As the third edition of the Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals is released with the latest recommendations for the prevention and management of healthcare-associated infections (HAIs), a new approach to reporting HAIs is just beginning to unfold. This next generation of HAI reporting will be fully electronic and based largely on existing data in electronic health record (EHR) systems and other electronic data sources. It will be a significant change in how hospitals report HAIs and how the Centers for Disease Control and Prevention (CDC) and other agencies receive this information. This paper outlines what that future electronic reporting system will look like and how it will impact HAI reporting.

During March 27–July 14, 2020, the Centers for Disease Control and Prevention’s National Healthcare Safety Network extended its surveillance to hospital capacities responding to COVID-19 pandemic. The data showed wide variations across hospitals in case burden, bed occupancies, ventilator usage, and healthcare personnel and supply status. These data were used to inform emergency responses.

Using data from the National Healthcare Safety Network (NHSN), we assessed changes to intensive care unit (ICU) bed capacity during the early months of the COVID-19 pandemic. Changes in capacity varied by hospital type and size. ICU beds increased by 36%, highlighting the pressure placed on hospitals during the pandemic.

Background: Hospital-onset bacteremia and fungemia (HOB) may be a preventable hospital-acquired condition and a potential healthcare quality measure. We developed and evaluated a tool to assess the preventability of HOB and compared it to a more traditional consensus panel approach. Methods: A 10-member healthcare epidemiology expert panel independently rated the preventability of 82 hypothetical HOB case scenarios using a 6-point Likert scale (range, 1= “Definitively or Almost Certainly Preventable” to 6= “Definitely or Almost Certainly Not Preventable”). Ratings on the 6-point scale were collapsed into 3 categories: Preventable (1–2), Uncertain (3–4), or Not preventable (5–6). Consensus was defined as concurrence on the same category among ≥70% expert raters. Cases without consensus were deliberated via teleconference, web-based discussion, and a second round of rating. The proportion meeting consensus, overall and by predefined HOB source attribution, was calculated. A structured HOB preventability rating tool was developed to explicitly account for patient intrinsic and extrinsic healthcare-related risks (Fig. 1). Two additional physician reviewers independently applied this tool to adjudicate the same 82 case scenarios. The tool was iteratively revised based on reviewer feedback followed by repeat independent tool-based adjudication. Interrater reliability was evaluated using the Kappa statistic. Proportion of cases where tool-based preventability category matched expert consensus was calculated. Results: After expert panel round 1, consensus criteria were met for 29 cases (35%), which increased to 52 (63%) after round 2. Expert consensus was achieved more frequently for respiratory or surgical site infections than urinary tract and central-line–associated bloodstream infections (Fig. 2a). Most likely to be rated preventable were vascular catheter infections (64%) and contaminants (100%). For tool-based adjudication, following 2 rounds of rating with interim tool revisions, agreement between the 2 reviewers was 84% for cases overall (κ, 0.76; 95% CI, 0.64–0.88]), and 87% for the 52 cases with expert consensus (κ, 0.79; 95% CI, 0.65–0.94). Among cases with expert consensus, tool-based rating matched expert consensus in 40 of 52 (77%) and 39 of 52 (75%) cases for reviewer 1 and reviewer 2, respectively. The proportion of cases rated “uncertain“ was lower among tool-based adjudicated cases with reviewer agreement (15 of 69) than among cases with expert consensus (23 of 52) (Fig. 2b). Conclusions: Healthcare epidemiology experts hold varying perspectives on HOB preventability. Structured tool-based preventability rating had high interreviewer reliability, matched expert consensus in most cases, and rated fewer cases with uncertain preventability compared to expert consensus. This tool is a step toward standardized assessment of preventability in future HOB evaluations.

Funding: None

Disclosures: None

Background: Accurate identification of Clostridioides difficile infections (CDIs) from electronic data sources is important for surveillance. We evaluated how frequently laboratory findings were supported by diagnostic coding and treatment data in the electronic health record. Methods: We analyzed a retrospective cohort of patients in the Veterans’ Affairs Health System from 2006 through 2016. A CDI event was defined as a positive laboratory test for C. difficile toxin or toxin genes in the inpatient, outpatient, or long-term care setting with no prior positive test in the preceding 14 days. Events were classified as incident (no CDI in the prior 56 days), or recurrent (CDI in the prior 56 days) and were evaluated for evidence of clinical diagnosis based on International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM codes and at least 1 dose of an anti-CDI agent (intravenous or oral metronidazole, fidaxomicin, or oral vancomycin). We further assessed the possibility of treatment without testing by quantifying positive laboratory tests and diagnostic codes among inpatients receiving an anti-CDI agent. A course of anti-CDI therapy was defined as continuous treatment with the same drug. Results: Among 119,063 incident and recurrent CDI events, 70,114 (58.9%) had a diagnosis code and 15,850 (13.3%) had no accompanying treatment. The proportion of patients with ICD codes was highest among patients treated with fidaxomicin (82.6% of 906) or oral vancomycin (74.3% of 30,777) and was lower among patients receiving metronidazole (63.3% of 103,231) and those without treatment (29.9% of 15,850). The proportion of events with ICD codes and treatment was similar between incident and recurrent episodes. During the study period, there were ~470,000 inpatient courses of metronidazole, fidaxomicin, and oral vancomycin. Table 1 shows the presence of ICD codes and positive laboratory tests by anti-CDI agents. Among 51,100 courses of oral vancomycin, 51% had an ICD code and 44% had a positive test for C. difficile within 7 days of treatment initiation. Among 1,013 courses of fidaxomicin, 79% had an ICD code and 56% had a positive laboratory test. Conclusions: In this large cohort, there was evidence of substantial CDI treatment without confirmatory C. difficile testing and, to a lesser extent, some positive tests without accompanying treatment or coding. A combination of data sources may be needed to more accurately identify CDI from electronic health records for surveillance purposes.

