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Measuring Empiric Antibiotic Spectrum Patterns Across Space and Time
- Michael Yarrington, Rebekah Moehring, Deverick John Anderson, Rebekah Wrenn, Christina Sarubbi, Justin Spivey
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s2-s4
- Print publication:
- October 2020
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Background: Quantitative evaluation of antibiotic spectrum is an important, underutilized metric in measuring antibiotic use (AU) and may assist antimicrobial stewards in identifying targets and strategy for intervention. We evaluated the spectrum of initial antibiotic choices by hospital location, day of the week, and time of day to determine whether these factors may be associated with broad-spectrum antibiotic choices. Methods: We identified all admissions with antibiotic exposure in medical and surgical wards and critical care units in a tertiary academic medical center between July 1, 2014, and July 1, 2019. The antibiotic spectrum index (ASI), proposed by Gerber et al, is a numeric score based on the number of pathogens covered by a particular agent. We defined ASI for initial antibiotic choice as follows: ASI for each unique antibiotic administered within 24 hours of the first antibiotic administration was summed and assigned to the administration time of the first dose. We categorized time into 4 distinct categories: weekday days (Monday–Friday, 7 a.m.–7 p.m.), weekday nights, weekend days, and weekend nights. Weekend time began 7 p.m. Friday and ended 7 a.m. Monday. We constructed heatmaps stratified by hospital location. Mann-Whitney U tests were applied to evaluate differences in the distributions of ASI using weekday days as a reference. Results: Data included 90,455 unique antibiotic admissions with initial antibiotic starts in medical and surgical wards and critical care units. Patterns of ASI for initial antibiotic choice varied between unit locations and time (Figs. 1 and 2). Mean and median ASIs for initial antibiotic choices were higher for medical ward and medical ICUs than for surgical wards and surgical ICUs. Initial antibiotic choices had higher ASIs during overnight hours for all units except the surgical ICU. Notable differences in ASIs were identified between weekday and weekend prescribing for surgical units, whereas medical units demonstrated less extreme differences. Conclusion: We observed a “weekend effect” across hospital units; the most extreme occurred in surgical wards. This observation may be due to differences in patient volume and rounding patterns. For example, hospitalist and critical care units have 7-day schedules, whereas surgical wards are highly influenced by operating room schedules. Antimicrobial stewardship teams may use these data to identify strategies targeting the most opportune time and place to intervene on the spectrum of initial antibiotic choice.
Funding: None
Disclosures: None
Alcohol Hand Rub Significantly Reduces Overall Bacterial Bioburden on Stethoscopes in a Real-World Clinical Setting
- Alexandra Johnson, Bobby Warren, Deverick John Anderson, Melissa Johnson, Isabella Gamez, Becky Smith
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s114-s115
- Print publication:
- October 2020
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Background: Stethoscopes are a known vector for microbial transmission; however, common strategies used to clean stethoscopes pose certain barriers that prevent routine cleaning after every use. We aimed to determine whether using readily available alcohol-based hand rub (ABHR) would effectively reduce bacterial bioburden on stethoscopes in a real-world setting. Methods: We performed a randomized study on inpatient wards of an academic medical center to assess the impact of using ABHR (AlcareExtra; ethyl alcohol, 80%) on the bacterial bioburden of stethoscopes. Stethoscopes were obtained from healthcare providers after routine use during an inpatient examination and were randomized to control (no intervention) or ABHR disinfection (2 pumps applied to tubing and bell or diaphragm by study personnel, then allowed to dry). Cultures of the tubing and bell or diaphragm were obtained with premoistened cellulose sponges. Sponges were combined with 1% Tween20-PBS and mixed in the Seward Stomacher. The homogenate was centrifuged and all but ~5 mL of the supernatant was discarded. Samples were plated on sheep’s blood agar and selective media for clinically important pathogens (CIPs) including S. aureus, Enterococcus spp, and gram-negative bacteria (GNB). CFU count was determined by counting the number of colonies on each