LabID Events Research Articles

Audit and Feedback to Ordering Providers to Reduce Inappropriate C. difficile Testing and Hospital Onset C. difficile Rate

Background: Inappropriate Clostridioides difficile (C. difficile) laboratory testing is common in hospitals and leads to over diagnosis, unnecessary treatment, and elevated hospital onset C. difficile infection (HO-CDI) metrics. Diagnostic stewardship is essential to avoid inappropriate testing, but limited data exists on optional interventions. Methods: A diagnostic stewardship intervention targeting CDI testing comprised of education and prospective audit with feedback was performed a VA facility (inpatient, outpatient, and long-term care units). Education on appropriate indications for CDI testing was provided in pre-intervention (9/2022 to 5/2023) and intervention periods (6/2023 to 12/2023). During the intervention period, all CDI tests (positive or negative) were audited after completion in real-time by an Infectious Diseases physician and feedback was given to ordering providers and/or their supervising physician (if trainee) for all tests not meeting an appropriate indication. Appropriate indication was defined as ≥3 liquid stools in 24 hours or symptoms of fulminant disease. Testing was considered inappropriate if no clinical symptoms, patient received laxatives within 48 hours, test was performed for test-of-cure or within 7 days of a prior test with no clinical change, or delayed testing in patients with diarrhea on admission. The rate of HO-CDI per 10,000 bed days of care (BDOC) per LabID event was compared during the pre-intervention and intervention periods, and ordering appropriateness was compared for all tests and hospital onset tests before (3/2023-5/2023) and after (6/2023-12/2023) feedback was performed. Results: After starting audit and feedback, HO-CDI rate decreased from 3.92 per 10,000 BDOC to 0.99 per 10,000 BDOC (p=0.03). HO-CDI rate among tests that were inappropriate was 2.19 and 0.80 per 10,000 BDOC during the pre-intervention and intervention periods, respectively (p=0.40). Average overall tests per month decreased from 37.8 to 28.1 after the intervention. Rate of all inappropriate tests decreased from 16.25 to 7.96 per 10,000 BDOC (p=0.04) and rate of hospital onset inappropriate tests trended toward decrease from 9.29 to 4.77 tests per 10,000 BDOC (p=0.07). The most common reasons for inappropriate testing were < 3 episodes of diarrhea in 24 hours (54% pre-intervention, 65% intervention) and laxative use (57% pre-intervention, 45% intervention). No cases of delayed testing leading to worsened disease were identified during the intervention. Cost savings for decreased tests were estimated at $150-300 per month. Conclusion: An intervention comprised of education and real-time audit and feedback of all CDI tests obtained at a VA facility resulted in decreased inappropriate testing and reduced the rate of HO-CDI.

739. Impact of Alcohol-based Nasal Decolonization’s Method of Documentation in Reducing Methicillin-resistant Staphylococcus aureus Hospital-associated Infections

Abstract Background Infections related to multi-drug resistant organisms (MDRO) are increasingly difficult to treat and contribute to elevated mortality, prolonged length of stay and financial burden. At a 382-bed academic medical center, universal decolonization using an alcohol-based nasal decolonizing agent is performed twice daily on all inpatients, as well as chlorhexidine gluconate (CHG) bathing on select populations. Numerous studies cite decolonization as an effective reduction method for MDRO such as methicillin-resistant Staphylococcus aureus (MRSA). Unfortunately, documentation method is a seldomly considered variable. The purpose of this review was to evaluate the impact of documentation method for an alcohol-based nasal decolonization agent in reducing MRSA hospital-associated infections. Methods The study included all National Healthcare Safety Network (NHSN) defined hospital associated laboratory identified (Lab ID) MRSA blood infections from July 2019 through March 2023. Data was divided into descriptive categories with number of infections noted. The baseline period included population specific decolonization (n=35), followed by universal decolonization documentation via nursing task list (n=13), and concluded with electronic medication administration record (EMAR) implementation (n=7). The Kruskal Wallace Test was utilized for evaluation due to non-normative distribution. Results A Kruskal Wallace H Test indicated a statistical significance in medians between the task list and EMAR H(1) =5.10, p=0.024. There was no significance noted between the baseline and task list H(1 =0.04), p=0.841. Medians between baseline and EMAR approached significance but did not meet for this study H(1) =3.24, p=0.072. Clinically, there was a reduction in MRSA Lab ID of 48% between baseline and task list, followed by a 46% reduction between task list and EMAR. Overall reduction from baseline to EMAR was 80%. Conclusion Reduction of infection is crucial for patient safety and quality outcomes. Documentation is not typically highlighted as a risk reduction method. This study noted a significant decrease in hospital-associated MRSA Lab ID events after transition of documentation from the nursing task list to the EMAR. Disclosures All Authors: No reported disclosures

Long-term impact of contact precautions cessation for Methicillin-Resistant Staphylococcus Aureus (MRSA)

