Yeh Pete, Pan Yiheng, Sanchez-Pinto L Nelson, Luo Yuan
Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Dept. of Elect. Eng. and Comp. Sci., Northwestern University, Evanston, IL, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2019 Nov;2019:1703-1707. doi: 10.1109/bibm47256.2019.8982933. Epub 2020 Feb 6.
Elevated serum chloride levels (hyperchloremia) and the administration of intravenous (IV) fluids with high chloride content have both been associated with increased morbidity and mortality in certain subgroups of critically ill patients, such as those with sepsis. Here, we demonstrate this association in a general intensive care unit (ICU) population using data from the Medical Information Mart for Intensive Care III (MIMIC-III) database and propose the use of supervised learning to predict hyperchloremia in critically ill patients. Clinical variables from records of the first 24h of adult ICU stays were represented as features for four predictive supervised learning classifiers. The best performing model was able to predict second-day hyperchloremia with an AUC of 0.80 and a ratio of 5 false alerts for every true alert, which is a clinically-actionable rate. Our results suggest that clinicians can be effectively alerted to patients at risk of developing hyperchloremia, providing an opportunity to mitigate this risk and potentially improve outcomes.
血清氯水平升高（高氯血症）以及给予高氯含量的静脉输液（IV）均与某些重症患者亚组（如脓毒症患者）的发病率和死亡率增加有关。在此，我们利用重症监护医学信息集市III（MIMIC-III）数据库的数据，在普通重症监护病房（ICU）人群中证实了这种关联，并提出使用监督学习来预测重症患者的高氯血症。成年ICU患者入住后首个24小时记录中的临床变量被用作四个预测性监督学习分类器的特征。表现最佳的模型能够以0.80的曲线下面积（AUC）预测次日高氯血症，每一个真实警报对应5个误报，这是一个具有临床可操作性的比率。我们的结果表明，临床医生可以有效地得到关于有发生高氯血症风险患者的警报，从而有机会降低这种风险并可能改善治疗结果。