plate and using dilution calculations to calculate the CFU of the original ~5 mL homogenate. Results: In total, 80 stethoscopes (40 disinfection, 40 control) were sampled from 46 physicians (MDs) and MD students (57.5%), 13 advanced practice providers (16.3%), and 21 nurses (RNs) and RN students (26.3%). The median CFU count was ~30-fold lower in the disinfection arm compared to control (106 [IQR, 50–381] vs 3,320 [986–4,834]; P < .0001). The effect was consistent across provider type, frequency of recent usual stethoscope cleaning, age, and status of pet ownership (Fig. 1). Overall, 26 of 80 (33%) of stethoscopes harbored CIP. The presence of CIP was lower but not significantly different for stethoscopes that underwent disinfection versus controls: S. aureus (25% vs 32.5%), Enterococcus (2.5% vs 10%), and GNB (2.5% vs 5%). Conclusions: Stethoscopes may serve as vectors for clean hands to become recontaminated immediately prior to performing patient care activities. Using ABHR to clean stethoscopes after every use is a practical and effective strategy to reduce overall bacterial contamination that can be easily incorporated into clinical workflow. Larger studies are needed to determine the efficacy of ABHR at removing CIP from stethoscopes as stethoscopes in both arms were frequently contaminated with CIP. Prior cleaning of stethoscopes on the study day did not seem to impact contamination rates, suggesting the impact of alcohol foam disinfection is short-lived and may need to be repeated frequently (ie, after each use).
Funding: None
Disclosures: None
Disclosures: None
Funding: None
Reflex Urine Culture Practices in a Regional Community Hospital Network
- Dorothy Ling, Jessica Seidelman, Elizabeth Dodds Ashley, Sarah Lewis, Rebekah Moehring, Deverick John Anderson, Sonali Advani
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, p. s370
- Print publication:
- October 2020
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Background: Reflex urine cultures (RUCs) have the potential to reduce unnecessary urine cultures and antibiotic use. However, urinalysis parameters that best predict true infection are unknown. In this study, we surveyed different RUC practices in laboratories across a regional network of community hospitals. Methods: We conducted a voluntary electronic survey of infection preventionists to describe laboratory practices relating to RUCs across 51 community hospitals in the Duke Infection Control Outreach Network (DICON) between May 15, 2019, and July 3, 2019. Results: We received 51 responses (response rate, 100%). Most hospital laboratories were located in North Carolina (n = 25, 49%) and Georgia (n = 18, 35%); 28 laboratories (55%) incorporated RUCs. Surveyed laboratories accepted urine samples from any source and various collection methods (eg, indwelling catheter specimens, clean catch specimens). Moreover, 24 laboratories (86%) offered RUCs for all patients, whereas 4 laboratories (14%) restricted RUCs to specific populations (ie, outpatient, emergency room or children). We observed wide variability in the urinalysis criteria used for RUCs (Table 1); 26 unique approaches were used among 28 laboratories. Also, 24 laboratories (86%) used multiple criteria and 4 (14%) used 1 criterion. Of those that used multiple criteria, all 24 proceeded to RUC if at least 1 UA criterion was met. Furthermore, 22 laboratories (79%) incorporated the presence of nitrites as a urinalysis criterion; 21 laboratories (75%) incorporated white blood cell count (WBC) as a criterion. The most frequent WBC cutoffs were “≥5” (n = 11, 39%) and “≥10” (n = 7, 25%). In addition, 21 laboratories (75%) incorporated leukocyte esterase as a urinalysis criterion, with criteria including “positive” (n = 15, 54%), “trace” (n = 4, 14%), “moderate” (n = 1, 4%), and “large” (n = 1, 4%). Also, 17 (61%) laboratories incorporated magnitude of bacteriuria as a urinalysis criterion. The cutoff ranged from “few” (n = 8, 29%), “moderate” (n = 7, 25%), to “many” (n = 2, 7%). Another 3 (11%) laboratories incorporated other criteria: presence of blood (n = 2, 7%) and presence of fungal elements (n = 1, 4%). Only 3 (11%) laboratories utilized epithelial cells as an exclusion criterion where urinalysis would not proceed to culture if epithelial cells in urinalysis samples exceeded the designated limit, ranging from “>5” to “>15”. Conclusions: More than half of the hospitals in our community hospital network utilize RUCs, but criteria varied widely. Future epidemiological research should aim to identify ideal urinalysis parameters as well as specific patient populations that safely benefit from RUC strategies.