Methicillin-Resistant Staphylococcus aureus (MRSA) is a major cause of healthcare-associated infections (HAI). Contact isolation has been traditionally implemented to stop transmission but its impact is increasingly questioned. A single center, retrospective, nonrandomized, observational, quasi-experimental study compared MRSA HAI rates between pre-/postdiscontinuation of MRSA contact isolation in a tertiary university hospital over 68 months. Data on primary outcomes, Central line-associated bloodstream infections and MRSA LabID bacteremia events, were analyzed by interrupted time series design using segmented Poisson regression modeling. As secondary outcomes catheter-associated urinary tract infections , ventilator-associated pneumonia , surgical site infections and hospital-associated pneumonia were compared using Fisher's exact tests. Current savings due to discontinuation were calculated based on gown use. Two hundred and ninty-five patients developed 399 HAIs. Infection rates between pre- and postinterventions were as follows: Central line-associated bloodstream infections: (0.02% vs 0.02%; P-value=.64), MRSA LabID events: (0.01% vs 0.02%; P-value=.32), hospital-associated pneumonia: (0.01% vs 0.01%; P-value=.64), catheter-associated urinary tract infections: (0% vs 0.01%; P-value=.56), ventilator-associated pneumonia: (0.01% vs 0.01%; P-value=.32), surgical site infections (0.55% vs 0.15%; P-value=.03). Savings amount to $139,228 annually. Discontinuing CP did not negatively impact endemic MRSA HAI rates between pre-postdiscontinuation periods and saved costs for isolation materials.

Rates of Clostridioides difficile at an Urban Hospital During Initial COVID Pandemic Wave

BackgroundDuring the first three months of the COVID 19 pandemic, our facility cared for an influx of patients. At the peak, the daily census exceeded 200 patients with COVID 19. Surveillance for healthcare acquired infections (HAIs) continued throughout this time. Despite the acuity of the patients and the frequent use of antibiotics, the rates of Clostridioides difficile lab ID (CDI) events remained relatively stable. We sought to determine the validity of this rate.MethodsTo determine if cases of CDI were missed, we compared the 3-month rate per 10,000 patient days in 2020 to the same time-period in 2019 (March, April, and May). The number of tests ordered during the two periods was also compared. Additionally, the Doctor of Pharmacy from our antibiotic stewardship team reviewed all orders for oral Vancomycin to determine if empiric CDI treatment was initiated without confirmatory testing.ResultsThe CDI rate for the 3 months in 2019 was zero compared to 0.48 per 10,000 patient days during the peak of the pandemic. The number of tests increased in the 2020 period to 17.5 per 10,000 patient days versus 15.8 in the 2019 period. Three patients received oral Vancomycin, each of whom had valid indications.ConclusionsBased on this data, CDI cases were not underreported. We speculate that the lack of an increase in CDI rates may be attributed to: Increased hand hygiene by staff – compliance increased to 91 % in 2020 compared to 83 % in 2019,enhanced attention to cleaning and high level disinfection, and Improved adherence to use of personal protective equipment.

14. Outcomes from the AHRQ Safety Program for Improving Antibiotic Use Across 439 Long-term Care Facilities

BackgroundImplementing effective antibiotic stewardship programs (ASPs) in long-term care (LTC) settings is challenging. We present the results of an intervention intended to change the culture of antibiotic prescribing in 439 United States LTC facilities (LTCF).MethodsThe LTC Safety Program assisted LTCFs with establishing and implementing ASPs from 12/2018 to 11/2019. Through webinars held 1–2 times per month and other educational content, the Safety Program emphasized 1) the science of safety to improve teamwork and identify antibiotic-associated harm and 2) clinical best practices in making antibiotic treatment decisions. Content was organized using the Four Moments of Antibiotic Decision Making Framework (Figure 1). All staff (e.g., physicians, nurses, nurse assistants) were encouraged to participate. LTCFs submitted monthly antibiotic days of therapy (DOT), numbers of new antibiotic starts, urine cultures (UCX) ordered, Clostridioides difficile LabID events, and census data. Generalized linear mixed effects models were used to calculate pre-post intervention changes at bi-monthly intervals for antibiotic DOT, antibiotic starts and UCX, each per 1,000 resident-days (RD), and C. difficile LabID events per 10,000 RD, comparing the beginning (1/2019 and 2/2019) and end (11/2019 and 12/2019) of the Safety Program.Figure 1. Four Moments of Antibiotic Decision Making in the Long-Term Care SettingResultsOf 439 LTCFs who completed the Safety program, the majority were mid-sized (75–149 beds; 229, 52.2%), most were non-hospital based and owned by a larger system (246, 56.0%), with similar distributions between urban and rural settings. Of these, 348 (79%) submitted both baseline and end-of-intervention data. Antibiotic starts decreased from 7.89 to 7.48 starts/1000 RD; P = 0.02). Days of therapy for all antibiotics decreased from 64.1 to 61.0 DOT/1,000 RD; P = 0.068) and for fluoroquinolones (an antibiotic targeted in the Safety Program) from 1.49 to 1.28 DOT/1,000RD; P=0.002. UCX decreased from 3.01 to 2.63 orders/1000 RD; P = 0.001). There were no significant differences in C. difficile LabID events Table 1. Table 1. Changes from baseline (Jan-Feb, 2019) to the end (Nov-Dec, 2019) of the AHRQ Safety Program ConclusionBy targeting both antibiotic prescribing culture and knowledge of best practices, the AHRQ Safety Program led to significant reductions in antibiotic use across a large cohort of LTCFs.Disclosures Morgan Katz, MD, MHS, AHRQ (Research Grant or Support)FutureCare Health Systems (Consultant)Roche (Advisor or Review Panel member) Robin Jump, MD, PhD, Accelerate (Grant/Research Support)Merck (Grant/Research Support)Pfizer (Grant/Research Support, Advisor or Review Panel member)Roche (Advisor or Review Panel member)

