Global Health-Health Systems & Policy, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden; Perioperative Medicine & Intensive Care, Karolinska University Hospital, Stockholm, Sweden; Department of Anaesthesia & Intensive Care, Queen Elizabeth Central Hospital, Blantyre, Malawi.
Global Health-Health Systems & Policy, Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Science, Innovation, and Technology, Karolinska Institutet, 171 77 Stockholm, Sweden.
Eur J Intern Med. 2017 Nov;45:37-40. doi: 10.1016/j.ejim.2017.09.012. Epub 2017 Sep 19.
Critical illness is any immediately life-threatening disease or trauma and results in several million deaths globally every year. Responsive hospital systems for managing critical illness include quick and accurate identification of the critically ill patients. Prognostic prediction models are widely used for this aim. To be clinically useful, a model should have good predictive performance, often measured using discrimination and calibration. This is not sufficient though: a model also needs to be tested in the setting where it will be used, it should be user-friendly and should guide decision making and actions. The clinical usefulness and impact on patient outcomes of prediction models has not been greatly studied. The focus of research should shift from attempts to optimise the precision of models to real-world intervention studies to compare the performance of models and their impacts on outcomes.
危重病是指任何可能立即危及生命的疾病或创伤，每年在全球导致数百万人死亡。用于管理危重病的有反应能力的医院系统包括快速准确地识别危重病患者。预后预测模型被广泛用于这一目的。为了具有临床实用性，模型应该具有良好的预测性能，通常使用区分度和校准度来衡量。然而，这还不够：模型还需要在将要使用的环境中进行测试，它应该易于使用，并指导决策和行动。预测模型的临床实用性及其对患者结局的影响尚未得到广泛研究。研究的重点应该从试图优化模型的精度转移到实际的干预研究，以比较模型的性能及其对结果的影响。
