Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome

Abstract

Introduction: Sepsis is a devastating condition that is generally treated as a single disease. Identification of meaningfully distinct clusters may improve research, treatment and prognostication among septic patients. We therefore sought to identify clusters among patients with severe sepsis or septic shock.

Methods: We retrospectively studied all patients with severe sepsis or septic shock admitted directly from the emergency department to the intensive care units (ICUs) of three hospitals, 2006-2013. Using age and Sequential Organ Failure Assessment (SOFA) subscores, we defined clusters utilizing self-organizing maps, a method for representing multidimensional data in intuitive two-dimensional grids to facilitate cluster identification.

Results: We identified 2533 patients with severe sepsis or septic shock. Overall mortality was 17 %, with a mean APACHE II score of 24, mean SOFA score of 8 and a mean ICU stay of 5.4 days. Four distinct clusters were identified; (1) shock with elevated creatinine, (2) minimal multi-organ dysfunction syndrome (MODS), (3) shock with hypoxemia and altered mental status, and (4) hepatic disease. Mortality (95 % confidence intervals) for these clusters was 11 (8-14), 12 (11-14), 28 (25-32), and 21 (16-26) %, respectively (p < 0.0001). Regression modeling demonstrated that the clusters differed in the association between clinical outcomes and predictors, including APACHE II score.

Conclusions: We identified four distinct clusters of MODS among patients with severe sepsis or septic shock. These clusters may reflect underlying pathophysiological differences and could potentially facilitate tailored treatments or directed research.

Fig. 1

Study population, with subjects excluded due to missing data. Asterisk two patients were missing more than one value

Fig. 2

Kohonen Self-Organizing Maps. The maps depict both overall clusters and individual nodes (circles) to show the internal patterns within clusters. Nodes represent smaller groupings of patients: each node contains 0–20 patients who are extremely similar to each other. The four clusters are divided by black lines and depicted in (a). b–f Show those same clusters, but with depictions of different attributes of the nodes within each cluster. Within each node, the given value is represented by the darkness of the colour in the node. Each node is shaded from white to dark green, where darker colours represent higher average values (e.g., higher SOFA subscore) among the patients in the given node. The patterns visible in (b–f) suggest that the four clusters represent: (1) shock with elevated creatinine, (2) minimal multiple organ dysfunction syndrome, (3) shock with hypoxemia and altered mental status, and (4) hepatic disease

Fig. 3

Distribution of variables not employed in clustering, across the nodes within each cluster. The clusters depicted here are the same as those depicted in Fig. 2. Each node on the map represents a group of patients and the value displayed represents the mean value of those patients. Within each node, the given value is represented by the darkness of the colour in the node. Each node is shaded from white to dark green, where darker colours represent higher average values (e.g., proportion of patients in that node with urinary infection as the cause of sepsis) among the patients in the given node. A cluster of patients with renal comorbidities is identified (d), which spreads over three clusters. In the pneumonia grid (e), two broad types are apparent: pneumonia with septic shock (f mainly in cluster 3) and pneumonia with severe sepsis (b mainly in cluster 2)