Here it is: sepsis writ Big Data.
And, considering it’s Big Data, it’s also a big publication: a 15 page primary publication, plus 90+ pages of online supplement – dense with figures, raw data, and methods both routine and novel for the evaluation of large data sets.
At the minimum, to put a general handle on it, this work primarily demonstrates the heterogeneity of sepsis. As any clinician knows, “sepsis” – with its ever-morphing definition – ranges widely from those generally well in the Emergency Department to those critically ill in the Intensive Care Unit. In an academic sense, this means the patients enrolled and evaluated in various trials for the treatment of sepsis may be quite different from one another, and results seen in one trial or setting may generalize poorly to another. This has obvious implications when trying to determine a general set of care guidelines from these disparate bits of data, and resulting in further issues down the road when said guidelines become enshrined in quality measures.
Overall, these authors ultimately define four phenotypes of sepsis, helpfully assigned descriptive labels using the letters of the greek alphabet. These four phenotypes of sepsis are derived from retrospective administrative data, then validated on additional retrospective administrative data, and finally the raw data from several prominent clinical trials in sepsis, including ACCESS, PROWESS, and ProCESS. The four phenotypes were derived by clustering and refinement, and are described by the authors as effectively: a mild type with low mortality; a cohort of those with chronic illness; a cohort with systemic inflammation and pulmonary disease; and a final cohort with liver dysfunction, shock, and high mortality.
We are quite far, however, from needing to apply these phenotypes in a clinical fashion. Any classification model is highly dependent upon the inputs, and in this study the inputs are the sorts of routine clinical data available from the electronic health record: vital signs, demographics, and basic labs. Missing data was common, including, for example, lactate levels, which was not obtained on 80% of patients in their model. These inputs then dictate how many different clusters you obtain, how the relative accuracy of classification diminishes with greater numbers of clusters, as well whether the model begins to overfit the derivation data set.
Then, this is a little bit of a fuzzy application in the sense these data represent as much different types of patients with sepsis, as it represents different types of sepsis. Consider the varying etiologies of sepsis, including influenza pneumonia, streptococcal toxic shock, or gram-negative bacteremia. These different etiologies would obviously result in different host responses depending on individual patient features. These phenotypes derived here effectively mash up causative agent with the underlying host, muddying clinical application.
If clinical utility is limited, then what might the best utility for this work? Well, this goes back to the idea above regarding translating work from clinical trials to different settings. A community Emergency Department might primarily see alpha-sepsis, a community ICU might see a lot of beta-sepsis, while an academic ICU might see predominantly delta-sepsis. These are important concepts to consider – and potentially subgroup-analyses to perform – when evaluating the outcomes of clinical trials. These authors do several simulations of clinical trials while varying the composition of phenotypes of sepsis, and note potentially important effects on primary outcomes. Pathways of care or resuscitation protocols could potentially be more readily compared between trial populations if these phenotypes were calculated.
This is a challenging work to process – but an important first step in better recognizing the heterogeneity in potential benefits and harms resulting from various interventions. The accompanying editorial does really a very excellent job of describing their methods, outcomes, and utility, as well.
“Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis”
https://jamanetwork.com/journals/jama/fullarticle/2733996
“New Phenotypes for Sepsis”
https://jamanetwork.com/journals/jama/fullarticle/2733994
They note alpha sepsis has a better response to EGDT, is this jsut Bc they are less sick, or is it statistical noise?
Does this give some credence to the idea that it’s not the underlying infection that matters, but rather the bodies response?
Are you at all worried that this may lead to an expansion of test that get ordered so people can look at septic patients through the lenses of these phenotypes, or is this sooooo not ready for prime time that we don’t have to worry?
These simulations are like another full step beyond a subgroup analysis with respect to hypothesis generation.
These data have implications primarily for those designing trials in sepsis, how to recruit, how to balance randomization, which study procedures might apply to certain patients etc. We’re years away from running a biomarker panel on potentially septic patients to determine the best treatment – and I’d expect when we do, it’ll include the immunologic activation biomarkers combined with some of the physiologic variables.
… and when that time comes, I’m sure I’ll be raging at the use of these panels when 40% of our patients are low-risk sepsis where clinical judgment is every bit as good as the panel (let alone the panels that might be run on patients with just _potential_ sepsis)!