Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, Washington, United States.
Appl Clin Inform. 2020 May;11(3):387-398. doi: 10.1055/s-0040-1710525. Epub 2020 May 27.
Early detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.
To understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality.
We performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression ( = 2), logistic regression ( = 5), support vector machines ( = 4), and Markov models ( = 4), as well as population (range: 24-198,833) and feature size (range: 2-285). Many of the studies used local data sets of varying sizes and locations while others used the publicly available Medical Information Mart for Intensive Care data. Additionally, vital signs or laboratory test results were commonly used as features for training and testing purposes; however, a few used more unique features including gene expression data from blood plasma and unstructured text and data from clinician notes.
Overall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.
早期发现和有效管理脓毒症对于提高医疗质量、效果和成本至关重要。由于脓毒症成本高、发病率高,因此它是各机构的重点关注领域,近年来出现了许多使用不同模型或新型机器学习技术来早期检测脓毒症或与脓毒症相关的潜在死亡率的研究。
了解脓毒症患者的预测分析解决方案,无论是在早期发现发病还是死亡率方面。
我们进行了系统的叙述性综述,并在使用预测分析解决方案的脓毒症患者研究中,确定了他们的方法和结果之间的共同和独特特征。在回顾了 148 篇检索到的论文后,总共分析了 31 篇合格论文,这些论文的模型存在差异,包括线性回归(=2)、逻辑回归(=5)、支持向量机(=4)和马尔可夫模型(=4),以及人群(范围:24-198833)和特征大小(范围:2-285)。许多研究使用了不同大小和位置的本地数据集,而其他研究则使用了公开的医疗信息集市重症监护数据。此外,生命体征或实验室测试结果通常用作训练和测试目的的特征;然而,有一些研究使用了更独特的特征,包括来自血浆的基因表达数据以及非结构化文本和来自临床医生笔记的数据。
总体而言,我们发现脓毒症患者预测分析工具的领域存在差异,从特征和人群规模到方法或算法的选择。所使用的算法或方法的可转移性和通用性仍然存在限制。然而,实施预测分析工具显然有助于早期发现脓毒症或与脓毒症相关的死亡。由于这些研究大多是回顾性的,因此应该进一步研究在不同病房的真实环境中的转化价值。