Background: Densely populated metropolitan cities like Singapore are susceptible to emerging infectious disease (EID) outbreaks. Singapore’s pandemic control measures include running biennial simulation exercises for all public hospitals on EID case management, in which a key assessment criterion is contact tracing. Current methods of contact tracing that involve retrospective review of the electronic medical record (EMR) are time-consuming and heavily manpower dependent, and they fail to capture a significant number of contacts. A real-time location system (RLTS) was accurate and effective in contact tracing. We compared the time taken to perform contact tracing and list of contacts identified for RTLS versus EMR, and we compared manpower and manpower hours required to perform contact tracing for RTLS versus EMR. Then we extrapolated the cost incurred by RTLS versus EMR. Methods: A prospective case study was conducted during a simulation exercise to determine and compare the list of contacts, time required, manpower required, and manpower hours required between RTLS and EMR. The costs of both methods were also compared. Results: RTLS identified almost 3 times more contacts than EMR (Fig. 2) with a 96.2% reduction in time taken, a 97.6% reduction in manpower, and a 97.5% reduction in manpower hours (Fig. 1). RTLS incurred significant equipment cost and therefore might require many contact-tracing episodes before providing economic benefit (Fig. 3). However, its speed and accuracy provided during contact tracing will allow the hospital to quickly isolate potentially exposed contacts, reducing the number of infected people during the spread of an infectious disease, particularly one like COVID-19. Conclusions: Albeit costly, RTLS is effective at contact tracing. RTLS has the potential to be the gold standard in contact-tracing methods of the future, particularly considering the current pandemic.

Disclosures: None

In this systematic literature review and meta-analysis, we did not find a statistically significant difference in readmission and treatment failure rates between home-based and facility-based OPAT. Optimal patient selection for appropriate OPAT location appears to be more important than the location itself for the best OPAT outcome.

Refugee and migrant populations have increased vulnerability to antimicrobial resistance, yet stewardship guidance is lacking. We addressed this gap through a cross-sectional survey, finding that these populations and immigrants from low and middle-income countries had lower health literacy on the issue compared to native-born Americans and those from high-income countries.

Background: The CDC recommends a public health response when novel and targeted multidrug-resistant organisms (nMDROs), such as carbapenem-resistant organisms or Candida auris, are identified in healthcare settings in nonendemic areas. nMDRO responses are supported by healthcare-associated infection-antimicrobial resistance programs in 50 state and 6 local and territorial health departments. Annually, health departments report nMDRO responses to the CDC. We summarize nMDRO responses nationally and report our assessment of colonization screening positivity rates by healthcare setting and pathogen. Methods: We analyzed nMDRO response data reported by health departments for the period August 2019–July 2021; we excluded prevention efforts (ie, widespread screening based on facility-level risk factors). Among nMDRO responses in which colonization screening was performed, we calculated the proportion of responses in which screening detected additional cases of the index nMDRO and the colonization screening positivity, by healthcare setting and pathogen. Results: Among 2,051 nMDRO responses, 732 (36%) had ≥1 colonization screening (representing 44,845 colonization screenings), of which 24 (representing 17,467 colonization screenings) were prevention efforts and were excluded. Among the remaining 708 nMDRO responses, the healthcare setting most frequently included was acute-care hospitals (ACHs; 337 of 708, 48%); the least frequently included was long-term ACHs (LTACHs; 83 of 708, 12%). Carbapenem-resistant Enterobacterales were the most common index nMDRO prompting a response (408 of 708, 58%). Screening identified additional cases of the index nMDRO in 248 responses (35%) and 2,378 (9%) of 27,378 colonization screenings. Identification of the index nMDRO varied by pathogen and setting (Fig. 1). Overall, ventilator-capable skilled nursing facilities (vSNFs) were the facility type in which colonization screening most frequently identified additional cases of the index nMDRO (63 of 92 responses, 63%), and LTACHs had the highest colonization screening positivity (750 of 5,798, 13%). Similar colonization screening positivity was observed in ACHs (9%) and vSNFs (8%). On average, Candida auris and carbapenem-resistant Acinetobacter baumannii (CRAB) had the highest colonization screening positivity rates across all healthcare settings: CRAB, 493 (12.6%) of 3,907 screened; Candida auris, 1,344 (11.7%) of 11,466 screened (Fig. 1B). More than one-half of responses identified ≥1 case of the index nMDRO. Conclusions: During public health nMDRO responses, additional cases were regularly identified through colonization screening. Responses in vSNFs and LTACHs and to environmental pathogens like Candida auris and CRAB detected additional cases in more than one-half of responses, suggesting that spread commonly occurred prior to detection of the first clinical case. The use of colonization screening is an effective strategy to detect unidentified nMDRO colonization, especially in high-acuity postacute-care settings.

