Background: Estimates of contamination of healthcare personnel (HCP) gloves and gowns with methicillin-resistant Staphylococcus aureus (MRSA) following interactions with colonized or infected patients range from 17% to 20%. Most studies were conducted in the intensive care unit (ICU) setting where patients had a recent positive clinical culture. The aim of this study was to determine the rate of MRSA transmission to HCP gloves and gown in non-ICU acute-care hospital units and to identify associated risk factors. Methods: Patients on contact precautions with history of MRSA colonization or infection admitted to non-ICU settings were randomly selected from electronic health records. We observed patient care activities and cultured the gloves and gowns of 10 HCP interactions per patient prior to doffing. Cultures from patients’ anterior nares, chest, antecubital fossa and perianal area were collected to quantify bacterial bioburden. Bacterial counts were log transformed. Results: We observed 55 patients (Fig. 1), and 517 HCP–patient interactions. Of the HCP–patient interactions, 16 (3.1%) led to MRSA contamination of HCP gloves, 18 (3.5%) led to contamination of HCP gown, and 28 (5.4%) led to contamination of either gloves or gown. In addition, 5 (12.8%) patients had a positive clinical or surveillance culture for MRSA in the prior 7 days. Nurses, physicians and technicians were grouped in “direct patient care”, and rest of the HCPs were included in “no direct care group.” Of 404 interactions, 26 (6.4%) of providers in the “direct patient care” group showed transmission of MRSA to gloves or gown in comparison to 2 of 113 (1.8%) interactions involving providers in the “no direct patient care” group (P = .05) (Fig. 2). The median MRSA bioburden was 0 log 10CFU/mL in the nares (range, 0–3.6), perianal region (range, 0–3.5), the arm skin (range, 0-0.3), and the chest skin (range, 0–6.2). Detectable bioburden on patients was negatively correlated with the time since placed on contact precautions (rs= −0.06; P < .001). Of 97 observations with detectable bacterial bioburden at any site, 9 (9.3%) resulted in transmission of MRSA to HCP in comparison to 11 (3.6%) of 310 observations with no detectable bioburden at all sites (P = .03). Conclusions: Transmission of MRSA to gloves or gowns of HCP caring for patients on contact precautions for MRSA in non-ICU settings was lower than in the ICU setting. More evidence is needed to help guide the optimal use of contact precautions for the right patient, in the right setting, for the right type of encounter.

Funding: None

Disclosures: None

Background: Greater than 10% of hospitalized MRSA carriers experience serious MRSA infection in the year following discharge. Prevention opportunities have primarily focused on hospital stays; however postdischarge interventions have the potential to reduce morbidity, mortality and healthcare costs. The CLEAR trial found a 30% hazard reduction in postdischarge MRSA infections among patients who had inpatient MRSA cultures and were given postdischarge decolonization (5 days twice-a-month for 6 months) relative to hygiene education alone. We conducted a cost analysis of the CLEAR intervention to quantify the economic implications and understand the value of adopting this MRSA decolonization strategy. Methods: We constructed a decision model to estimate the one-year healthcare utilization and costs associated with postdischarge decolonization relative to hygiene education. Trial results for MRSA infection risk and downstream outcomes (including outpatient and emergency room visits, hospitalizations, related nursing home stays, and postdischarge antibiotics) were used to parameterize the model. Other medical care and prescription drug costs were based on Medicare Fee Schedules, Red Book and the literature. Patient out-of-pocket costs and time costs associated with subsequent infections were from a survey of trial participants experiencing infection (n=405). All costs were reported in 2019 US dollars. The analysis was conducted using healthcare system and societal perspectives. Sensitivity analyses were conducted on key parameters. Results: Among a hypothetical cohort of 1,000 hospitalized MRSA carriers, we estimated that a postdischarge decolonization intervention versus hygiene education would result in at least 36 fewer subsequent MRSA infections (130 vs 93 of 1,000, respectively) and >40 fewer MRSA-attributable healthcare events including 32 hospitalizations and 6 postdischarge nursing home visits over the course of a year. Assuming an intervention cost of $185 per individual, the program would result in an overall cost savings of $469,000 per 1,000 MRSA carriers undergoing decolonization. This translates to an overall savings of $13,200 per infection averted and $9,000 per infection averted from the healthcare system perspective. Even assuming a lower infection rate or a less effective intervention (15% reduction in infections vs 30% in the CLEAR trial), or a more expensive (up to $653 per patient) intervention, a decolonization program would still result in cost-savings for society, the healthcare system and patients. Conclusions: In addition to health benefits of preventing infections, postdischarge decolonization of MRSA carriers yields substantial savings to society and the healthcare system. Future recommendations for reducing postdischarge MRSA-related disease among MRSA carriers should consider routine decolonization at hospital discharge.

Funding: This study was supported by a grant from the AHRQ Healthcare-Associated Infections Program (R01HS019388) and by the University of California Irvine Institute for Clinical and Translational Science, which was funded by a grant from the NIH Clinical and Translational Sciences Award program (UL1 TR000153).

Disclosures: Dr. Huang reports conducting clinical studies in which participating nursing homes and hospitals received donated products from Stryker (Sage Products), Mölnlycke, 3M, Clorox, Xttrium Laboratories, and Medline. Ms. Singh reports conducting clinical studies in which participating nursing homes and hospitals received donated products from Stryker (Sage Products), 3M, Clorox, Xttrium Laboratories, and Medline. Dr. Rashid, conducting clinical studies in which participating nursing homes and hospitals received donated products from Stryker(Sage Products), Clorox, and Medline. Dr. McKinnell reports receiving grant support to his institution from Melinta Therapeutics, and fees for serving as a research investigator from Lightship, conducting clinical studies in which participating nursing homes and hospitals received donated products from Stryker (Sage Products), 3M, Clorox, Xttrium Laboratories and Medline, and serving as cofounder of Expert Stewardship. Dr. Miller reports receiving grant support from Gilead Sciences, Merck, Abbott, Cepheid, Genentech, Atox Bio, and Paratek Pharmaceuticals, grant support and fees for serving on an advisory board from Achaogen and grant support, consulting fees, and fees for serving on an advisory board from Tetraphase and conducting clinical studies in which participating nursing homes and hospitals received donated products from Stryker (Sage Products), 3M, Clorox, Xttrium Laboratories, and Medline.

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