Funding: None
Disclosures: None
Comparison of Metrics Used to Track CLABSIs and CAUTIs Across a Regional Network
- Sonali Advani, Becky Smith, Jessica Seidelman, Nicholas Turner, Christopher Hostler, Deverick John Anderson, Sarah Lewis
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s178-s179
- Print publication:
- October 2020
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Background: The standardized infection ratio (SIR) is the nationally adopted metric used to track and compare catheter-associated urinary tract infections (CAUTIs) and central-line– associated bloodstream infections (CLABSIs). Despite its widespread use, the SIR may not be suitable for all settings and may not capture all catheter harm. Our objective was to look at the correlation between SIR and device use for CAUTIs and CLABSIs across community hospitals in a regional network. Methods: We compared SIR and SUR (standardized utilization ratio) for CAUTIs and CLABSIs across 43 hospitals in the Duke Infection Control Outreach Network (DICON) using a scatter plot and calculated an R2 value. Hospitals were stratified into large (>70,000 patient days), medium (30,000–70,000 patient days), and small hospitals (<30,000 patient days) based on DICON’s benchmarking for community hospitals. Results: We reviewed 24 small, 11 medium, and 8 large hospitals within DICON. Scatter plots for comparison of SIRs and SURs for CLABSIs and CAUTIs across our network hospitals are shown in Figs. 1 and 2. We detected a weak positive overall correlation between SIR and SUR for CLABSIs (0.33; R2 = 0.11), but no correlation between SIR and SUR for CAUTIs (−0.07; R2 = 0.00). Of 15 hospitals with SUR >1, 7 reported SIR <1 for CLABSIs, whereas 10 of 13 hospitals with SUR >1 reported SIR <1 for CAUTIs. Smaller hospitals showed a better correlation for CLABSI SIR and SUR (0.37) compared to medium and large hospitals (0.19 and 0.22, respectively). Conversely, smaller hospitals showed no correlation between CAUTI SIR and SUR, whereas medium and larger hospitals showed a negative correlation (−0.31 and −0.39, respectively). Conclusions: Our data reveal a weak positive correlation between SIR and SUR for CLABSIs, suggesting that central line use impacts CLABSI SIR to some extent. However, we detected no correlation between SIR and SUR for CAUTIs in smaller hospitals and a negative correlation for medium and large hospitals. Some hospitals with low CAUTI SIRs might actually have higher device use, and vice versa. Therefore, the SIR alone does not adequately reflect preventable harm related to urinary catheters. Public reporting of SIR may incentivize hospitals to focus more on urine culture stewardship rather than reducing device utilization.
