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The efficacy of COVID-19 mRNA vaccines is high, but breakthrough infections still occur. We compared the SARS-CoV-2 genomes of 76 breakthrough cases after full vaccination with BNT162b2 (Pfizer/BioNTech), mRNA-1273 (Moderna), or JNJ-78436735 (Janssen) to unvaccinated controls (February-April 2021) in metropolitan New York, including their phylogenetic relationship, distribution of variants, and full spike mutation profiles. The median age of patients in the study was 48 years; 7 required hospitalization and 1 died. Most breakthrough infections (57/76) occurred with B.1.1.7 (Alpha) or B.1.526 (Iota). Among the 7 hospitalized cases, 4 were infected with B.1.1.7, including 1 death. Both unmatched and matched statistical analyses considering age, sex, vaccine type, and study month as covariates supported the null hypothesis of equal variant distributions between vaccinated and unvaccinated in χ2 and McNemar tests (P > 0.1), highlighting a high vaccine efficacy against B.1.1.7 and B.1.526. There was no clear association among breakthroughs between type of vaccine received and variant. In the vaccinated group, spike mutations in the N-terminal domain and receptor-binding domain that have been associated with immune evasion were overrepresented. The evolving dynamic of SARS-CoV-2 variants requires broad genomic analyses of breakthrough infections to provide real-life information on immune escape mediated by circulating variants and their spike mutations.

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed.MethodsWe developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83–97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients’ mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivorsDiscussionThis study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality.ConclusionAs we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

PurposeEnvironmental sustainability is a growing concern to healthcare providers, given the health impacts of climate change and air pollution, and the sizable footprint of healthcare delivery itself. Though many studies have focused on environmental footprints of operating rooms, few have quantified emissions from inpatient stays. This study quantifies solid waste and greenhouse gas emissions (GHGs) per bed-day in a regular inpatient (low intensity) and intensive care unit (high intensity).MethodsThis study uses hybrid environmental life cycle assessment (LCA) to quantify average emissions associated with resource use in an acute inpatient unit with 49 beds and 14,427 hospitalization days and an intensive care unit (ICU) with 12 beds and 2536 hospitalization days. The units are located in a single tertiary, private hospital in Brooklyn, NY, USA.Results and discussionAn acute care unit generates 5.5 kg of solid waste and 45 kg CO2-e per hospitalization day. The ICU generates 7.1 kg of solid waste and 138 kg CO2-e per bed day. Most emissions originate from purchase of consumable goods, building energy consumption, purchase of capital equipment, food services, and staff travel.ConclusionsThe ICU generates more solid waste and GHGs per bed day than the acute care unit. With resource use and emission data, sustainability strategies can be effectively targeted and tested. Medical device and supply manufacturers should also aim to minimize direct solid waste generation.

We report two cases of SARS-CoV-2 infection (COVID-19) in infants presenting with fever in the absence of respiratory distress who required hospitalization for evaluation of possible invasive bacterial infections. The diagnoses resulted from routine isolation and real-time RT-PCR-based testing for SARS-CoV-2 for febrile infants in an outbreak setting.

SignificanceEpidemics of community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) are of growing medical concern. To understand the emergence of virulence and antimicrobial resistance, both of which promote CA-MRSA spread, we examined an on-going disease cluster within an enclosed community by analyzing the genome sequences of CA-MRSA clones characterized by high prevalence and a profound persistence. Metabolic adaptation and a phage primed the clone for success, and then a fully optimized variant was created by selection of plasmid-mediated biocide resistance. The data provide mechanistic insight and indicate that high-risk populations are incubators for evolution of consequential phenotypes. Immediate interruption of this evolutionary pattern is essential for forestalling dissemination of resistance from high-risk communities to hospitals and the general population. The past two decades have witnessed an alarming expansion of staphylococcal disease caused by community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA). The factors underlying the epidemic expansion of CA-MRSA lineages such as USA300, the predominant CA-MRSA clone in the United States, are largely unknown. Previously described virulence and antimicrobial resistance genes that promote the dissemination of CA-MRSA are carried by mobile genetic elements, including phages and plasmids. Here, we used high-resolution genomics and experimental infections to characterize the evolution of a USA300 variant plaguing a patient population at increased risk of infection to understand the mechanisms underlying the emergence of genetic elements that facilitate clonal spread of the pathogen. Genetic analyses provided conclusive evidence that fitness (manifest as emergence of a dominant clone) changed coincidently with the stepwise emergence of (i) a unique prophage and mutation of the regulator of the pyrimidine nucleotide biosynthetic operon that promoted abscess formation and colonization, respectively, thereby priming the clone for success; and (ii) a unique plasmid that conferred resistance to two topical microbiocides, mupirocin and chlorhexidine, frequently used for decolonization and infection prevention. The resistance plasmid evolved through successive incorporation of DNA elements from non-S. aureus spp. into an indigenous cryptic plasmid, suggesting a mechanism for interspecies genetic exchange that promotes antimicrobial resistance. Collectively, the data suggest that clonal spread in a vulnerable population resulted from extensive clinical intervention and intense selection pressure toward a pathogen lifestyle that involved the evolution of consequential mutations and mobile genetic elements.