Disclosures: None

Background: The CDC recommends routine use of contact precautions for patients infected or colonized with multidrug-resistant organisms (MDROs). There is variability in implementation of and adherence to this recommendation, which we hypothesized may have been exacerbated by the COVID-19 pandemic. Methods: In September 2022, we emailed an 8-question survey to Emerging Infections Network (EIN) physician members with infection prevention and hospital epidemiology responsibilities. The survey asked about the respondent’s primary hospital’s recommendations on transmission-based precautions, adjunctive measures to reduce MDRO transmission, and changes that occurred during the COVID-19 pandemic. We sent 2 reminder emails over a 1-month period. We used descriptive statistics to summarize the data and to compare results to a similar EIN survey (n = 336) administered in 2014 (Russell D, et al. doi:10.1017/ice.2015.246). Results: Of 708 EIN members, 283 (40%) responded to the survey, and 201 were involved in infection prevention. Most respondents were adult infectious diseases physicians (n = 228, 80%) with at least 15 years of experience (n = 174, 63%). Respondents were well distributed among community, academic, and nonuniversity teaching facilities (Table 1). Most respondents reported that their facility routinely used CP for methicillin-resistant Staphylococcus aureus (MRSA, 66%) and vancomycin-resistant Enterococcus (VRE, 69%), compared to 93% and 92% respectively, in the 2014 survey. Nearly all (>90%) reported using contact precautions for Candida auris, carbapenem-resistant Enterobacterales (CRE), and carbapenem-resistant Acinetobacter spp, but there was variability in the use of contact precautions for carbapenem-resistant Pseudomonas aeruginosa and extended-spectrum β-lactamase–producing gram-negative organisms. In 2014, 81% reported that their hospital performed active surveillance testing for MRSA, and in 2022 this rate fell to 54% (Table 2). The duration of contact precautions varied by MDRO (Table 3). Compared to 2014, in 2022 facilities were less likely to use contact precautions indefinitely for MRSA (18% vs 6%) and VRE (31% vs 11%). Also, 180 facilities (90%) performed chlorhexidine bathing in at least some inpatients and 106 facilities (53%) used ultraviolet light or hydrogen peroxide vapor disinfection at discharge in some rooms. Furthermore, 89 facilities (44%) reported institutional changes to contact precautions policies after the start of the COVID-19 pandemic that remain in place. Conclusions: Use of contact precautions for patients with MDROs is heterogenous, and policies vary based on the organism. Although most hospitals still routinely use contact precautions for MRSA and VRE, this practice has declined substantially since 2014. Changes in contact-precaution policies may have been influenced by the COVID-19 pandemic, and more specifically, contemporary public health guidance is needed to define who requires contact precautions and for what duration.

Disclosures: None

Background: Surveillance of healthcare-associated infection (HAI) is the basis of infection prevention programs. However, manual review of medical records is a labor-intensive and time-consuming process. We evaluated the diagnostic performance of automated surveillance of HAI using electronic screening algorithms. Methods: Between April and June 2022, we conducted surveillance of HAI manually and automatically using electronic screening algorithm on 75 units (general medical and surgical wards and ICUs) in a 2,700-bed, tertiary-care hospital in South Korea. Algorithms for surveillance of HAI were developed accordance with NHSN surveillance definitions (Fig. 1). Catheter-associated urinary tract infections (CAUTIs) were automatically detected when eligible pathogen and fever (>38°C) were matched within infection window period. Other specific types of infection were automatically classified based on laboratory results that met NHSN criteria. After the algorithm showed possible cases that met laboratory-confirmed bloodstream infection (LCBI) criteria, we excluded secondary BSIs using the automatic surveillance algorithm. We analyzed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the automated surveillance system compared to manual surveillance. Results: An algorithm for detecting CAUTI showed 98.7% sensitivity (78 of 79), 100.0% specificity (2,443 of 2,443), 100.0% PPV (78 of 78), and 100.0% NPV (2,443 of 2,444). For CLABSI, the algorithm had 97.3% sensitivity (214 of 220), 98.3% specificity (5,759 of 5,861), 67.7% PPV (214 of 316), and 99.9% NPV (5,759 of 5,765). In total, 102 cases of possible CLABSI were identified by the algorithm, and 76 (74.5%) were eventually diagnosed as secondary BSIs. Also, by chart review, 20 BSIs (19.6%) were present on arrival in ER (ER-POA). In 4 cases (3.9%), an original pathogen reoccurred in a repeated infection timeframe (RIT), and 2 cases (2%) were mucosal barrier injury-LCBI (MBI-LCBI). When we additionally performed manual surveillance for intra-abdominal infection secondary BSI, ER-POA, and assigning pathogen to original BSI in RIT, PPV increased to 87.7% (214 of 244). Conclusions: Algorithm for automated surveillance of CAUTI had good performance; however, automated surveillance of CLABSI was suboptimal. More elaborate screening algorithm for diagnosis CLABSI is needed, and further studies are needed to determine whether an automated surveillance system can reduce workload for surveillance of HAI.

