Development of an Algorithm to Predict Mortality in Patients With Sepsis and Coagulopathy

Abstract

Sepsis is a systemic response to infection with a high rate of mortality and complex pathophysiology involving inflammation, infection response, hemostasis, endothelium, and platelets. The purpose of this study was to develop an equation incorporating biomarker levels at intensive care unit (ICU) admission to predict mortality in patients with sepsis, based on the hypothesis that a combination of biomarkers representative of multiple physiological systems would provide improved predictive value. Plasma samples and clinical data were collected from 103 adult patients with sepsis at the time of ICU admission. Biomarker levels were measured using commercially available methods. A 28-day mortality was used as the primary end point. Stepwise linear regression modeling was performed to generate a predictive equation for mortality. Differences in biomarker levels between survivors were quantified using the Mann-Whitney test and the area under the receiver operating curve (AUC) was used to describe predictive ability. Significant differences (P < .05) were observed between survivors and nonsurvivors for plasminogen activator inhibitor 1 (AUC ¼ 0.70), procalcitonin (AUC ¼ 0.77), high mobility group box 1 (AUC ¼ 0.67), interleukin (IL) 6 (AUC ¼ 0.70), IL-8 (AUC ¼ 0.70), protein C (AUC ¼ 0.71), angiopoietin-2 (AUC ¼ 0.76), endocan (AUC ¼ 0.58), and platelet factor 4 (AUC ¼ 0.70). A predictive equation for mortality was generated using stepwise linear regression modeling, which incorporated procalcitonin, vascular endothelial growth factor, the IL-6:IL-10 ratio, endocan, and platelet factor 4, and demonstrated a better predictive value for patient outcome than any individual biomarker (AUC ¼ 0.87). The use of mathematical modeling resulted in the development of a predictive equation for sepsis-associated mortality with performance than any individual biomarker or clinical scoring system which incorporated biomarkers representative of multiple systems.

<p>Coagulopathy of Patients with COVID-19 is Associated with Infectious and Inflammatory Markers</p>

“…During the process of fighting against viral or bacterial invasion, immune systems are inevitably activated, thus stimulating the coagulation system. 9,15 Thus, SARS-CoV-2 infection was also observed to cause disturbances of coagulation and immune systems, which are non-negligible issues during the treatment of COVID-19 patients. This study reveals that SARS-CoV-2-associated coagulopathy exists in almost all critically ill COVID-19 patients.…”

The prognostic utility of protein C as a biomarker for adult sepsis: a systematic review and meta-analysis

“…All twelve studies were prospective observational and published between 1993 and 2021. Five studies were conducted in North America [ 28 , 31 , 33 , 35 , 37 ], four in Eastern Asia [ 27 , 29 , 32 ] and three in Europe [ 30 , 34 , 38 ]. The number of participants across studies ranged from 48 to 971 [ 31 , 34 ].…”

“…Protein C levels were measured using two different biochemical techniques. Five studies used antibody-based techniques, such as ELISAs [ 28 , 31 , 33 , 35 , 37 ], while the remainder of studies measured PC using functional clotting assays [ 27 , 29 , 30 , 32 , 35 , 36 ]. All studies reported their PC measurements as a percentage of healthy PC levels, with the exception of Shapiro et al, which reported PC measurements in ug/mL [ 37 ].…”

“…Nine studies examined PC’s ability to predict sepsis-related mortality, with six reporting PC biomarker levels. Of these six studies, three used functional PC assays [ 30 , 35 , 37 ], while three used immunoassays [ 31 , 33 , 34 ]. There was a statistically significant difference between PC levels in survivors and non-survivors (751 patients, SMD 0.52 (95% CI 0.24–0.81), p = 0.0003).…”

“…Five studies examined the difference between septic patients and their control populations (Additional file 1 : Table S6). Three studies used functional PC assays [ 29 , 35 , 38 ] and two used immunoassays [ 34 , 37 ]. These data could not be synthesized for meta-analysis due to the variability in control groups between the studies.…”