Background: Staphylococcus aureus –colonized hospitalized patients are at risk for invasive infection and can transmit S. aureus to other patients in the absence of symptoms. Infection isolation precautions do not reduce the risk of infection in colonized patients and are untenable in health systems with high rates of S. aureus colonization. Objective: We implemented an inpatient S. aureus screening and targeted decolonization program across hospital campuses to reduce transmission and invasive infection. We screen and decolonize for methicillin-susceptible S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) because MSSA makes up more than half of all S. aureus isolated from clinical cultures in our health system. Methods: All medicine, pediatrics, and transplant patients receive S. aureus nares culture at admission and upon change in level of care for medicine, and at admission and weekly for pediatrics and transplant patients. All S. aureus– colonized patients receive decolonization with nasal mupirocin ointment and chlorhexidine baths. Two implementation frameworks guide our processes for S. aureus screening and decolonization: the Consolidated Framework for Implementation Research, to evaluate factors affecting implementation at different levels of the health system, and the Dynamic Sustainability Framework, to account for iterative changes as the hospital setting and patient population change over time. Implementation interventions focus on education of patients and bedside nurses who perform S. aureus screening and decolonization; utilization of the electronic health record to identify patients for screening and/or decolonization and avoid human error; and introduction of a clinical nurse specialist to oversee the program and to provide iterative feedback. Results: At baseline, 21% of patients had S. aureus colonization, 20% of which was MRSA, and the MRSA bloodstream infection rate was 0.06 per 1,000 patient days. After program implementation, there was no change in S. aureus colonization and the MRSA bloodstream infection rate fell to 0.04 per 1,000 patient days. Screening compliance improved from 39% (N = 1,805) of eligible patients in the 6-month period before the introduction of the clinical nurse specialist to 52% (N = 2,024) after the introduction of the clinical nurse specialist. In the same periods, decolonization increased from 18.6% to 41% of eligible patients. Conclusions: We used 2 implementation frameworks to design our S. aureus screening and decolonization program and to make iterative changes to the program as it evolved to include new patient populations and different hospital settings. This resulted in a large-scale, sustainable, health system program for S. aureus control that avoids reliance on infection isolation precautions. Funding: None Disclosures: None

Background: Readmissions to hospitals are common, costly and often preventable, notably readmissions due to infections. A 30-day readmission analysis following hospital discharges, found much of the variation in Medicare spending between hospitals was related to readmissions and skilled nursing facility (SNF) care. Although some readmissions of patients with advanced disease are not preventable, efforts to decrease readmission are most effectively directed towards those patients with intermediate levels of a specific risk. A prediction model to identify patients at highest (or intermediate) risk of infection readmission will help healthcare administrators and providers to allocate appropriate resources. Hospitals should use electronic health record (EHR) data with modern data mining techniques to create more curated, sophisticated models as part of a comprehensive transitional care program. We propose using the risk estimates of a validated prediction model to notify stakeholders and develop readmission rate reports by SNF or discharging physician. Methods: We applied machine learning (ML) methods to predict the risk of 30-day readmission due to sepsis and pneumonia of patients discharged to SNF. We used our EHR data during 2012–2017 to train and data from 2018 to validate. We applied ML algorithms to data including logistic regression, random forest, gradient boosting trees, and support vector machine. Data from EDW and EPIC clarity tables were extracted and managed using SAS Base 9.4 and Enterprise Miner 14.3 (SAS Institute, Cary, NC). We assessed the discrimination and calibration to select the most effective prediction model. Using the resulted risk estimates, we created a notification system and reports for key stakeholders. Results: Figures 1 and 2 show the discrimination and calibration results of the final selected gradient boosting model (GBM). For predicting unplanned readmissions with sepsis and with pneumonia within 30 days after discharge to SNF, the c-statistic for final GBM model with 140 features was 0.69 (95% CI 0.65-0.73) and 73 features was 0.71 (95% CI 0.66-0.75), respectively. Table 1 lists features important to the validation set of the prediction model. We used estimates from these models to develop a daily email notification of patients discharged to SNF stratified into a low, medium, and high risk group for sepsis and pneumonia. We additionally created reports with case-mix adjustments to benchmark SNFs and discharging physicians to monitor and understand performance. Conclusions: Hospitals should leverage the plethora of data found in EHRs to curate readmission prediction models, and promote collaboration among transitional care teams and IPC to ultimately reduce readmissions due to sepsis and pneumonia. Funding: None Disclosures: None