Disclosures: None

Background: Some hospitals continue to struggle with nosocomial transmission of Clostridioides difficile infection (CDI) and methicillin-resistant Staphylococcus aureus (MRSA) despite years of infection control efforts. We investigated the relationship between unit infrastructural–organizational risk factors and nosocomial transmission of CDI and MRSA. Methods: This retrospective observational study included 100 eligible acute-care inpatient units from 12 hospitals in British Columbia, Canada, from April 1, 2020, to September 16, 2021. The outcome variables included whether a unit was on the CDI or MRSA vulnerable unit list (ie, defined as having ≥5 CDI cases or ≥6 MRSA cases being attributed to the unit in the last 6 fiscal periods), the average CDI/MRSA rate, as well as the average CDI/MRSA standardized infection ratio (SIR). Independent variables included, but were not limited to, infection control factors (eg hand hygiene rate), infrastructural factors (eg, unit age, total beds on unit), and organizational factors (eg, hallway bed utilization, nursing overtime). Multivariable regression was performed to identify statistically significant risk factors using SAS, R Studio, and Stata software. Results: For CDI, older units were associated with higher odds of being on the CDI vulnerable unit list (aOR, 1.086; 95% CI, 1.024–1.175), higher CDI rate (adjusted relative risk [aRR], 0.012; 95% CI, 0.004–0.020), and higher CDI SIR (aRR, 0.011; 95% CI, 0.003–0.020). Larger unit size was associated with higher odds of being on the CDI vulnerable unit list (aOR, 1.210; 95% CI, 1.095–1.400) and higher CDI SIR (aRR, 0.013; 95% CI, 0.001–0.026). For MRSA, an increase in hand hygiene rate was associated with lower odds of being on the MRSA vulnerable unit list (aOR, 0.71; 95% CI, 0.53–0.897), lower MRSA rate (aRR, −0.035; 95% CI, −0.063 to −0.008), and lower MRSA SIR (aRR, −0.039; 95% CI, −0.069 to −0.008). Higher MRSA bioburden was associated with higher odds of being on the MRSA vulnerable unit list (aOR, >999; 95% CI, >999 to >999), higher MRSA rate (aRR, 9.008; 95% CI, 5.586–12.429), and higher MRSA SIR (aRR, 4.964; 95% CI, 1.971–7.958). Additionally, higher MRSA rates were associated increased utilization of hallway beds (aRR, 0.680; 95% CI, 0.094–1.267), increased nursing overtime rate (aRR, 5.018; 95% CI, 1.210–8.826), and not having a clean supply room with the door consistently closed (aRR, −0.283; 95% CI, −0.536 to −0.03). Conclusions: Several infrastructural and organizational factors were associated with nosocomial transmissions of CDI and MRSA. Further research is needed to investigate the mechanisms by which these factors are associated.

Disclosures: None

This study assessed outcomes prior to and after electronic medical record-based clinical decision support implementation combined with prospective audit in patients with COVID-19. This multimodal stewardship intervention was associated with a decrease in antibiotic exposure for patients with COVID-19 (44.4% vs 61.8%, p = 0.002) within the first 7 days of hospitalization.