Funding: None
Disclosures: None
Significant Regional Differences in Antibiotic Use Across 576 US Hospitals and 11,701,326 Million Admissions, 2016–2017
- Katherine Goodman, Sara Cosgrove, Lisa Pineles, Laurence Magder, Deverick John Anderson, Elizabeth Dodds Ashley, Ronald Polk, Hude Quan, William Trick, Keith Woeltje, Surbhi Leekha, Anthony Harris
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s51-s52
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- October 2020
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Background: Reducing inappropriate antibiotic use is critical for fighting antibiotic resistance. Quantifying the amount and diversity of antibiotic use in US hospitals is foundational to these efforts but hampered by limited national surveillance. The current study aims to address this knowledge gap by examining adult inpatient antibiotic usage, including regional, facility, and case-mix differences, across 576 hospitals and nearly 12 million encounters in 2016–2017. Methods: We conducted a retrospective cohort study of patients aged ≥18 years discharged from hospitals in the Premier Healthcare Database, a repository of nearly 1 of every 4 annual US hospitalizations, between January 1, 2016, and December 31, 2017. Detailed hospital- and patient-level data were extracted for each admission. Facilities were classified geographically by census division. Using daily antibiotic charge data, we mapped antibiotics to 18 mutually exclusive classes and to categories based upon spectrum of activity. Patient-level data were transformed into hospital case-mix variables (eg, hospital mean patient age), and relationships between antibiotic days of therapy (DOTs), and these and other facility-level variables were evaluated in negative binomial regression models. Results: The study included 11,701,326 adult admissions, totaling 64,064,632 patient days across 576 US hospitals. Overall, antibiotics were used in 65% of all hospitalizations, at a rate of 870 DOTs per 1,000 patient days. The most commonly used classes per patient days were
β-lactam/β-lactamase inhibitor combinations (206 DOTs), third- and fourth-generation cephalosporins (128 DOTs), and glycopeptides (113 DOTs) (Fig. 1). By spectrum of activity, antipseudomonal agents (245 DOTs) were the most common. Crude usage rates varied by geographic region (Fig. 2). In multivariable analyses, teaching hospitals, and/or larger bed sizes were independently associated with lower use across a range of antibiotic classes (adjusted IRR ranges, 0.90–0.94 and 0.96–0.98, respectively). Significant regional differences also persisted. Compared to the South Atlantic region (chosen as the reference category because it had the largest representation in the cohort), rates of total antibiotic use were 6%, 15%, and 18% lower on average in the Pacific, New England, and the Middle Atlantic regions, respectively. By class, carbapenems reflected the most geographic variability. Conclusions: In a large, diverse cohort of US hospitals, adult inpatients received antibiotics at a rate similar to, but higher than, previously published estimates. In adjusted models, lower antibiotic use was frequently associated with facilities likely to have robust antibiotic stewardship programs—those with teaching status and larger bed size. Further research to understand other reasons for regional differences in antibiotic use such as different rates of resistance is needed.
Funding: This work was supported by Funding: from the Agency for Healthcare Research and Quality (AHRQ) (R01-HS026205 to A.D.H.).
Disclosures: None
Efficacy of UV-C Disinfection in Hyperbaric Chambers
- Bobby Warren, Jason Masker, Gregory Brown, Isabella Gamez, Becky Smith, Deverick John Anderson, Nicholas Turner
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s210-s211
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- October 2020
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Background: UV-C light reduces contamination of high-touch clinical surfaces. Few studies have tested the relative efficacy of UV-C devices in real-world clinical environments. Methods: We assessed the efficacy of the Tru-D (SmartUVC) and Moonbeam-3 UV-C (Diversey) devices at eradicating important clinical pathogens in 2 hyperbaric chambers at a tertiary-care hospital. Formica sheets were inoculated with 106–107 CFU of MRSA (USA300) or 104–105 CFU of C. difficile (NAP1). Sheets were placed in 6 predetermined locations throughout the chambers. Two Moonbeam-3 UV-C devices were positioned in the center of each chamber and were run for 3-minute (per manufacturer’s instructions) and 5-minute cycles. One Tru-D was positioned in the center of the chamber and was run on the vegetative cycle for MRSA and the spore cycle for C. difficile. UV-C dosage was measured for both machines. Quantitative cultures were collected using Rodac plates with DE neutralizing agar and were incubated at 37C for 48 hours. C. difficile was likewise plated onto sheep’s blood agar. Results: We ran each combination of chamber, microbe, and UV-C device in triplicate for In total, 108 samples per species.
