ABSTRACT
Background. Eravacycline, a novel synthetic fluorocycline, is structurally similar to tigecycline. Cases of tigecycline associated hyperfibrinogenemia have been reported in the literature, however the mechanism is not currently well described. At this time it is unknown if this is a class effect. Methods. Two cases of patients on eravacycline for treatment of Mycobacterium abscessus (M.abscessus) who received regular fibrinogen monitoring are described. Results. Patient 1 received a kidney transplant (2010) and was admitted for acute hypoxic respiratory failure secondary to COVID-19. Their course was complicated by multiple infections including disseminated M.abscessus with positive cultures from the lung and blood. Eravacycline 1 mg/kg BID (80 mg) was started on D1 and continued through D24. Fibrinogen levels on D1 was 448 mg/dl and on D23 were 120 mg/dl. Eravacycline was stopped and fibrinogen returned to normal range (228 mg/dl) in 5 days. Eravacycline was re-trialed at 80 mg BID and fibrinogen level on D1 was 310 mg/dl and 147 mg/dl on D8. No repeat fibrinogen levels were obtained. Patient 2 was a lung transplant recipient (2019) admitted for treatment of M.abscessus skin and soft tissue infection. The patient was started on eravacycline 1 mg/kg BID (90 mg) due to concerns of hypofibrinogenemia from tigecycline. On D1 of eravacycline fibrinogen was 167 mg/dl , on D19 of therapy fibrinogen was 64 mg/dl and eravacycline was stopped. Fibrinogen level returned to normal 3 days after eravacycline discontinuation (212 mg/dl). Conclusion. Similar to tigecycline, we observed eravacycline related hypofibrinogenemia. Time to onset was variable in the two cases presented. Hypofibrinogenemia was readily reversible, within 3-5 days, with drug withdrawal and reproducible in one patient with re-challenge of eravacycline. Further analysis into eravacycline related hypofibrinogenemia and its impact on coagulation outcomes are warranted based on these reports.
ABSTRACT
Background. COVID-19 shifted antibiotic stewardship program resources and changed antibiotic use (AU). Shifts in patient populations with COVID surges, including pauses to surgical procedures, and dynamic practice changes makes temporal associations difficult to interpret. Our analysis aimed to address the impact of COVID on AU after adjusting for other practice shifts. Methods. We performed a longitudinal analysis of AU data from 30 Southeast US hospitals. Three pandemic phases (1: 3/20-6/20;2: 7/20-10/20;3: 11/20-2/21) were compared to baseline (1/2018-1/2020). AU (days of therapy (DOT)/1000 patient days (PD)) was collected for all antimicrobial agents and specific subgroups: broad spectrum (NHSN group for hospital-onset infections), CAP (ceftriaxone, azithromycin, levofloxacin, moxifloxacin, and doxycycline), and antifungal. Monthly COVID burden was defined as all PD attributed to a COVID admission. We fit negative binomial GEE models to AU including phase and interaction terms between COVID burden and phase to test the hypothesis that AU changes during the phases were related to COVID burden. Models included adjustment for Charlson comorbidity, surgical volume, time since 12/2017 and seasonality. Results. Observed AU rates by subgroup varied over time;peaks were observed for different subgroups during distinct pandemic phases (Figure). Compared to baseline, we observed a significant increase in overall, broad spectrum, and CAP groups during phase 1 (Table). In phase 2, overall and CAP AU was significantly higher than baseline, but in phase 3, AU was similar to baseline. These phase changes were separate from effects of COVID burden, except in phase 1 where we observed significant effects on antifungal (increased) and CAP (decreased) AU (Table). Conclusion. Changes in hospital AU observed during early phases of the COVID pandemic appeared unrelated to COVID burden and may have been due to indirect pandemic effects (e.g., case mix, healthcare resource shifts). By pandemic phase 3, these disruptive effects were not as apparent, potentially related to shifts in non-COVID patient populations or ASP resources, availability of COVID treatments, or increased learning, diagnostic certainty, and provider comfort with avoiding antibacterials in patients with suspected COVID over time. (Figure Presented).