We evaluated the impact of carbapenem shortage on antimicrobial practice and patient outcome at a tertiary care center. During the shortage, hospital antimicrobial practice could be safely managed through additional antimicrobial stewardship measures including treatment guidance and mandatory preauthorization. Antimicrobial shortage may present a unique opportunity for promoting antimicrobial stewardship.

Background: The NHSN Antimicrobial Resistance (AR) Option is an important avenue for acute-care hospitals to electronically report facilitywide antibiogram data. The NEDSS Base System (NBS) is the statewide surveillance system for mandatory reporting of all carbapenem-resistant Enterobacteriaceae (CRE) cases. The state health department (SHD) validated CRE case data reported through the AR Option to assess completeness and accuracy. Methods: NHSN AR Option data from July 2020–December 2021 for 24 facilities were validated by comparing reported CRE and susceptibility results to CRE isolates reported via the NBS. Isolates were matched based on specimen date, sex, birth month and day, pathogen, and specimen source. NHSN susceptibility results were dichotomized as “not resistant” and “resistant” to match the NBS results. Susceptibility discordance (differing proportions of resistant isolates) of matched pairs were evaluated using the McNemar exact test in SAS version 9.4 software. Results: The SHD identified 270 CRE cases from the NHSN and 1,254 unique CRE isolates from the NBS. Of the NHSN events, 72 (26.67%) were matched to the NBS. Among matched isolates, discordance was significant for doripenem (0 resistant isolates in the NHSN vs 13 in the NBS; P < .001) and imipenem (5 resistant isolates in the NHSN vs 23 in the NBS; P < .0001). Discordance was not significant for ertapenem nor meropenem. Sensitivity analyses maximized the match rate at 30.74% (83 matches) when NBS isolates from unknown sources were included and matching factors were specimen date and date of birth ± 1 day, and pathogen. Among all 6,325 CRE isolates in NBS, 290 (4.58%) did not have a specimen source provided. Of all 47,348 NHSN events, 7,624 (16.10%) had impossible patient birthdays. Conclusions: Many NHSN isolates could not be matched to NBS due to either isolates being missing from NBS or to data differences across the systems. This mismatch highlights the need for data validation and standardization at the point of entry for both systems. Discordant susceptibility outcomes raise concerns about using the NHSN as a method for facility and regional antibiogram data.

Disclosures: None

The presence of a penicillin allergy label in a patient’s medical chart is associated with negative clinical and economic outcomes. Given that less than 10% of reported reactions are truly immunoglobulin E-mediated, removal of unverified penicillin allergy labels is a public health priority and an area of ongoing implementation research. The Veterans Health Administration (VHA) is the largest integrated healthcare system in the United States, with almost 9 million veterans currently enrolled. However, studies analyzing the impact of the penicillin allergy label in this population are limited to single facilities and largely focus on short-term outcomes of allergy documentation correction, usage of β-lactams, and avoidance of antibiotic-related side effects. Broader, national VHA studies focusing on health outcomes and costs are lacking. As with non-VHA facilities, penicillin allergy evaluations are limited owing to the absence of formal allergy/immunology services at most VHA facilities. Pharmacy-driven screening and referral for clinic-based penicillin skin testing is a promising and frequently discussed modality in the literature, but its scalability within the VHA is not yet proven. Broader, evidence-based strategies that can be adapted to the available resources of individual VHA facilities, including those without on-site access to allergy providers, are needed.

