Infections caused by Acinetobacter baumannii (AB), an increasingly prevalent nosocomial pathogen, have been associated with high morbidity and mortality. We conducted this study to analyze the clinical features, outcomes, and factors influencing the survival of patients with AB bacteremia. We retrospectively examined the medical records of all patients developing AB bacteremia during their hospital stay at a tertiary care hospital in Beirut between 2010 and 2015. Ninety episodes of AB bacteremia were documented in eighty-five patients. Univariate analysis showed that prior exposure to high dose steroids, diabetes mellitus, mechanical ventilation, prior use of colistin and tigecycline, presence of septic shock, and critical care unit stay were associated with a poor outcome. High dose steroids and presence of septic shock were significant on multivariate analysis. Crude mortality rate was 63.5%. 70.3% of the deaths were attributed to the bacteremia. On acquisition, 39 patients had septicemia. Despite high index of suspicion and initiation of colistin and/or tigecycline in 18/39 patients, a grim outcome could not be averted and 37 patients died within 2.16 days. Seven patients had transient benign bacteremia; three of which were treated with removal of the line. The remaining four did not receive any antibiotics due to withdrawal of care and died within 26.25 days of acquiring the bacteremia, with no signs of persistent infection on follow up. A prolonged hospital stay is frequently associated with loss of functionality, and steroid and antibiotic exposure. These factors seem to impact the mortality of AB bacteremia, a disease with high mortality rate and limited therapeutic options.

Background The US Food and Drug Administration issued an Emergency Use Authorization for remdesivir use in patients with severe COVID-19. Methods We utilized data from two quaternary, acute care hospitals. The outcomes of interest were the impact of remdesivir on in-hospital death by day 28 as well as time to recovery, clinical improvement, and discharge. We utilized Cox proportional hazards models and stratified log-rank tests. Results 224 patients were included in the study. Median age was 59 years; 67.0% were male; 17/125 patients (13.6%) who received supportive care and 7/99 patients (7.1%) who received remdesivir died. The unadjusted risk for 28-day in-hospital death was lower for patients who received remdesivir compared to patients who received supportive care (HR 0.42; 95% CI: 0.16-1.08). Although this trend remained the same after adjusting for age, sex, race and oxygen requirements on admission (aHR 0.49; 95% CI: 0.19-1.28), as well as chronic comorbidities and use of corticosteroids (aHR 0.44; 95% CI: 0.16-1.23), it did not reach statistical significance. The use of remdesivir was not associated with an increased risk of acute kidney injury (AKI) and liver test abnormalities. Although not statistically significant, the rate ratios for time to recovery, clinical improvement, and discharge were higher in women and Black or African American patients. Conclusion Patients on remdesivir had lower, albeit not significant, all-cause in hospital mortality, and the use of remdesivir did not increase the risk for AKI. Promising signals from this study need to be confirmed by future placebo-controlled randomized clinical trials.

Background In order for healthcare systems to prepare for future waves of COVID-19, an in-depth understanding of clinical predictors is essential for efficient triage of hospitalized patients. Methods We performed a retrospective cohort study of 259 patients admitted to our hospitals in Rhode Island to examine differences in baseline characteristics (demographics and comorbidities) as well as presenting symptoms, signs, labs, and imaging findings that predicted disease progression and in-hospital mortality. Results Patients with severe COVID-19 were more likely to be older (p = 0.02), Black (47.2% vs. 32.0%, p = 0.04), admitted from a nursing facility (33.0% vs. 17.9%, p = 0.006), have diabetes (53.9% vs. 30.4%, p<0.001), or have COPD (15.4% vs. 6.6%, p = 0.02). In multivariate regression, Black race (adjusted odds ratio [aOR] 2.0, 95% confidence interval [CI]: 1.1–3.9) and diabetes (aOR 2.2, 95%CI: 1.3–3.9) were independent predictors of severe disease, while older age (aOR 1.04, 95% CI: 1.01–1.07), admission from a nursing facility (aOR 2.7, 95% CI 1.1–6.7), and hematological co-morbidities predicted mortality (aOR 3.4, 95% CI 1.1–10.0). In the first 24 hours, respiratory symptoms (aOR 7.0, 95% CI: 1.4–34.1), hypoxia (aOR 19.9, 95% CI: 2.6–152.5), and hypotension (aOR 2.7, 95% CI) predicted progression to severe disease, while tachypnea (aOR 8.7, 95% CI: 1.1–71.7) and hypotension (aOR 9.0, 95% CI: 3.1–26.1) were associated with increased in-hospital mortality. Conclusions Certain patient characteristics and clinical features can help clinicians with early identification and triage of high-risk patients during subsequent waves of COVID-19.

