A Multibiomarker-Based Outcome Risk Stratification Model for Adult Septic Shock*

Abstract

Objectives Clinical trials in septic shock continue to fail due, in part, to inequitable and sometimes unknown distribution of baseline mortality risk between study arms. Investigators advocate that interventional trials in septic shock require effective outcome risk stratification. We derived and tested a multibiomarker-based approach to estimate mortality risk in adults with septic shock. Design Previous genome-wide expression studies identified 12 plasma proteins as candidates for biomarker-based risk stratification. The current analysis used banked plasma samples and clinical data from existing studies. Biomarkers were assayed in plasma samples obtained from 341 subjects with septic shock within 24 hours of admission to the ICU. Classification and regression tree analysis was used to generate a decision tree predicting 28-day mortality based on a combination of both biomarkers and clinical variables. The derived tree was first tested in an independent cohort of 331 subjects, then calibrated using all subjects (n = 672), and subsequently validated in another independent cohort (n = 209). Setting Multiple ICUs in Canada, Finland, and the United States. Subjects Eight hundred eighty-one adults with septic shock or severe sepsis. Intervention None. Measurements and Main Results The derived decision tree included five candidate biomarkers, admission lactate concentration, age, and chronic disease burden. In the derivation cohort, sensitivity for mortality was 94% (95% CI, 87–97), specificity was 56% (50–63), positive predictive value was 50% (43–57), and negative predictive value was 95% (89–98). Performance was comparable in the test cohort. The calibrated decision tree had the following test characteristics in the validation cohort: sensitivity 85% (76–92), specificity 60% (51–69), positive predictive value 61% (52–70), and negative predictive value 85% (75–91). Conclusions We have derived, tested, calibrated, and validated a risk stratification tool and found that it reliably estimates the probability of mortality in adults with septic shock.

Data mining-based statistical analysis of biological data uncovers hidden significance: clustering Hashimoto’s thyroiditis patients based on the response of their PBMC with IL-2 and IFN-γ secretion to stimulation with Hsp60

“…Along these lines, we analyzed our data on cytokine secretion, using a nonparametric hypothesis testing, followed by another different approach named classification and regression tree learning algorithm. This is a data mining methodology, used in various scientific disciplines, including, lately, in the biological and medical fields (Richette et al 2014;Wong et al 2014). By applying this nonconventional statistical strategy, we could find in our data information of interest, which had not been evidenced by the conventional approach used earlier [1].…”

Red Blood Cells Induce Necroptosis of Lung Endothelial Cells and Increase Susceptibility to Lung Inflammation

“…Subjects were selected from the Molecular Epidemiology of Severe Sepsis in the ICU cohort study at the University of Pennsylvania (21,22) Figures 1B and 1C). Strikingly, RBC-treated ECs also displayed marked plasma membrane loss and nuclear swelling.…”

Lactate, a useful marker for disease mortality and severity but an unreliable marker of tissue hypoxia/hypoperfusion in critically ill patients

“…6,7 Lactate was recently included in a multibiomarker-based outcome risk model for patients with septic shock. 8 Although hyperlactatemia has been widely recognized as a marker of tissue hypoxia/hypoperfusion, the source, biochemistry, and metabolic functions of lactate remain unclear. Recently, it was clearly suggested that hyperlactatemia could result not only from tissue hypoxia or anaerobic glycolysis but also from increased or accelerated aerobic glycolysis during the stress response.…”