Funding: None

Disclosures: None

Background: Regional changes in United States C. difficile infection (CDI) are not well understood but important for targeting prevention strategies. Methods: Community-onset (CO) CDI was defined as positive C. difficile stool tests collected on or before hospital day 3 (where admission was day 1), reported by acute-care hospitals to the CDC NHSN over 3 years: year 1, July 1, 2015–June 30, 2016; year 2, July 1, 2016–June 30, 2017; year 3, July 1, 2017–June 30, 2018. Healthcare facility-onset CDI (HO-CDI) was similarly defined but with stool collection after hospital day 3. Hospital referral regions (HRRs) were defined by the Dartmouth Atlas of Health Care, and they represent 306 healthcare markets. Standardized infection ratios (SIRs) were calculated using separate multivariable models for (1) CO-CDI events in an emergency department/observation unit (ED/Obs), (2) CO-CDI events among inpatients, and (3) HO-CDI, accounting for facility-level factors, They resulted in ratios of observed to predicted infections, similar to established methods. SIRs were pooled within each facility to create a hospital-identified SIR by summing observed and predicted events for CO-CDI events in both testing locations and HO-CDI events, then pooled by HRR by summing all facility observed and predicted events within the region. Data from facilities not within an HRR were excluded. Results: Total CO-CDI (ED/Obs and inpatient) and HO-CDI events decreased, even as the number of reporting facilities slightly increased over the 3-year period (Fig. 1). Among 306 HRRs in year 3, the median number of hospitals was 10 (IQR, 6–17), with a median of 526 (IQR, 272–1,002) hospital-identified CDI events per HRR. Variables significantly associated with CDI incident rate and included in SIR models 1–3 included C. difficile test type, hospital type, teaching affiliation, hospital bed size, and presence of an ED/Obs unit. Intensive care unit capacity was included in models 2 and 3, and the ratio of hospital admissions to emergency department encounters in model 1. Pooled mean HRR hospital-identified C. difficile SIRs decreased each year (0.972, 0.914, and 0.838), and decreases also varied by HRR (Fig. 2). Conclusions: National decreases in a combined hospital-identified C. difficile SIR are widespread but may be more aggregated in particular regions. Although SIR adjustments were limited to facility-level factors, aggregation of CDI SIR by HRR may be useful for infection preventionists and public health authorities to further understand regional CDI patterns.

Funding: None

Disclosures: None

Background: Hospitalists play a critical role in antimicrobial stewardship as the primary antibiotic prescriber for many inpatients. We sought to describe antibiotic prescribing variation among hospitalists within a healthcare system. Methods: We created a novel metric of hospitalist-specific antibiotic prescribing by linking hospitalist billing data to hospital medication administration records in 4 hospitals (two 500-bed academic (AMC1 and AMC2), one 400-bed community (CH1), and one 100-bed community (CH2)) from January 2016 to December 2018. We attributed dates that a hospitalist electronically billed for a given patient as billed patient days (bPD) and mapped an antibiotic day of therapy (DOT) to a bPD. Each DOT was classified according to National Healthcare Safety Network antibiotic categories: broad-spectrum hospital-onset (BS-HO), broad-spectrum community-onset (BS-CO), anti-MRSA, and highest risk for Clostridioides difficile infection (CDI). DOT and bPD were pooled to calculate hospitalist-specific DOT per 1,000 bPD. Best subsets regression was performed to assess model fit and generate hospital and antibiotic category-specific models adjusting for patient-level factors (eg, age ≥65, ICD-10 codes for comorbidities and infections). The models were used to calculate predicted hospitalist-specific DOT and observed-to-expected ratios (O:E) for each antibiotic category. Kruskal-Wallis tests and pairwise Wilcoxon rank-sum tests were used to determine significant differences between median DOT per 1,000 bPD and O:E between hospitals for each antibiotic category. Results: During the study period, 116 hospitalists across 4 hospitals contributed a total of 437,303 bPD. Median DOT per 1,000 bPD varied between hospitals (BS-HO range, 46.7–84.2; BS-CO range, 63.3–100; anti-MRSA range, 48.4–65.4; CDI range, 82.0–129.4). CH2 had a significantly higher median DOT per 1,000 bPD compared to the academic hospitals (all antibiotic categories P < .001) and CH1 (BS-HO, P = .01; anti-MRSA, P = .02) (Fig. 1A). The 4 antibiotic groups at 4 hospitals resulted in 16 models, with good model fit for CH2 (R2 > 0.55 for all models), modest model fit for AMC2 (R2 = 0.46–0.55), fair model fit for CH1 (R2 = 0.19–0.35), and poor model fit for AMC1 (R2 < 0.12 for all models). Variation in hospitalist-specific O:E was moderate (IQR, 0.9–1.1). AMC1 showed greater variation than other hospitals, but we detected no significant differences in median O:E between hospitals (all antibiotic categories P > .10) (Fig. 1B). Conclusions: Adjusting for patient-level factors significantly reduced much of the variation in hospitalist-specific DOT per 1,000 bPD in some but not all hospitals, suggesting that unmeasured factors may drive antibiotic prescribing. This metric may represent a target for stewardship intervention, such as hospitalist-specific feedback of antibiotic prescribing practices.

Funding: None

Disclosures: Scott Fridkin, consulting fee - vaccine industry (various) (spouse)

Hospital-onset bacteremia and fungemia (HOB), a potential measure of healthcare-associated infections, was evaluated in a pilot study among 60 patients across 3 hospitals. Two-thirds of all HOB events and half of nonskin commensal HOB events were judged as potentially preventable. Follow-up studies are needed to further develop this measure.