Cichosz Simon Lebech, Johansen Mette Dencker, Hejlesen Ole
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
J Diabetes Sci Technol. 2015 Oct 14;10(1):27-34. doi: 10.1177/1932296815611680.
Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.
糖尿病是医学科学和医疗保健管理的首要重点之一，关于这些患者有大量的数据和信息。无论数据来自统计模型还是复杂的模式识别模型，它们都可以融合到结合患者信息和预后结果的预测模型中。这些知识可用于临床决策支持、疾病监测和公共卫生管理，以改善患者护理。我们的目的是回顾文献，并介绍糖尿病常见短期和长期并发症筛查及管理中的预测模型。已经开发出用于糖尿病及其并发症管理的预测模型，在过去十年中，关于此类模型的出版物数量一直在增加。通常使用多元逻辑回归或类似的线性回归来开发预测模型，这可能是由于其功能透明。最终，要使预测模型证明有用，它们必须显示出影响，即其使用必须产生更好的患者结果。尽管在构建这些预测模型方面已经付出了巨大努力，但影响研究却非常匮乏。
