Jain Sukrit S, Sarkar Indra Neil, Stey Paul C, Anand Rajsavi S, Biron Dustin R, Chen Elizabeth S
Alpert Medical School and Center for Biomedical Informatics, Brown University, Providence, RI.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1319-1328. eCollection 2018.
Recognizing factors associated with mortality in patients admitted to the ICU with acute exacerbation of chronic obstructive pulmonary disease could reduce healthcare costs and improve end-of-life care. Previous studies have identified possible predictive variables, but analysis is lacking on the combined effect of demographic factors and comorbidities. Using the MIMIC-III database, this study examined factors associated with mortality in a model incorporating comorbidities, comorbidity indices, and demographic factors. After determining associations between predictive variables and mortality through univariate and multivariate binomial logistic regression, three predictive models were developed: (1) univariate GLM-derived logistic, (2) Mean Gini-derived logistic (MGDL), and (3) random forest. The MGDL model best predicted mortality with an AUROC of 0.778. Variables with the greatest relative importance in determining mortality included the Charlson Comorbidity Index, Elixhauser Index, male, and arrhythmia. The results support the potential of using the MGDL model and need for further work in exploring demographic factors.
识别因慢性阻塞性肺疾病急性加重而入住重症监护病房(ICU)患者的死亡相关因素,可降低医疗成本并改善临终护理。以往研究已确定了可能的预测变量,但缺乏对人口统计学因素和合并症综合影响的分析。本研究利用MIMIC-III数据库,在一个纳入合并症、合并症指数和人口统计学因素的模型中,研究了与死亡相关的因素。通过单变量和多变量二项逻辑回归确定预测变量与死亡率之间的关联后,开发了三个预测模型:(1)单变量广义线性模型(GLM)衍生的逻辑模型,(2)平均基尼系数衍生的逻辑模型(MGDL),以及(3)随机森林模型。MGDL模型对死亡率的预测效果最佳,曲线下面积(AUROC)为0.778。在确定死亡率方面相对重要性最大的变量包括查尔森合并症指数、埃利克斯豪泽指数、男性和心律失常。研究结果支持使用MGDL模型的潜力,并表明需要进一步研究人口统计学因素。