Sepsis Prediction: Biomarkers Combined in a Bayesian Approach

TL;DR

This study uses a Bayesian model combining biomarkers to predict sepsis in children after heart surgery.

Contribution

A novel Bayesian network model integrating sTREM-1, CRP, and leukocyte count for sepsis prediction is proposed.

Findings

sTREM-1 levels were significantly higher in sepsis-diagnosed patients (394.58 pg/mL) compared to non-sepsis patients (239.93 pg/mL).

The Bayesian model achieved 100% probability of sepsis with specific thresholds for CRP, leukocyte count, and sTREM-1.

The model showed promise for diagnosing sepsis using a combination of biomarkers.

Abstract

Sepsis is a serious public health problem. sTREM-1 is a marker of inflammatory and infectious processes that has the potential to become a useful tool for predicting the evolution of sepsis. A prediction model for sepsis was constructed by combining sTREM-1, CRP, and a leukogram via a Bayesian network. A translational study carried out with 32 children with congenital heart disease who had undergone surgical correction at a public referral hospital in Northeast Brazil. In the postoperative period, the mean value of sTREM-1 was greater among patients diagnosed with sepsis than among those not diagnosed with sepsis (394.58 pg/mL versus 239.93 pg/mL, p < 0.001). Analysis of the ROC curve for sTREM-1 and sepsis revealed that the area under the curve was 0.761, with a 95% CI (0.587–0.935) and p = 0.013. With the Bayesian model, we found that a 100% probability of sepsis was related to postoperative blood concentrations of CRP above 71 mg/dL, a leukogram above 14,000 cells/μL, and sTREM-1 concentrations above the cutoff point (283.53 pg/mL). The proposed model using the Bayesian network approach with the combination of CRP, leukocyte count, and postoperative sTREM-1 showed promise for the diagnosis of sepsis.

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