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BACKGROUND: The clinical course of COVID-19 patients ranges from asymptomatic infection, via mild and moderate illness, to severe disease and even fatal outcome. Biomarkers which enable an early prediction of the severity of COVID-19 progression, would be enormously beneficial to guide patient care and early intervention prior to hospitalization. METHODS: Here we describe the identification of plasma protein biomarkers using an antibody microarray-based approach in order to predict a severe cause of a COVID-19 disease already in an early phase of SARS-CoV-2 infection. To this end, plasma samples from two independent cohorts were analyzed by antibody microarrays targeting up to 998 different proteins. RESULTS: In total, we identified 11 promising protein biomarker candidates to predict disease severity during an early phase of COVID-19 infection coherently in both analyzed cohorts. A set of four (S100A8/A9, TSP1, FINC, IFNL1), and two sets of three proteins (S100A8/A9, TSP1, ERBB2 and S100A8/A9, TSP1, IFNL1) were selected using machine learning as multimarker panels with sufficient accuracy for the implementation in a prognostic test. CONCLUSIONS: Using these biomarkers, patients at high risk of developing a severe or critical disease may be selected for treatment with specialized therapeutic options such as neutralizing antibodies or antivirals. Early therapy through early stratification may not only have a positive impact on the outcome of individual COVID-19 patients but could additionally prevent hospitals from being overwhelmed in potential future pandemic situations.
We aimed to identify components of the blood present during the early phase of SARS-CoV-2 infection that distinguish people who are likely to develop severe symptoms of COVID-19. Blood from people who later developed a mild or moderate course of disease were compared to blood from people who later had a severe or critical course of disease. Here, we identified a combination of three proteins that were present in the blood of patients with COVID-19 who later developed a severe or critical disease. Identifying the presence of these proteins in patients at an early stage of infection could enable physicians to treat these patients early on to avoid progression of the disease.
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PURPOSE: The purpose was to determine whether a simplified procalcitonin (PCT) algorithm guided by pharmacist recommendations reduces antibiotic duration of therapy in critically ill patients with suspected sepsis. METHODS: This was a single-centered pre-post study conducted at a 1368-bed community teaching hospital in the United States. A prospective cohort with pharmacist intervention utilizing a simplified PCT algorithm was compared with a retrospective historical cohort with standard therapy. Adult patients admitted to the intensive care unit (ICU) with suspected sepsis who received intravenous antibiotics were included. A pharmacist recommended continuation or discontinuation of antibiotics based on the PCT level per our algorithm and full clinical assessment of the patient. Primary outcome was total duration of antibiotic therapy. Secondary outcomes were ICU and hospital length of stay (LOS), reinitiation of antibiotic therapy within 72 hours of discontinuation, and 28-day in-hospital mortality. RESULTS: From September 2017 to May 2018, 360 patients were screened for eligibility. Of these, 26 patients were included in the PCT group and 26 patients in the standard therapy group. Baseline characteristics were similar between groups. A significant difference in duration of antibiotic therapy was detected with a median of 9 days in the PCT group versus 12 days in the standard therapy group (P = .02). There were no significant differences in secondary endpoints of ICU and hospital LOS, reinitiation of antibiotics at 72 hours, or 28-day mortality. CONCLUSION: Use of a simplified PCT algorithm with pharmacist-guided recommendations significantly reduced the duration of antibiotic therapy in critically ill patients with sepsis.
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BACKGROUND: Health care-acquired Clostridium difficile infection (HACDI) is associated with adverse outcomes at both the organization and patient level. Factors that increase risk for development of HACDI have been identified. Objectives of this study were to develop a predictive screening tool to identify patients at risk for HACDI and implement a bundle of mitigation interventions. METHODS: A predictive screening tool was developed based on risk factors identified in the literature and validated by retrospective analysis of all HACDI cases occurring in critically ill patients during 2013. The tool was used to screen all patients admitted to an intensive care unit. Evidence-based interventions (bundle) were implemented for patients identified as being at high risk for HACDI. Effectiveness of the model was measured by reduction of HACDI rate during the intervention period compared with the preintervention period. RESULTS: During the 12-month intervention period 217 high-risk patients were identified as infected with Clostridium difficile. Sixty-two of these met exclusion criteria, resulting in a study population of 157 patients. During the preintervention phase, 10 cases of HACDI occurred (overall incidence rate, 14.7). During the 12-month study period, 2 cases of HACDI were identified (incidence rate, 3.12). The reduction was statistically significant. CONCLUSION: A strategy for identifying patients at increased risk and implementation of multidisciplinary risk-mitigation strategies is effective in reducing incidence of HACDI.