Implementation of Two-Step Clostridioides difficile Testing Algorithm and Management of Possible Carriers

Background: Updated IDSA-SHEA guidelines recommend different diagnostic approaches to C. difficile depending on whether There are pre-agreed institutional criteria for patient stool submission. If stool submission criteria are in place, nucleic acid amplification testing (NAAT) alone may be used. If not, a multistep algorithm is suggested, incorporating various combinations of toxin enzyme immunoassay (EIA), glutamate dehydrogenase (GDH), and NAAT, with discordant results adjudicated by NAAT. At our institution, we developed a multistep algorithm leading with NAAT with reflex to EIA for toxin testing if NAAT is positive. This algorithm resulted in a significant proportion of patients with discordant results (NAAT positive and toxin EIA negative) that some experts have categorized as possible carriers or C. difficile colonized. In this study, we describe the impact of a multistep algorithm on hospital-onset, community-onset, and healthcare-facility–associated C. difficile infection (HO-CDI, CO-CDI, and HFA-CDI, respectively) rates and the management of possible carriers. Methods: The study setting was a 399-bed, tertiary-care VA Medical Center in Richmond, Virginia. A retrospective chart review was conducted. The multistep C. difficile testing algorithm was implemented June 4, 2019 (Fig. 1). C. difficile testing results and possible carriers were reviewed for the 5 months before and 4 months after implementation (January 2019 to September 2019). Results: In total, 587 NAATs were performed in the inpatient and outpatient setting (mean, 58.7 per month). Overall, 123 NAATs (21%) were positive: 59 in the preintervention period and 63 in the postintervention period. In the postintervention period, 23 positive NAATs (26%) had a positive toxin EIA. Based on LabID events, the mean rate of HO+CO+HCFA CDI cases per 10,000 bed days of care (BDOC) decreased significantly from 9.49 in the preintervention period to 1.15 in the postintervention period (P = .019) (Fig. 2). Also, 9 of the possible carriers (22%) were treated for CDI based on high clinical suspicion, and 6 of the possible carriers (14%) had a previous history of CDI. Of these, 5 (83%) were treated for CDI. In addition, 1 patient (2%) converted from possible carrier to positive toxin EIA within 14 days. The infectious diseases team was consulted for 11 possible carriers (27%). Conclusions: Implementation of a 2-step C difficile algorithm leading with NAAT was associated with a lower rate of HO+CO+HCFA CDI per 10,000 BDOC. A considerable proportion (22%) of possible carriers were treated for CDI but did not count as LabID events. Only 2% of the possible carriers in our study converted to a positive toxin EIA.Funding: NoneDisclosures: None

Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

Background: The NHSN has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than are EIAs. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017 through June 30, 2018. Methods: Calendar quarters for which CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT vs EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as pattern EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference of SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate SIRs, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIA clustered at the lower end of the histogram versus rates for NAAT (Fig. 1). The SIR distributions of both NAAT and EIA overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIR (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distributions of both NAAT and EIA substantiate the soundness of NHSN risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Disclosures: NoneFunding: None

Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

Background: The National Healthcare Safety Network (NHSN) has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than EIA use. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017, through June 30, 2018. Methods: Calendar quarters where CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO-CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following 2 analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT versus EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference in SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate an SIR, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIAs clustered at the lower end of the histogram versus rates for NAATs (Fig. 1). The SIR distributions, both NAATs and EIAs, overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIRs (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distribution for both NAAT and EIA substantiate the soundness of the NHSN’s risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Funding: NoneDisclosures: None

Epidemiology of Hospital Onset Staphylococcus aureus Bloodstream Infections (HO-SA-BSI) in the Era of MRSA LabID Reporting