Background: Clostridioides difficile and multidrug-resistant organisms (MDROs) pose challenges due to treatment complexities and substantial morbidity and mortality. Susceptibility to colonization with these organisms and potential onward transmission if colonized (ie, infectivity) is influenced by the human microbiome and its dynamics. Disruptive effects of antibiotics on the microbiome imply potential indirect effects of antibiotics on C. difficile colonization. Mathematical models can help explore the relative impact of key pathways linking antibiotic use to C. difficile colonization, including the relationship between population-level antibiotic use and colonization prevalence. Methods: We built a compartmental model of long-term C. difficile colonization prevalence of nursing home residents (though malleable for any MDRO), allowing interactions between the microbiome and the colonization process. Based on proportional abundance of microbial taxa, we classified individuals into high and low α diversity groups, each further stratified into uncolonized or colonized with C. difficile. The rate of transition from the high to low microbiome diversity group was proportional to the population-level rate of antibiotic use. Transmission dynamics followed a susceptible–infectious–susceptible framework with the possibility for increased susceptibility and infectivity for the low-diversity microbiome group. First, as a comparator, we used a “null model” in which microbiome diversity did not influence host susceptibility or infectivity. Next, we sampled from realistic (literature informed) parameter ranges to analyze how the microbiome mediates the effect of antibiotics on colonization in this population. Results: Our analysis suggests that antibiotic use can catalyze colonization with C. difficile through interactions with the host microbiome, resulting in a sharp increase in colonization with a modest increase in antibiotic use (Fig 1). Increasing the population-level antibiotic use by 5% led to a median 24% increase in long-term colonization prevalence in the model (Fig 2). In contrast, increasing susceptibility or infectivity rates by 5% resulted in slightly higher increases in total colonization (27% and 29%, respectively). However, there was considerable uncertainty around these estimates, with interquartile ranges of up to 20% for some parameters (Fig 2). Conclusions: Higher population-level antibiotic use likely increases colonization by C. difficile through indirect effects of the microbiome. The increased colonization burden attributable to increasing antibiotic use may be substantial. With high uncertainty around some estimates, conducting observational studies to better understand key colonization and microbiome parameters (eg, the relative increase in susceptibility or infectivity with lower microbiome diversity) is critical for future efforts to estimate the impact of antibiotic use on colonization with C. difficile and MDROs.

Disclosures: None

Background: Tecovirimat (TPOXX) is an antiviral drug only available via an Expanded Access Program (EAP) investigational new drug protocol and is recommended for treatment of select patients with monkeypox (mpox) infection. Alameda County Public Health Department prioritizes health equity but does not have a dedicated public health clinic. Therefore, we partnered closely with local healthcare providers that serve communities disproportionally impacted by mpox to ensure there was access to TPOXX. Using data collected during the outbreak we assessed whether populations in Alameda County most affected by mpox received treatment. Methods: We describe Alameda County patients with confirmed or probable mpox who received TPOXX during June–October 2022. Data were collected from case investigation interviews with patients and state-wide reportable disease database(s), which included demographic, clinical, and behavioral information. Confidence intervals (CIs) were calculated using the exact method for Poisson counts. We compared characteristics of mpox patients who received and did not receive TPOXX using the Pearson χ2 or Fisher exact test. P < .05 was considered significant. Results: Mpox case rates in Alameda County were highest among Black or African-American residents (35.6 per 100,000, 95% CI, 26.7–46.4) and Hispanic or Latinx residents (25.2, 95% CI, 20.2–31.0) compared to Asian residents (3.9, 95% CI, 2.3–6.1) and white residents (10.4, 95% CI, 7.7–13.9) residents. Among 242 mpox patients, 69 patients (28.5%) received TPOXX. The distribution of demographic and clinical characteristics among patients who received TPOXX was not significantly different than among those who did not, including residents aged 31–40 years (36.2% vs 34.7%), Black or African-American residents (20% vs 26.3%), Hispanic or Latinx residents (38.5% vs 41%), male residents (89.9% vs 95.3%), gay, lesbian, or same-gender loving residents (67.2% vs 67.4%) in the city of Oakland (63.2% vs 61.5%), or residents with human immunodeficiency virus infection (43.5% vs 36.6%). Conclusions: During the Alameda County mpox outbreak, nearly one-third of patients received TPOXX. Demographic and clinical characteristics were similar among TPOXX recipients and nonrecipients. A proactive approach to obtaining TPOXX in Alameda County and strong relationships with local providers may have allowed for treatment to be accessible to mpox patients. Regular review of outbreak data can inform public health activities, ensure health equity, and help refine local response efforts.