Objective We aimed to externally validate the predictive performance of two recently developed COVID‐19‐specific prognostic tools, the COVID‐GRAM and CALL scores, and prior prognostic scores for community‐acquired pneumonia (CURB‐65), viral pneumonia (MuBLSTA) and H1N1 influenza pneumonia (Influenza risk score) in a contemporary US cohort. Methods We included 257 hospitalised patients with laboratory‐confirmed COVID‐19 pneumonia from three teaching hospitals in Rhode Island. We extracted data from within the first 24 hours of admission. Variables were excluded if values were missing in >20% of cases, otherwise, missing values were imputed. One hundred and fifteen patients with complete data after imputation were used for the primary analysis. Sensitivity analysis was performed after the exclusion of one variable (LDH) in the complete dataset (n = 257). Primary and secondary outcomes were in‐hospital mortality and critical illness (mechanical ventilation or death), respectively. Results Only the areas under the receiver‐operating characteristic curves (RO‐AUC) of COVID‐GRAM (RO‐AUC = 0.775, 95% CI 0.525‐0.915) for in‐hospital death, and CURB65 for in‐hospital death (RO‐AUC = 0.842, 95% CI 0.674‐0.932) or critical illness (RO‐AUC = 0.766, 95% CI 0.584‐0.884) were significantly better than random. Sensitivity analysis yielded similar trends. Calibration plots showed better agreement between the estimated and observed probability of in‐hospital death for CURB65, compared with COVID‐GRAM. The negative predictive value (NPV) of CURB65 ≥2 was 97.2% for in‐hospital death and 88.1% for critical illness. Conclusions The COVID‐GRAM score demonstrated acceptable predictive performance for in‐hospital death. The CURB65 score had better prognostic utility for in‐hospital death and critical illness. The high NPV of CURB65 values ≥2 may be useful in triaging and allocation of resources.

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In this commentary, we provide a broad overview of how the rapidly evolving coronavirus disease 2019 (COVID-19) diagnostic landscape has impacted clinical care during the COVID-19 pandemic. We review aspects of both molecular and serologic testing and discuss the logistical challenges faced with each. We also highlight the progress that has been made in the development and implementation of these assays as well as the need for ongoing improvement in diagnostic testing capabilities.

BackgroundNosocomial outbreaks of Serratia marcescens have been widely reported and the source is identified in most cases. We report a Serratia marcescens outbreak in a community hospital with no obvious source.MethodsAn epidemiologic investigation was started after an outbreak was suspected. Clinical data were collected from charts of patients with positive culture for Serratia marcescens. Molecular relatedness of available isolates was determined by pulsed-field gel electrophoresis.ResultsBetween December 2016 and August 2017, 13 non-pigmented Serratia marcescens isolates were identified from 11 patients. Bacteria were isolated from blood, abdominal and respiratory cultures. Susceptibility profiles showed variable resistance to ceftriaxone, ceftazidime, imipenem, tobramycin and aztreonam. Infection control measures: Isolates were identified from adult patients who underwent various cardiothoracic/vascular surgeries. Patients were traced back to a single floor of the new hospital building. To control this outbreak, the infection prevention team started with hand hygiene initiatives and observations, environmental sampling, and reviewing management of ventilator, dialysis equipment, and ECMO machines. Ice machine carbonless filters were installed, UV disinfection systems were used, and new TEE cleaning rooms were designated. In conjunction with recommendations of department of health, hospital was contracted with a water cleaning company; laminar flow aerators were installed, water sampling plan was implemented and ultimately the whole building’s water system was hyper-chlorinated.ConclusionWhile water contamination was the most likely source, a specific cause could not be identified. An important lesson learnt is the quick implementation of infection control measures after identifying infected patients is key in controlling an outbreak.Disclosures All authors: No reported disclosures.

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