Sepsis-induced acute respiratory failure (ARF) is a serious complication with
a poor prognosis. This paper presents a deep representation learningbased
phenotyping method to identify distinct groups of clinical trajectories of
septic patients with ARF. For this retrospective study, we created a dataset
from electronic medical records (EMR) consisting of data from sepsis patients
admitted to medical intensive care units who required at least 24 hours of
invasive mechanical ventilation at a quarternary care academic hospital in
southeast USA for the years 2016-2021. A total of N=3349 patient encounters
were included in this study. Clustering Representation Learning on Incomplete
Time Series Data (CRLI) algorithm was applied to a parsimonious set of EMR
variables in this data set. To validate the optimal number of clusters, the
K-means algorithm was used in conjunction with dynamic time warping. Our model
yielded four distinct patient phenotypes that were characterized as liver
dysfunction/heterogeneous, hypercapnia, hypoxemia, and multiple organ
dysfunction syndrome by a critical care expert. A Kaplan-Meier analysis to
compare the 28-day mortality trends exhibited significant differences (p <
0.005) between the four phenotypes. The study demonstrates the utility of our
deep representation learning-based approach in unraveling phenotypes that
reflect the heterogeneity in sepsis-induced ARF in terms of different mortality
outcomes and severity. These phenotypes might reveal important clinical
insights into an effective prognosis and tailored treatment strategies.

通过深度表示学习的方法，该研究揭示了散发性与急性呼吸衰竭的临床轨迹的不同群体，并对不同的死亡率和严重程度进行了特征化，这将为有效的预后和个体化的治疗策略提供重要的临床见解。