Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
Department of General Practice and Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.
J Clin Epidemiol. 2024 Aug;172:111387. doi: 10.1016/j.jclinepi.2024.111387. Epub 2024 May 8.
Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.
临床预测模型可提供健康结果的风险，从而为患者提供信息并支持医疗决策。然而，大多数模型从未真正在实践中得到实施。这种缺乏实施的常见原因是预测模型通常未经外部验证。虽然我们通常鼓励进行外部验证，但我们认为，在实施之前，外部验证通常既不充分也不必要，不是一个必要的步骤。因此，任何可用的外部验证都不应被视为模型实施的许可证。我们通过讨论关于外部验证的 3 个常见误解来澄清这一论点。我们认为，不存在一种推荐的验证设计，外部验证并非总是必要的，有时还需要多次外部验证。本文的观点可以帮助读者考虑、设计、解释和欣赏外部验证研究。