Background: Acute-care hospitals began reporting methicillin-resistant Staphylococcus aureus (MRSA) LabID facility-wide inpatient events to the NHSN in 2013. Few data are available regarding the epidemiology of these patients. Methods: We conducted a retrospective cohort study of patients who developed hospital onset Staphylococcus aureus bloodstream infections (HO-SA-BSIs) to describe the epidemiology (characteristics and outcomes) from January 2014 through June 2019 and to compare MRSA LabID BSIs to HO-MSSA BSIs. Proportions were compared using 2 and continuous variables using the Kruskal-Wallis test (EpiInfo). Results: Overall, 264 HO-SA BSIs occurred over the study period (2.21 per 10,000 patient days), 160 HO-MSSA BSIs (1.34 per 10,000 patient days), and 104 MRSA LabID BSIs (0.869 per 10,000 patient days). These rates have not significantly changed over time (Fig. 1). Most of these patients were men (64%); 42.4% were African-American; mean age was 43.5 years; mean Charlson comorbidity index was 3.2; 67.8% were admitted for medical care (vs surgical); and 13.3% had a previous history of S. aureus infection. Of all HO-SA-BSIs, 49.2% were acquired in the ICU, 53.8% were primary BSIs, and 37.9% were catheter associated. Patients were hospitalized a mean of 19.9 days prior to HO-SA BSI, and the mean overall length of stay was 48.5 days. Compared to HO-MSSA BSIs, there were no significant differences in these characteristics among MRSA LabID BSIs except that a significantly greater proportion were catheter associated (46.2% vs 32.5%; OR, 1.78; 95% CI, 1.07–2.96; P = .04). Overall, 101 patients (38.3%) died: 41 with MRSA LabID BSI (39.4%) and 60 with HO-MSSA BSI (37.5%). Mortality rates have not changed significantly over time. The mean number of days to death was 154.2, and 59 patients (22.3%) died during incident hospitalization: 26.9% of MRSA patients and 19.4% of MSSA BSI patients. Moreover, 28.3% of patients were readmitted within 30 days of discharge from incident hospitalization, and compared to HO-MSSA BSI, this rate was significantly higher among MRSA LabID BSI patients (34.2% vs 24.8%; OR, 2.07; 95% CI, 1.09–3.93; P = .03). Among those who died, 58.4% died during hospitalization, 52.5% died within 30 days, 66.3% died within 60 days, and 74.3% had died within 90 days. Also, 47.5% died as a result of their HO-SA BSI, and compared to HO-MSSA BSI, this rate was significantly higher among those with MRSA LabID-BSI (63.4% vs 36.7%; OR, 2.99; 95% CI, 1.31–6.83; P = .02). Conclusions: Among patients with HO-SA BSI, methicillin-resistance continues to be associated with higher attributable mortality, and in our study, higher rates of 30-day readmission. There has been no significant change in HO-SA BSI rates (MSSA or MRSA) since reporting for MRSA LabID events began. Furthermore, mortality rates have not changed and remain high for both MRSA BSI and MSSA BSI patients. Given these findings, MSSA LabID event reporting should be considered.Funding: NoneDisclosures: None

Is Hospital-Onset Bloodstream Infection (HOBSI) a Useful Measure to Evaluate Infection Prevention Progress?

Background: Acute-care hospitals in the United States are required to submit 6 healthcare-associated infection (HAI) metrics to the CMS for reporting and performance purposes prior to payment. We examined the association between HAI rate trends and hospital-onset bloodstream infection (HO-BSI) rate trends across a large, multihospital health system. Methods: HO-BSI events were identified across 52 hospitals attributable to Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, or Candida spp using the NHSN Lab ID event definition of ≥day 4 of admission. We compared the performance from January 2016 to March 2019 for HO-BSI and the 6 NHSN-defined HAIs: central-line–associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), Clostridioides difficile, methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, abdominal hysterectomy surgical site infections (SSIs), and colon SSI. We calculated 2 “infection composite scores” to account for the 6 HAIs based on all observed or predicted events (score 1) and an average of the 6 HAI standardized infection ratios (SIRs; score 2). We normalized both measures to 1 for a 12-months rolling baseline. We evaluated the HO-BSI rate change over time and compared it to the change in the infection score over the same period. We compared the change in the 12-month rolling rates of the 2 HAI scores and the HO-BSI rate. Results: During the 39-month period, 3,288 HO-BSI events occurred over 9,775,118 patient days. The source of HO-BSI events included S. aureus (33.5%), P. aeruginosa (10.2%), E. coli (19.7%), K. pneumoniae (13.8%), and Candida spp (22.8%). HO-BSI event rates decreased by 17.3% from 12-month rolling baseline to last 12 months (3.70 vs 3.06 per 10,000 patient days). Similarly, 7,648 HAI events were observed, with the source of events being Clostridioides difficile (57.0%), CAUTI (15.1%), CLABSI (12.8%), MRSA (7.0%), colon SSI (6.4%), and abdominal hysterectomy SSI (1.7%). The 2 HAI scores and the HO-BSI rate all showed a notable decrease from the 2016 baseline period (Fig. 1). The reductions in the HAI scores were both strongly correlated with the reduction in the HO-BSI rate, with the HAI score 1 having a stronger correlation (r = 0.949; P < .001) than was observed for HAI score 2 (r = 0.867; P < .001). Conclusions: Utilization of a HO-BSI measure may prove useful as a correlated but distinct marker of infection prevention improvement or trends. HO-BSI could be useful as an objective electronically obtainable measure to assist in the evaluation of performance within and across facilities.Funding: NoneDisclosures: None

Identifying Opportunities to Improve Accuracy of NHSN Reporting: Lessons Learned From State Health Department Validations.

