Combined Biomarkers Predict Acute Mortality Among Critically Ill Patients With Suspected Sepsis

Abstract

Objectives: Sepsis is associated with high early and total in-hospital mortality. Despite recent revisions in the diagnostic criteria for sepsis that sought to improve predictive validity for mortality, it remains difficult to identify patients at greatest risk of death. We compared the utility of nine biomarkers to predict mortality in subjects with clinically suspected bacterial sepsis.

Design: Cohort study.

Setting: The medical and surgical ICUs at an academic medical center.

Subjects: We enrolled 139 subjects who met two or more systemic inflammatory response syndrome (systemic inflammatory response syndrome) criteria and received new broad-spectrum antibacterial therapy.

Interventions: We assayed nine biomarkers (α-2 macroglobulin, C-reactive protein, ferritin, fibrinogen, haptoglobin, procalcitonin, serum amyloid A, serum amyloid P, and tissue plasminogen activator) at onset of suspected sepsis and 24, 48, and 72 hours thereafter. We compared biomarkers between groups based on both 14-day and total in-hospital mortality and evaluated the predictive validity of single and paired biomarkers via area under the receiver operating characteristic curve.

Measurements and main results: Fourteen-day mortality was 12.9%, and total in-hospital mortality was 29.5%. Serum amyloid P was significantly lower (4/4 timepoints) and tissue plasminogen activator significantly higher (3/4 timepoints) in the 14-day mortality group, and the same pattern held for total in-hospital mortality (Wilcoxon p ≤ 0.046 for all timepoints). Serum amyloid P and tissue plasminogen activator demonstrated the best individual predictive performance for mortality, and combinations of biomarkers including serum amyloid P and tissue plasminogen activator achieved greater predictive performance (area under the receiver operating characteristic curve > 0.76 for 14-d and 0.74 for total mortality).

Conclusions: Combined biomarkers predict risk for 14-day and total mortality among subjects with suspected sepsis. Serum amyloid P and tissue plasminogen activator demonstrated the best discriminatory ability in this cohort.

Figure 1. Longitudinal biomarker change from time of suspected sepsis and 14-day mortality

LOESS regression was performed for the 9 biomarkers using the actual time of sample collection, with fitting performed by a redescending M-estimator (Tukey’s biweight function) to minimize the impact of outliers. Biomarker values were compared between acute 14-day mortality groups. The line indicates the point estimate at each timepoint, and the grey shading indicates the 95% confidence interval. In both analyses, SAP and TPA demonstrated the best separation between mortality versus no mortality.

Figure 2. Longitudinal biomarker change from time of suspected sepsis and in-hospital mortality

LOESS regression was performed for the 9 biomarkers using the actual time of sample collection, with fitting performed by a redescending M-estimator (Tukey’s biweight function) to minimize the impact of outliers. Biomarker values were compared between total in-hospital mortality groups. The line indicates the point estimate at each timepoint, and the grey shading indicates the 95% confidence interval. In both analyses, SAP and TPA demonstrated the best separation between mortality versus no mortality.

Figure 3. Biomarker combinations to discriminate acute and total in-hospital mortality

The color intensity shows the AUROC achieved by logistic regression models based on each pairwise combination of biomarkers. (A) The intensity of orange shows the AUROC for acute (14-day) mortality. (B) The intensity of blue shows the AUROC for total in-hospital mortality. Each model was evaluated with an interaction term (upper triangle) and without an interaction term (lower triangle). The matrix diagonal depicts the AUROC for single biomarkers. Combinations with SAP and TPA improved the AUROCs for single biomarkers.