Clinical prediction models in anesthesia and surgery research have many clinical applications including preoperative risk stratification with implications for clinical utility in decision-making, resource utilization, and costs. It is imperative that predictive algorithms and multivariable models are validated in a suitable and comprehensive way in order to establish the robustness of the model in terms of accuracy, predictive ability, reliability, and generalizability. The purpose of this article is to educate anesthesia researchers at an introductory level on important statistical concepts involved with development and validation of multivariable prediction models for a binary outcome. Methods covered include assessments of discrimination and calibration through internal and external validation. An anesthesia research publication is examined to illustrate the process and presentation of multivariable prediction model development and validation for a binary outcome. Properly assessing the statistical and clinical validity of a multivariable prediction model is essential for reassuring the generalizability and reproducibility of the published tool.

中文翻译：

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临床预测模型的统计开发和验证。

麻醉和手术研究中的临床预测模型有许多临床应用，包括术前风险分层，对决策、资源利用和成本的临床效用产生影响。必须以适当和全面的方式验证预测算法和多变量模型，以建立模型在准确性、预测能力、可靠性和通用性方面的稳健性。本文的目的是对麻醉研究人员进行介绍性教育，了解与二元结果的多变量预测模型的开发和验证相关的重要统计概念。涵盖的方法包括通过内部和外部验证对歧视和校准进行评估。对麻醉研究出版物进行了检查，以说明多变量预测模型开发和二元结果验证的过程和演示。正确评估多变量预测模型的统计和临床有效性对于确保已发布工具的普遍性和再现性至关重要。