Background: State Health Departments (SHDs) have systematically studied the validity of healthcare-associated infection (HAI) surveillance data submitted by healthcare facilities in their jurisdictions to the Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) for central-line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections following colon and abdominal hysterectomy procedures (SSI COLO and HYST), methicillin-resistant Staphylococcus aureus and Clostridioides difficile laboratory identified (MRSA and CDI LabID respectively) events. These studies are a key source of information about data quality and completeness serving as an impetus and a guide for improving the caliber of NHSN’s HAI data. Methods: We contacted SHD HAI coordinators in all states for an inventory of HAI validation studies. We used data from these studies to calculate pooled mean sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HAI case determinations. HAI case reporting “error rates” were computed as the proportion of mismatches (underreport and overreport) among the medical records reviewed by SHDs and reasons for misclassification were categorized. Results: SHD validation studies varied by HAI type (range, 4 studies for MRSA LabID and 23 for CLABSI). Pooled mean sensitivity of HAI reporting ranged from 73.1% (COLO SSI) to 92.7% (CDI LabID). Pooled mean specificity and PPV exceeded 90% for all HAIs. LabID event validations demonstrated the lowest NPV (58.8% for MRSA and 55.1% for CDI). Error rates of HAI reporting to NHSN ranged from 2.5% (HYST SSI) to 13.6% (MRSA LabID). Common errors identified during CLABSI and CAUTI validations were incorrect application of general NHSN and CLABSI- and CAUTI-specific definitions. Incorrect secondary BSI attribution was the most frequently identified reason by CLABSI SHD validations (64.7%). Of all operative procedure-associated misclassifications, inconsistent surveillance practices (66.6%), incorrect NHSN operative procedure category assignment (55.5%), and misapplication of general organ-space and/or site-specific infection criteria (44.4%) were identified as the most common shortcomings. Among MRSA and CDI LabID validations, missed case finding due to failure to review candidate events and gaps in understanding the 14-day reporting rule of LabID protocol were identified as predominant reasons for inaccurate reporting. Conclusions: SHD HAI data validations identified specific targets for additional surveillance training, especially CLABSI determinations and application of the protocol rules for MDRO/CDI LabID case determinations. Further work is also needed to assure that data sources in addition to wound cultures are used for SSI determinations and that postdischarge SSI surveillance is more vigorous and comprehensive.Funding: NoneDisclosures: None

Potential for the current National Healthcare Safety Network (NHSN) >3 days after admission definition of laboratory-identified, healthcare-facility-onset, Clostridioides difficile infection (HO-CDI) to overestimate rates.

Healthcare-facility-onset C.difficile LabID events are defined as positive stool samples collected >3 days after hospitalization. Using a definition of >72 hours, we found that 84 of 1013 cases (8.3%) identified as C. difficile LabID events were collected between 48 and 72 hours after admission.

2363. Implementation of a Multi-Step Diagnostic Algorithm for C. difficile Infection

BackgroundThe diagnosis of C. difficile infection (CDI) in the hospital is challenging asymptomatic colonization rates vary between 3% and 26%. Guidelines recommend multistep testing for CDI diagnosis. On July 1, 2018 a two-step testing algorithm was implemented at our institution. Positive nucleic acid amplification test (NAAT) results reflexed to a toxin enzyme immunoassay (EIA) test. The EIA test result was then used for NHSN reporting; however, both test results were visible to the clinician. Updated guidance on the interpretation of the test and treatment of CDI was released to the medical staff in July. We compared the incidence of CDI lab ID events per 1000 patient-days and the rate of C. difficile antibiotic starts before and after the implementation of the testing algorithm.MethodsA retrospective observational study was performed at an 800 bed regional medical center. CDI lab ID events between January 1 and December 31, 2018 were reviewed. Antibiotic initiation of intravenous (IV) and oral (PO) metronidazole and PO vancomycin was collected for all hospitalized patients diagnosed with C. difficile. The incidence of hospital onset (HO) and community-onset (CO) lab ID events as well as the rate of antibiotic starts were compared before and after implementation of the algorithm using a two-sided z test for proportions with an alpha of 0.05.ResultsThe incidence of HO and CO lab ID events per 1000 patient-days decreased significantly from 0.56 to 0.16 (P < 0.0001) and 1.18 to 0.3 (P < 0.0001) after implementation of the testing algorithm (Figure 1). The CDI SIR decreased from 0.729 to 0.322, (P = 0.0048). The rate of antibiotic starts per 1,000 patient-days for IV and PO Metronidazole decreased significantly from 1.1 to 0.45 (P < 0.0001) and 0.86 to 0.35 (P < 0.0001), respectively. PO Vancomycinstarts decreased from 1.51 to 1.23 (P = 0.11) (Table 1).ConclusionA two-step algorithm for diagnosing CDI decreases the overall number of HO and CO C. difficile lab ID events and decreases overall antimicrobial use for CDI. Disclosures All authors: No reported disclosures.

