Author(s): Jean-Louis Vincent1
Intensive care medicine is still a relatively young specialty but in its short lifetime has evolved rapidly with huge advances in technology and understanding of disease pathogenesis and processes. However, progress in therapeutics has been much less obvious. The fact that for decades we have enrolled heterogeneous, poorly characterized patient groups into our clinical trials goes a long way to explaining why we still have no new therapies, notably for sepsis; the sepsis response is so complex and personal that no single agent will be effective in all patients with sepsis. Now, as a result of advances in technology, greater comprehension of disease pathogenesis and pathophysiology, new understanding of biochemical and hematological data, novel genomic, proteomic, and metabolomic techniques, and improved data mining and computational modeling, we have begun to be able to characterize critically ill patients more precisely, moving beyond the global nonspecific syndromic groupings of the past (e.g., "systemic inflammatory response syndrome (SIRS)", "sepsis", "acute respiratory distress syndrome (ARDS)") to more detailed classification and characterization at an individual patient level. This approach will enable us to determine on a more personal level which treatments will be best adapted to each patient, thus maximizing his/her chances of survival.
From "poorly characterized" to "personalized" medicine
Although patients are individuals, traditionally we have tended to "label" them according to their disease or condition and often treated them accordingly, using similar interventions and therapies for all patients with the same "diagnosis". Indeed, this has been one of the key problems with randomized controlled trials in critically ill patients--particularly those with sepsis--in which interventions have been tested in poorly characterized groups of patients believed to be similar because they meet a specific definition or have a specific diagnosis, but in fact varying markedly at an individual level with different infecting organisms, durations of disease, degrees of immune response, comorbidities, and so forth [1-3]. The results of such trials have not surprisingly been mostly negative. However, for many of these studies that showed no overall efficacy on outcome, later analyses suggested that the intervention may have been effective in specific subgroups of patients. For example, Man et al.  used whole genome amplification on samples from patients in the Protein C Worldwide Evaluation in Severe Sepsis (PROWESS) study  and identified genetic biomarkers that identified subgroups of patients with a greater response to drotrecogin alfa (activated). Similarly, Shakoory et al.  recently analyzed data from a randomized controlled trial of an interleukin (IL)-1 receptor antagonist that had shown no overall effect on outcome and identified a subgroup of patients with so-called macrophage activation syndrome (sepsis plus hepatobiliary dysfunction/disseminated intravascular coagulation) in which the mortality rate was significantly reduced with the intervention compared to placebo (hazard ratio for death 0.28 (95% confidence interval 0.11-0.71); p = 0.0071). Being able to better characterize patients will enable us to identify such subgroups, enabling interventions to be tested in more targeted populations and treatments to be personalized to a much greater extent than is currently...
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