Mercy Virtual Care Center, 15740 S. Outer Forty, Chesterfield, MO 63017, USA.
Mercy Virtual Care Center, 15740 S. Outer Forty, Chesterfield, MO 63017, USA; Department of Data Science, Maryville University, 650 Maryville University Drive, St. Louis, MO 63141, USA.
J Crit Care. 2018 Dec;48:339-344. doi: 10.1016/j.jcrc.2018.08.041. Epub 2018 Sep 12.
To determine the efficacy of survival analysis for predicting septic shock onset in ICU patients.
We performed a retrospective analysis on ICU cases from Mercy Hospital St. Louis from 2012 to 2016. As part of the procedure for inclusion in the Apache Outcomes database, each case is reviewed by critical care clinicians to identify septic shock patients as well as the time of septic shock onset. We used survival analysis to predict septic shock onset in these cases and employed lagging to compensate for uncertainties in septic shock onset time.
Survival analysis was highly effective at predicting septic shock onset, producing AUC values of >0.87. The methodology was robust to lag times as well as the specific method of survival analysis used.
This methodology has the potential to be implemented in the ICU for real time prediction and can be used as a building block to expand the approach to other hospital wards or care environments.
确定生存分析在预测 ICU 患者脓毒症休克发作中的效果。
我们对 2012 年至 2016 年密西西比州圣路易斯 Mercy 医院的 ICU 病例进行了回顾性分析。作为 Apache Outcomes 数据库纳入程序的一部分，每位患者都由重症监护临床医生进行审查，以确定脓毒症休克患者以及脓毒症休克发作的时间。我们使用生存分析来预测这些病例中的脓毒症休克发作，并使用滞后来补偿脓毒症休克发作时间的不确定性。
生存分析在预测脓毒症休克发作方面非常有效，产生的 AUC 值大于 0.87。该方法对滞后时间以及使用的特定生存分析方法都具有稳健性。
该方法有可能在 ICU 中实时预测，并且可以用作扩展该方法到其他病房或护理环境的构建块。