Disclosures: None

Background: Rates of ventilator-associated events (VAEs), including infection-related ventilator-associated complications (IVACs) and probable ventilator-associated pneumonia (PVAPs) have increased nationwide since the onset of the COVID-19 pandemic. In December 2021, our health system adopted a new electronic medical record (EMR), which changed the way surveillance for VAEs is performed. We reviewed surveillance criteria, COVID-19 status, and culturing practices in attempts to understand why VAE rates continue to be elevated. Methods: We collected data on VAE type, culture data, COVID-19 status, and surveillance criteria for all patients meeting NHSN definitions for VAE from 2018 through November 2022. For all patients in 2022 (post-EMR transition), 2 physicians (A.D. and M.D.) manually reviewed documented ventilator settings from flow sheets to validate the automated EMR data, and they evaluated culture data for appropriateness. Cultures were defined as appropriate unless they were included in “pancultures” for leukocytosis without concern for pneumonia documented. Rates were compared using an interrupted time series (ITS) analysis before and after the onset of the COVID-19 pandemic and the EMR transition. Patient level data were compared across periods using the χ2 test. All analyses were performed using SAS version 9.4 software. Results: COVID-19 has been implicated in the increasing number of VAEs since the pandemic began: 6% of patients in 2020, 18% in 2021, and 23% in 2022 (P < .001). The percentage of patients meeting criteria for VAE by positive end-expiratory pressure (PEEP) decreased from 2018 to 2022 (92%, 95%, 93%, 85%, 85%, respectively; P = .0004). Patients meeting criteria for VAE by fraction of inspired oxygen (FiO2) increased from 2018 to 2022 (9%, 6%, 11%, 17%, 19%, respectively; P = .0002). Manual review of 2022 data indicated opportunities for test stewardship in 8 of 65 patients with cultures (12%). ITS analysis revealed that IVAC+ rates were climbing prior to the onset of the COVID-19 pandemic (Fig. 1). We observed a marked increase in rates with the implementation of our new EMR and the changes to our surveillance process (0.32 cases per 100 ventilator days). Manual review of records from 2022 revealed 5 patients in which documentation of ventilator settings to meet VAE diagnosis could not be retrieved from flow sheets. Conclusions: COVID-19 continues to affect VAE despite vaccine availability and may partially account for elevated rates nationwide. However, changes in EMR-automated VAE surveillance may also affect rates. Our findings suggest that automated surveillance captures transient or spurious changes in ventilator machine settings that do not accurately represent clinical status. These data may contribute to spurious increases in VAE. More studies are needed to better understand the impact of both COVID-19 and automated surveillance on VAE.

Disclosures: None

Novel ST398 methicillin susceptible Staphylococcus aureus (MSSA) in the United States was first observed in New York City (2004–2007); its diffusion across the country resulted in changing treatment options. Utilizing outpatient antimicrobial susceptibility data from the Veterans Health Administration from 2010 to 2019, the spatiotemporal prevalence of potential ST398 MSSA is documented.

Background: Community-acquired pneumonia (CAP) is a common indication for antibiotic use in hospitalized children and is a key target for pediatric antimicrobial stewardship programs (ASPs). Building upon prior work, we developed and refined an electronic algorithm to identify children hospitalized with CAP and to evaluate the appropriateness of initial antibiotic choice and duration. Methods: We performed a cross-sectional study including children 6 months to 17 years hospitalized for CAP between January 1, 2019, and October 31, 2022, at a tertiary-care children’s hospital. CAP was defined electronically as an International Classification of Disease, Tenth Revision (ICD-10) code for pneumonia, a chest radiograph or chest computed tomography scan (CT) performed within 48 hours of admission, and systemic antibiotics administered within the first 48 hours of hospitalization and continued for at least 2 days. We applied the following exclusion criteria: patients transferred from another healthcare setting, those who died within 48 hours of hospitalization, children with complex chronic conditions, and those with intensive care unit stays >48 hours. Criteria for appropriate antibiotic choice and duration were defined based on established guidelines. Two physicians performed independent medical record reviews of 80 randomly selected patients (10% sample) to evaluate the performance of the electronic algorithm in (1) identifying patients treated for clinician-diagnosed CAP and (2) classifying antibiotic choice and duration as appropriate. A third physician resolved discrepancies. The electronic algorithm was compared to this medical record review, which served as the reference standard. Results: Of 80 children identified by the electronic algorithm, 79 (99%) were diagnosed with CAP based on medical record review. Antibiotic use was classified as the appropriate choice in 75 (94%) of 80 cases, and appropriate duration in 16 (20%) of 80 cases. The sensitivity of the electronic algorithm for identifying appropriate initial antibiotic choice was 94%; specificity could not be calculated because no events of inappropriate antibiotic choice were identified based on chart review. The sensitivity and specificity for determining appropriate duration were 88% and 97%, respectively (Table 1).

Conclusions: The electronic algorithm accurately identified children hospitalized with CAP and demonstrated acceptable performance for identifying appropriate antibiotic choice and duration. Use of this electronic algorithm may improve the efficiency of stewardship activities and could facilitate alignment with updated accreditation standards. Future studies validating this algorithm at other centers are needed.

Disclosures: None