533. Impact of Different Definitions on Reported Rates of Carbapenem-Resistant Enterobacteriaceae (CRE)

BackgroundDifferent definitions exist for tracking and trending rates of hospital-acquired Carbapenem-resistant Enterobacteriaceae (CRE). National Health Safety Network (NHSN) allows for Laboratory Identified Event (LabID) and Healthcare-associated infection (HAI) surveillance reporting for CRE. Our facility developed an internal definition for CRE prior to release of the NHSN modules that differs from the CDC definitions in that patients colonized or infected with CRE identified on hospital day 1 or 2 who had a hospitalization within the past four weeks are considered community-onset healthcare facility acquired (COHCFA) and are included in our HA definition. In addition, by our definition once a patient develops CRE any subsequent positive cultures for the same organism are not considered new events.MethodsAll CRE cultures at our facility were reviewed by an infection preventionist and hospital epidemiologist who categorized each culture as hospital acquired by our internal HA definition, NHSN LabID definition and NHSN HAI definition. Results from each method of surveillance were compiled and compared as were trends of HA CRE over time by each definition.Results590 patients with 975 clinical cultures for Carbapenem-resistant Klebsiella spp., Enterobacter spp and E. coli were reviewed from January 2012 to March 2019. 297 cultures met our internal definition for HA CRE compared with 302 by NHSN LabID and 189 by NHSN HAI surveillance. Sixty-one (21%) of HA cases by our definition were COHCFA. 259 patients had multiple CRE cultures and 1 patient had 22 cultures with the same CRE organism between 2014 and 2019 and met for 5 lab ID events and 5 NHSN HAI events. All 3 tests agreed that a culture was HA in 140 instances (14%) and all 3 agreed that a culture was not HA in 589 instances (60%). At least one definition yielded a discordant result in 246 cultures (25%). Trends over time were compared between the definitions. While the number of HA cases varied based on the definition used, overall trends over time were similar regardless of the definition utilized. (Figure 2)ConclusionRegardless of the definition used for surveillance of CRE, trends over time are similar. Consideration should be given to monitoring COHCFA cases in addition to those acquired on or after hospital day 3. Disclosures All authors: No reported disclosures.

1881. The Agency for Healthcare Research and Quality (AHRQ) Safety Program for Improving Antibiotic Use: Results From a National Antibiotic Stewardship Intervention of 402 United States (US) Hospitals

BackgroundThe AHRQ Safety Program for Improving Antibiotic Use aims to improve antibiotic (abx) use in acute, long-term, and outpatient care settings by enhancing abx stewardship programs (ASP) and engaging frontline providers to incorporate stewardship into daily abx decision-making, with an emphasis on viewing appropriate prescribing as a patient safety issue. We report on the impact of implementation of the Acute Care Safety Program on abx use and Clostridioides difficile in a cohort of US hospitals.MethodsThe Acute Care Safety Program was implemented from December 2017 to November 2018. At least one unit from each hospital participated. The Safety Program trained local ASP leaders and assisted ASPs and frontline staff to: (a) address attitudes and culture that pose challenges to judicious abx use and (b) incorporate best practices for the management of common infections into daily practice using the Four Moments of Antibiotic Decision Making framework (Figure 1). Education occurred via 17 live Webinars and an online toolkit that included recorded Webinars, narrated presentations, and other tools to assist with the development and dissemination of syndrome-specific local guidelines (Table 1). Units submitted days of abx therapy (DOT) per 1,000 patient-days (PD), C. difficile LabID events per 10,000 PD, and 10 review forms per month documenting structured discussions between the ASP and frontline staff about patients on abx. Linear and generalized linear mixed-effects models were employed to calculate pre-post intervention changes in abx use and C. difficile LabID events, respectively.Results402 hospitals completed the Safety Program, including 28 (7%) academic medical centers (AMC), 289 (72%) community hospitals, and 85 (21%) critical access hospitals. 476 participating units consisted of 165 (35%) ICUs, 300 (63%) medical-surgical floors, and 11 (2%) other units. Both abx use and C. difficile LabID events decreased when comparing pre-post data (−41 DOT per 1,000 PD, [from 886.56, Figure 2], P = 0.001 and −1.2 LabID events per 10,000 PD [from 6.3], P = 0.027), respectively.ConclusionBy targeting both improving abx prescribing culture and knowledge of best practices, the AHRQ Safety Program led to reductions in abx use across a diverse cohort of hospitals. DisclosuresSara E. Cosgrove, MD, MS, Basilea: Consultant; Theravance: Consultant.

