Toma Tudor, Abu-Hanna Ameen, Bosman Robert-Jan
Academic Medical Center, Universiteit van Amsterdam, Department of Medical Informatics, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands.
Artif Intell Med. 2008 May;43(1):47-60. doi: 10.1016/j.artmed.2008.01.002. Epub 2008 Apr 3.
The current established mortality predictive models in the intensive care rely only on patient information gathered within the first 24 hours of admission. Recent research demonstrated the added prognostic value residing in the sequential organ-failure assessment (SOFA) score which quantifies on each day the cumulative patient organ derangement. The objective of this paper is to develop and study predictive models that also incorporate univariate patterns of the six individual organ systems underlining the SOFA score. A model for a given day d predicts the probability of in-hospital mortality.
We use the logistic framework to combine a summary statistic of the historic SOFA information for a patient together with selected dummy variables indicating the occurrence of univariate frequent temporal patterns of individual organ system functioning. We demonstrate the application of our method to a large real-life data set from an intensive care unit (ICU) in a teaching hospital. Model performance is tested in terms of the AUC and the Brier score.
An algorithm for categorization, discovery, and selection of univariate patterns of individual organ scores and the induction of predictive models. The case-study resulted in six daily models corresponding to days 2-7. Their AUC ranged between 0.715 and 0.794 and the Brier scores between 0.161 and 0.216. Models using only admission data but recalibrated for days 2-7 generated AUC ranging between 0.643 and 0.761 and Brier scores ranged between 0.175 and 0.230.
The results show that temporal organ-failure episodes improve predictions' quality in terms of both discrimination and calibration. In addition, they enhance the interpretability of models. Our approach should be applicable to many other medical domains where severity scores and sub-scores are collected.
目前重症监护中已建立的死亡率预测模型仅依赖于入院后24小时内收集的患者信息。近期研究表明，序贯器官衰竭评估（SOFA）评分具有额外的预后价值，该评分可每天量化患者器官功能障碍的累积情况。本文的目的是开发并研究预测模型，这些模型还纳入了构成SOFA评分的六个独立器官系统的单变量模式。给定日期d的模型可预测院内死亡概率。
我们使用逻辑框架，将患者历史SOFA信息的汇总统计数据与选定的虚拟变量相结合，这些虚拟变量表明了各个器官系统功能单变量频繁时间模式的发生情况。我们展示了该方法在一家教学医院重症监护病房（ICU）的大型真实数据集上的应用。通过AUC和Brier评分来测试模型性能。
一种用于分类、发现和选择个体器官评分单变量模式以及归纳预测模型的算法。案例研究得出了与第2至7天相对应的六个每日模型。它们的AUC在0.715至0.794之间，Brier评分在0.161至0.216之间。仅使用入院数据但针对第2至7天重新校准的模型，其AUC在0.643至0.761之间，Brier评分在0.175至0.230之间。
结果表明，器官衰竭的时间性发作在区分度和校准方面均提高了预测质量。此外，它们增强了模型的可解释性。我们的方法应适用于许多其他收集严重程度评分和子评分的医学领域。
