Abstract
Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765–0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.
Highlights
Improved risk stratification and prognosis prediction in sepsis is a critical unmet need
Improved accuracy in sepsis prognosis would improve clinical care through appropriate matching of patients with resources: the very sick can be diverted to intensive care unit (ICU) for maximal intervention, while patients predicted to have a better outcome may be safely watched in the hospital ward or discharged early
Three different teams (Duke University, Sage Bionetworks, and Stanford University) performed separate analyses using the same input data; we sampled the possible model space to determine whether output performance is a function of analytical approaches (Fig. 1)
Summary
Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis. Current tools for risk stratification include clinical severity scores such as APACHE or SOFA as well as blood lactate levels While these measures assess overall illness severity, they do not adequately quantify the patient’s dysregulated response to the infection and fail to achieve the personalization necessary to improve sepsis care[7]. Three scientific groups were invited to build models to predict 30-day mortality based on gene expression profiles These three groups produced four different prognostic models, which were validated in external validation cohorts composed of patients with either communityacquired sepsis or hospital-acquired infections (HAIs)
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