2348. Incorporating Electronic Medical Record Hard Stops to Reduce Inappropriate Clostridioides difficile Testing at an Academic Medical Center: A Quality Improvement Study

Background Clostridioides difficile is the most common pathogen causing healthcare-associated infections. This study highlights the multi-disciplinary efforts to reduce C. difficile infections (CDI) at a large, tertiary care teaching facility.MethodsA quality improvement study was performed between March 2017 and April 2018, using six Plan-Do-Study-Act cycles that included transmission prevention, diagnostic stewardship, education, and antimicrobial stewardship. Process measures included hand hygiene, isolation precautions, low-level disinfection compliance, number of tests ordered, lab cancelation of tests, and compliance with the Electronic Medical Record (EMR) hard stop for patients with laxative use, and negative C.difficile test in the past 7 days.ResultsA total of 2,046 C. difficile tests were ordered during the initiative. Of the 124 patients with a positive C. difficile LabID event, 50% were male with a median age of 65 years (range: 11–92 years). A 53% reduction in C. difficile LabID events (7.5 to 4 events per 10,000 patient-days, P < 0.001), with a pronounced decrease between cycle 4 and 5 (5.4 to 2.9 events per 10,000 patient-days, P < 0.001) was achieved. The largest decrease in C. difficile lab tests ordered was seen after implementation of the EMR hard-stop (cycle 5), with fewer than 0.5 LabID events per 1,000 patient-days for each subsequent month after EMR hard-stop implementation. Frequent reasons for physician phone calls to Infection prevention department was related to chronic use of lactulose in patients with cirrhosis (30%) and unexplained diarrhea (70%). Based on provider feedback, EMR changes were made to remove lactulose from the hard-stop and offer infectious disease consultation upfront. There was 99% compliance with electronic medical record hard stop. There was a nonsignificant increase in lab cancelations due to inappropriate stool specimens over time (1.9% to 3.1% from cycle 1 to 6, P = 0.28) A 55% reduction in hospital-onset CDI surveillance events (from 6.9 to 3.2 per 10,000 patient-days, P < 0.001) was noted.ConclusionA multi-disciplinary Quality Improvement initiative is a successful strategy in reducing CDI events, with the largest decrease seen with introduction of EMR hard stops. Disclosures All authors: No reported disclosures.

2373. Impact of a Change in Testing Strategy for Clostridioides difficile Infection on a Publicly Reported Metric and Treatment Days of Therapy

BackgroundIn an effort to optimize diagnostic testing for Clostridioides difficile infection (CDI) our health system changed from stand-alone PCR testing to a “2-step” approach wherein all positive PCR results reflexed to an EIA. We report the effects of this change on publicly reported CDI metrics and treatment days of therapy (DOT).MethodsThe setting includes 10 Cleveland Clinic Health System hospitals in northeast Ohio and one in Florida. On June 12, 2018, 9 NE Ohio hospitals changed from PCR alone to PCR followed by EIA. Stand-alone PCR testing remained at one and GDH / EIA / PCR for discordant for another. Testing volumes were obtained from the microbiology laboratory. C. difficile LabID event SIRs were obtained from NHSN. Public reporting interpretative categories were identified based on SIR for second half of 2018. DOT for CDI agents were obtained from an antimicrobial stewardship database.ResultsAmong hospitals that changed strategy the volume of PCR testing and the percent PCR + was similar between time periods. EIA positivity ranged from 23% to 53%. 4/11 hospitals improved their public reporting category: 3/9 that changed testing strategy and 1/2 that did not (Table 1). Two of 3 that changed strategy and improved public reporting also had a decrease in DOT. DOT increased in the 2 hospitals that did not change strategy.ConclusionSix months after adopting a 2-step CDI testing strategy 7 of 9 hospitals had a lower SIR with 3 also demonstrating an improvement in public reporting category favorably impacting reputational and reimbursement risk for our healthcare system. CDI agent DOT was similar before and after the change. The impact of choice of test on publicly reported metrics demonstrates the difficulty of utilizing a proxy for hospital onset CDI, the CDI LabID event, as a measure of quality of care provided. Disclosures All authors: No reported disclosures.

2353. Easy Does It: Decreasing and Sustained Hospital-Onset (HO) CDI Lab-ID Event Incidence Through a Series of Interventions

