Sepsis Subclasses: Be Careful of What You Wish for*

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Similar to many conditions encountered in the ICU, sepsis is a heterogeneous syndrome, rather than a uniform disease. It follows that there exist categories of sepsis defined by clinical features, physiology, biology, and/or genetics. Such categories are denoted by various labels, including “subphenotypes,” “subclasses,” “subgroups,” and “endotypes” (1). For convenience, we use the term “subclass” from this point forward.
The clinical utility of subclass identification is to inform therapy. Indeed, we already care for patients with sepsis in this manner. The subclass of patients with sepsis due to Gram-positive bacteria receives an antibiotic regimen different to that of the subclass with Gram-negative bacteria. Similarly, those with septic shock primarily characterized by low vascular resistance receive cardiovascular support that is different to those with septic shock primarily due to myocardial dysfunction. These are fundamental and effective subclassification strategies, but the field can move toward more granular and sophisticated strategies commensurate with the complex biology of sepsis (2).
When evaluating subclassification strategies, it is important to consider the approach to subclassification (3). Beyond the initial derivation of the subclassification strategy, it is imperative to validate the strategy in an independent cohort, wherein the strategy is applied a priori, without modifications, and then one determines whether the subclassification yields similar observations to that seen in the initial derivation phase. Additionally, the derivation and validation phases of the strategy require large numbers of study subjects, preferably from multiple centers, in order to capture patient heterogeneity.
One approach to deriving a subclassification strategy is “knowledge-based.” In this approach, the investigator creates subclasses and membership criteria based on existing knowledge and paradigms, and subsequently tests whether the subclasses associate with outcomes or treatment responses. The advantages of this approach are that it is based on the traditional scientific method and therefore tends to be hypothesis-driven, and can often take advantage of readily available clinical and biological data. General familiarity with this approach also leads to acceptance by the medical community. The disadvantages of a knowledge-based approach are that it is limited by existing knowledge, it can be based on flawed paradigms, and it is highly susceptible to investigator bias.
In this issue of Pediatric Critical Care Medicine, Carcillo et al (4) report on inflammation-based subclasses of sepsis and their association with multiple organ failure outcome. The investigators used a knowledge-based approach. Accordingly, we suggest that the readership considers the data presented in the context described above. That is, consider the important issues of derivation, validation, and study subject number, and the nuances of a knowledge-based approach. We also suggest that the readership considers the vast complexity of inflammation and immune function, and whether this complexity can be reliably captured by the laboratory assays employed in this study. Appropriately, Carcillo et al (4) stress that their study should not be interpreted as explicitly advocating for the use of Rituximab, Eculizumab, Anakinra, or plasma exchange for pediatric sepsis. We strongly agree with this qualification of the data. With regard to plasma exchange, it is disappointing that a trial was conducted to evaluate this therapy among children with thrombocytopenia associated multiple organ failure, including those with sepsis, but the results have not been published; nor are there any results posted at the ClinicalTrials.gov website despite a trial completion date of February 2012 (NCT00118664).
An alternative approach to subclass identification is “discovery-based.” In this data-driven approach, the investigator makes no a priori assumptions regarding subclass characteristics or membership. The approach typically leverages high throughput technologies such as transcriptomics, proteomics, and metabolomics, or other forms of high-dimensional data, and relies on complex bioinformatics and machine-learning tools to identify subclasses in an unsupervised manner.

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