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IMPORTANCE: Coronavirus disease 2019 patients have an increased risk of thrombotic complications that may reflect immunothrombosis, a process characterized by blood clotting, endothelial dysfunction, and the release of neutrophil extracellular traps. To date, few studies have investigated longitudinal changes in immunothrombosis biomarkers in these patients. Furthermore, how these longitudinal changes differ between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia are unknown. OBJECTIVES: In this pilot observational study, we investigated the utility of immunothrombosis biomarkers for distinguishing between coronavirus disease 2019 patients and noncoronavirus disease septic patients with pneumonia. We also evaluated the utility of the biomarkers for predicting ICU mortality in these patients. DESIGN SETTING AND PARTICIPANTS: The participants were ICU patients with coronavirus disease 2019 (n = 14), noncoronavirus disease septic patients with pneumonia (n = 19), and healthy age-matched controls (n = 14). MAIN OUTCOMES AND MEASURES: Nine biomarkers were measured from plasma samples (on days 1, 2, 4, 7, 10, and/or 14). Analysis was based on binomial logit models and receiver operating characteristic analyses. RESULTS: Cell-free DNA, d-dimer, soluble endothelial protein C receptor, protein C, soluble thrombomodulin, fibrinogen, citrullinated histones, and thrombin-antithrombin complexes have significant powers for distinguishing coronavirus disease 2019 patients from healthy individuals. In comparison, fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have significant powers for distinguishing coronavirus disease 2019 from pneumonia patients. The predictors of ICU mortality differ between the two patient groups: soluble thrombomodulin and citrullinated histones for coronavirus disease 2019 patients, and protein C and cell-free DNA or fibrinogen for pneumonia patients. In both patient groups, the most recent biomarker values have stronger prognostic value than their ICU day 1 values. CONCLUSIONS AND RELEVANCE: Fibrinogen, soluble endothelial protein C receptor, antithrombin, and cell-free DNA have utility for distinguishing coronavirus disease 2019 patients from noncoronavirus disease septic patients with pneumonia. The most important predictors of ICU mortality are soluble thrombomodulin/citrullinated histones for coronavirus disease 2019 patients, and protein C/cell-free DNA for noncoronavirus disease pneumonia patients. This hypothesis-generating study suggests that the pathophysiology of immunothrombosis differs between the two patient groups.
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To determine if a set of time-varying biological indicators can be used to: 1) predict the sepsis mortality risk over time and 2) generate mortality risk profiles. DESIGN: Prospective observational study. SETTING: Nine Canadian ICUs. SUBJECTS: Three-hundred fifty-six septic patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical data and plasma levels of biomarkers were collected longitudinally. We used a complementary log-log model to account for the daily mortality risk of each patient until death in ICU/hospital, discharge, or 28 days after admission. The model, which is a versatile version of the Cox model for gaining longitudinal insights, created a composite indicator (the daily hazard of dying) from the "day 1" and "change" variables of six time-varying biological indicators (cell-free DNA, protein C, platelet count, creatinine, Glasgow Coma Scale score, and lactate) and a set of contextual variables (age, presence of chronic lung disease or previous brain injury, and duration of stay), achieving a high predictive power (conventional area under the curve, 0.90; 95% CI, 0.86-0.94). Including change variables avoided misleading inferences about the effects of day 1 variables, signifying the importance of the longitudinal approach. We then generated mortality risk profiles that highlight the relative contributions among the time-varying biological indicators to overall mortality risk. The tool was validated in 28 nonseptic patients from the same ICUs who became septic later and was subject to 10-fold cross-validation, achieving similarly high area under the curve. CONCLUSIONS: Using a novel version of the Cox model, we created a prognostic tool for septic patients that yields not only a predicted probability of dying but also a mortality risk profile that reveals how six time-varying biological indicators differentially and longitudinally account for the patient's overall daily mortality risk.
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INTRODUCTION: Although sepsis is the leading cause of death in noncoronary critically ill patients, identification of patients at high risk of death remains a challenge. In this study, we examined the incremental usefulness of adding multiple biomarkers to clinical scoring systems for predicting intensive care unit (ICU) mortality in patients with severe sepsis. METHODS: This retrospective observational study used stored plasma samples obtained from 80 severe sepsis patients recruited at three tertiary hospital ICUs in Hamilton, Ontario, Canada. Clinical data and plasma samples were obtained at study inclusion for all 80 patients, and then daily for 1 week, and weekly thereafter for a subset of 50 patients. Plasma levels of cell-free DNA (cfDNA), interleukin 6 (IL-6), thrombin, and protein C were measured and compared with clinical characteristics, including the primary outcome of ICU mortality and morbidity measured with the Multiple Organ Dysfunction (MODS) score and Acute Physiology and Chronic Health Evaluation (APACHE) II scores. RESULTS: The level of cfDNA in plasma at study inclusion had better prognostic utility than did MODS or APACHE II scores, or the biomarkers measured. The area under the receiver operating characteristic (ROC) curves for cfDNA to predict ICU mortality is 0.97 (95% CI, 0.93 to 1.00) and to predict hospital mortality is 0.84 (95% CI, 0.75 to 0.94). We found that a cfDNA cutoff value of 2.35 ng/µl had a sensitivity of 87.9% and specificity of 93.5% for predicting ICU mortality. Sequential measurements of cfDNA suggested that ICU mortality may be predicted within 24 hours of study inclusion, and that the predictive power of cfDNA may be enhanced by combining it with protein C levels or MODS scores. DNA-sequence analyses and studies with Toll-like receptor 9 (TLR9) reporter cells suggests that the cfDNA from sepsis patients is host derived. CONCLUSIONS: These studies suggest that cfDNA provides high prognostic accuracy in patients with severe sepsis. The serial data suggest that the combination of cfDNA with protein C and MODS scores may yield even stronger predictive power. Incorporation of cfDNA in sepsis risk-stratification systems may be valuable for clinical decision making or for inclusion into sepsis trials.