Background Clostridioides difficile infection (CDI) Laboratory (Lab) identified (ID) events are reportable to CMS through the CDC’s NHSN. Prevention of transmission has been the main component of interventions; however, avoiding false-positive laboratory diagnoses can also lead to decreased incidence.MethodsA retrospective analysis of HO-CDI Lab-ID events was conducted to evaluate the results of a series of interventions at the University of Alabama Hospital, a 1150-bed tertiary care center in Birmingham, AL. The study period was from the first quarter of 2013 (1Q 2013) until 1Q of 2019. Interventions were implemented in sequential order were: (i) CDI prevention bundle education (3Q 2014); (ii) two-step laboratory testing algorithm (2Q 2015); (iii) selective enhanced environmental disinfection on oncology units (2Q 2016); (iv) diagnostic stewardship by reminding providers to reconsider testing if the patient received a laxative within 48 hours (4Q 2016).ResultsAt the beginning of the study period, the HO CDI Lab ID Event SIR was 0.96. The standard infection ratio (SIR) over the time period is shown in Figure 1. We observed a slight decrease in HO-CDI Lab ID event SIR after implementation of the CDI prevention bundle (0.96 vs. 0.77). A change in the diagnostic testing from PCR-based to a two-step algorithm (EIA testing for GDH and Toxin confirmed by PCR) resulted in a slight increase although not statistically significant (0.77 vs. 0.83). A downward trend was observed when selective enhanced terminal disinfection with hydrogen peroxide vapor was performed on all oncology patient rooms vacated by patients with CDI (0.83 vs. 0.72). The largest and sustained impact was observed after implementation of a computer-assisted diagnostic stewardship in which providers were reminded if the patient was administered any stool softener or laxative within 48 hours of the order for CDI testing (0.72 vs. 0.32). The institutions SIR value became significant in 2Q 2016 (P = 0.0014) and significance was maintained since that time. The difference between expected and observe HO-CDI Lab ID events is demonstrated in Figure 2.ConclusionThrough a series of interventions, we observed a decrease in HO-CDI event rates. Diagnostic stewardship with academic detailing resulted in the most impactful and sustained improvement. Disclosures All authors: No reported disclosures.

Collect that Stool! The Impact of Delay in Specimen Collection to NHSN Lab ID C. difficile Events

BACKGROUND Despite multiple rapid laboratory tests available for Clostridium difficile infection (CDI), delays in specimen collection may contribute to delays in diagnosis and treatment. Additionally, specimen collection delays may contribute to falsely increased National Health and Safety Network (NHSN) laboratory-identified (Lab ID) hospital onset (HO) CDI rates. Time from order to specimen collection and the impact of collection delays on HO CDI rates is unknown. METHODS Two community hospitals (450 and 472 licensed beds) conducted a retrospective review of electronic data from March-October 2018, including date of order for C. difficile toxin assay and date of specimen collection. Time to collection was compared for hospital of admission, level of care (intensive care unit [ICU] versus other) and shift of order (day versus night) using Mann-Whitney U (SPSS) and Chi-square analysis (Epi Info). The impact of delay on HO CDI cases was assessed. RESULTS 1516 stool specimens were collected. On average, there were 7.7?hours between order and collection (maximum delay = 7.9 days). Hospital A, ICU level of care, and night shift were correlated with significantly shorter time to specimen collection (p = 0.01, 0.02. 0.02, respectively). 330 specimens (22%) were collected on a subsequent calendar day, although this was only significantly impacted by level of care (p = 0.04). Of the 31 cases determined to be HO-CDI per NHSN Lab ID definition, 4 (13%) would have been CO had they been collected on the day ordered. Conclusions In this sample, delay in specimen collection for C. difficile testing was common, with many tests delayed to the next day. This may have a considerable impact on HO CDI rates, impacting reimbursement and hospital reputation. Future research is needed to remedy these delays. Until that time, use of order date and time should be considered for inclusion into NHSN Lab ID event definitions.

Burden of Clostridioides difficile in a State's Long-term Care Facilities

BACKGROUND Nearly 500,000 persons in the United States developed a Clostridioides difficile infection (CDI) in 2015 and more than 100,000 of these infections were in residents of long-term care facilities (LTCF). It is difficult to estimate the true burden of CDI in LTCF as reporting is not required. To gain a better understanding of CDI, the state health department (SHD) encourages LTCF to enroll and report CDI data into the National Healthcare Safety Network (NHSN). METHODS The SHD analyzed CDI data reported into NHSN's Long-Term Care Component from March 2017 to October 2018. Descriptive analyses were performed on CDI LabID events which were categorized as community-onset (CO), long-term care onset (LO) and acute care-transfer-long-term care onset (ACT-LO) according to NHSN definitions. CDI rates were also calculated based on the NHSN definitions. RESULTS As of October 2018, 44 out of 320 LTCFs in the state were enrolled in NHSN. The median bed size was 103 (38–444). Between March 2017–October 2018, 29 out of 44 facilities reported 174 CDI LabID events with a range of 1–40. Of the total CDI events, 31 events were CO, 143 were LO and 64 LO events were categorized as ACT-LO. The overall CDI rate was 0.70 per 10,000 resident days. The CDI LO-Incidence rate was 0.52 per 10,000 resident days compared to a rate of 5.05 in acute care hospitals during the same period. CONCLUSIONS 14% of long-term care facilities in the state are reporting CDI data voluntarily into NHSN. These data allow the SHD to better characterize the true burden of CDI statewide. Accurate reporting of infection data is imperative in order to establish a baseline infection rate and guide infection prevention strategies. Based on the variation in the data, the SHD plans to perform external validation of CDI data and education on NHSN definitions.