢失，导致可能产生误导性的结果以及损伤风险预测不够准确。应避免使用临界值将连续变量二分。在其原始尺度上使用连续变量具有优势，因为信息不会被丢弃，结果预测准确性不会丧失，并且可以促进个性化医疗。
鼓励研究人员和临床医生使用连续变量分析和解释测试和测量的结果，避免依赖单一临界值来指导决策。以自然形式使用连续变量时，可以更准确地预测损伤风险。更准确的风险预测将有助于采取个性化方法减轻损伤风险，并可能导致损伤率下降。
5级。