Background: Whole-genome sequencing (WGS) has a high discriminatory power in confirming outbreaks. Outbreak investigation models that categorize the possibility of an outbreak based on the degree of genetic relatedness of isolates are highly dependent on the single-nucleotide polymorphism (SNP) threshold used. Methods: NYU Langone Medical center is a 725-bed academic center that has implemented WGS of methicillin-resistant Staphylococcus aureus (MRSA) isolates since 2016. Patients admitted to a medical or intensive care unit were screened on admission and transfer. The first surveillance and clinical MRSA isolate during each hospitalization was sequenced. We conducted a retrospective analysis to identify strong epidemiologic links among patients involved in genetically related clusters. We used different SNP thresholds to define genetic relatedness to identify the optimal threshold that should prompt an outbreak investigation. We considered strong hospital epidemiologic links sharing the same room or unit or having resided in the same room or unit within 7 days. A pairwise analysis was conducted to compare the epidemiologic links among patients involved in genetically related clusters. Results: Among 1,070 isolates, our analysis focused on 777 belonging to USA100 and USA300 clones. For USA100 isolates, we identified 8, 14, and 20 clusters comprising of 16, 29, and 42 patients when the threshold for genetic relatedness was set at 20, 40, and 60 SNP differences, respectively. Patients identified in a cluster yielded a strong hospital epidemiologic link in 62.5%, 87.5%, and 91.7% of cases (Fig. 1). For USA300 isolates, SNP differences of 10, 20, and 30 were used, identifying 20, 34, and 40 clusters of 43, 79, and 127 patients. The expansion of the threshold from 10 to 30 resulted in a decrease of the percentage of pairwise analyses with a strong hospital epidemiologic link from 57.7% to 13.6% by increasing 13-fold the number of analyses that were conducted to identify only 3 times more cases with strong epidemiologic links (Fig. 2). Conclusions: The results of our study indicate that SNPs thresholds determined by intrapatient variability of MRSA isolates might need to be tailored to the individual setting to guide infection control interventions because optimal thresholds might vary depending on characteristics of the population, MRSA isolates, and screening practices. Establishing conservative thresholds might allow the identification and quantification over time of the locations (eg, rooms or units) where transmission is occurring as well as the investigation of the clusters without strong epidemiologic links that might be valuable in elucidating unrecognized routes of transmission. Funding: None Disclosures: None

Background Colonization with Staphylococcus aureus, particularly MRSA, is a crucial risk factor for subsequent infection. Decolonization measures are often undertaken to prevent recurrent MRSA infection and transmission; however, increasing rate of resistance to the gold standard mupirocin has been noted globally. At our institution, there is >85% high-level resistance to mupirocin among strains from a geographically defined genotypic cluster of CA-MRSA in children from Orthodox communities in Brooklyn. Retapamulin is a topical bacteriostatic pleuromutilin antibiotic that has demonstrated excellent in vitro activity against mupirocin-resistant isolates from pediatric patients with MRSA infection presenting to our institution suggesting that it may be a promising alternative decolonization therapy. We sought to determine the efficacy of retapamulin as a topical decolonizing agent against mupirocin-resistant MRSA among the identified high-risk Brooklyn cluster via a randomized, placebo-controlled, double-blinded phase three trial. Methods Children aged 9 months-17 years who resided in high-risk zip codes used as a proxy for Orthodox Jewish predominant neighborhoods were recruited either from inpatient units at NYU Langone or at a partnered community clinic. Participants were screened via nasal and rectal culture to detect MRSA colonization. Enrolled participants were randomized to receive either retapamulin or placebo and instructed to apply the ointment nasally and rectally twice a day for 5 days. Repeat nasal and rectal swab cultures were collected one week and one month after completion of topical therapy to assess MRSA colonization status. The change in colonization rates was assessed via Fisher’s exact test. Results 173 participants were screened from December 2017 to March 2019 in which 47 ultimately underwent randomization (23 in the retapamulin group and 24 in the placebo group). The median age was 3.9 years (SD 3.5 years). Children in the placebo group were 15.2 times more likely to be colonized with MRSA after one week of the decolonization protocol compared with the retapamulin group (OR 15.2, CI 2.8–81, P = 0.0004). However, children in the placebo group were only 1.1 times more likely to be colonized with MRSA after one month compared with the retapamulin group (OR 1.1, CI 0.3–3.9, P = 1). (*Full data analysis currently in progress with additional results available soon.) Conclusion In this small pilot randomized trial, children who received retapamulin had a significantly lower rate of MRSA colonization and higher rates of clearance compared with placebo at one week post decolonization, but no significant difference at the one month mark. These data suggest that retapamulin is a promising alternative short-term nasal and peri-rectal decolonzing therapy in order to prevent infections and the spread of this mupirocin-resistant MRSA clone among pediatric patients in this affected community and our hospital. Disclosures All authors: No reported disclosures.