For MRSA, the Tru-D vegetative cycle, the 5-minute Moonbeam cycle, and the 3-minute Moonbeam cycle resulted in average CFU log10 reductions of 7.02 (95% CI, 7.02–7.02), 6.99 (95% CI, 6.95–7.02), and 6.58 (95% CI, 6.37–6.79), respectively (Fig. 1). The Tru-D vegetative and 5-minute Moonbeam cycles were similarly effective (P > .99), and both were more effective than the 3-minute Moonbeam cycle (P < .001 and P < .001, respectively). MRSA samples receiving direct UV-C exposure had significantly greater log10 reductions (6.95; 95% CI, 6.89–7.01) than did indirect exposure (6.67; 95% CI, 6.46–6.87; P < .05) (Fig. 2). For C. difficile, the Tru-D sporicidal, the 5-, and 3-minute Moonbeam cycles resulted in average CFU log10 reductions of 1.78 (95% CI, 1.43–2.12), 0.57 (95% CI, 0.33–0.81) and 0.64 (95% CI, 0.42–0.86), respectively (Fig. 1). Tru-D was significantly more effective than either the 3- or 5-minute Moonbeam cycles (P < .00). C. difficile samples receiving direct UV-C exposure had higher dosage and significantly greater log10 reductions (1.34; 95% CI, 1.10–1.58) than did indirect exposure (0.58; 95% CI, 0.31–0.86; P < .01) (Fig. 2). Conclusions: Use of the Tru-D vegetative cycle and the Moonbeam 3- and 5-minute cycles resulted in similar reductions in MRSA; both resulted in significantly greater reductions than the manufacturer’s recommended 3-minute Moonbeam cycle. Therefore, healthcare facilities should carefully evaluate manufacturer-recommended run times in their specific clinical setting. For C. difficile, the Tru-D sporicidal cycle was significantly more effective than either of the Moonbeam cycles, likely due to higher irradiation levels. As such, direct UV-C exposure resulted in greater average reductions than indirect exposure.
Funding: None
Disclosures: None
Epidemiology of Posttransrectal Prostate Biopsy Bloodstream Infections and Impact of a Screening Program
- Dorothy Ling, Sarah Lewis, Christina Sarubbi, Rebekah Moehring, Sonali Advani, Deverick John Anderson, Becky Smith
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 41 / Issue S1 / October 2020
- Published online by Cambridge University Press:
- 02 November 2020, pp. s218-s219
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- October 2020
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Background: Antibiotic prophylaxis choice for transrectal prostate biopsy (TRPB) has been affected by the emergence of fluoroquinolone-resistant Escherichia coli (FQRE). Prebiopsy FQRE screening and targeted antibiotic prophylaxis may reduce post-TRPB bloodstream infection (BSI). We assessed the impact of a FQRE screening program on post-TRPB BSIs at an academic medical center. Methods: We implemented a FQRE screening program and targeted TRPB antimicrobial prophylaxis guidelines on May 1, 2017 (Fig. 1). We performed a retrospective cohort study of all TRPB and compared the incidence of post-TRPB BSI (within 7 calendar days) per 100 procedures before the intervention (January, 1, 2016, to April 30, 2017) and to the incidence after the intervention (May 1, 2017, to August 31, 2019). We used a subanalysis to compare BSI incidence between patients with positive (+) and negative () FQRE screens and appropriate prophylaxis use, defined as administration of guideline-recommended antibiotics. The Fisher exact test of independence was used to analyze nominal data. Results: The analysis included 2,157 TRPB procedures: 647 in the preintervention period and 1,510 in the postintervention period. FQRE screening compliance was 61% (n = 914) in the postintervention group (Fig. 2); 168 FQRE screens (18%) were positive. The median time from FQRE screen to procedure was 40 days (IQR, 13–69). Postprocedure BSI rates were higher in than those in the preimplementation group; however, this difference was not statistically significant (0.86 vs 0.46; OR, 2.01; P = .42). Among FQRE-screened patients, BSI rates differed significantly between FQRE+ and FQRE patients (2.98 vs 0.54; OR, 5.67; 95% CI, 1.21–28.94; P = .01). Screened patients receiving appropriate prophylaxis had lower BSI rates than those receiving inappropriate prophylaxis; however, this was not statistically significant (1.10 vs 2.02; OR, 0.54; P = .35). The most common BSI pathogen was E. coli (2 (67%) before implementation and 10 (77%) after implementation). Also, 5 E. coli BSIs (50%) were fluoroquinolone resistant in the postimplementation group compared to 1 (33%) in the preimplementation group. Of 13 postimplementation BSIs, 6 occurred in patients who received aminoglycosides perioperatively; however, all 6 BSI pathogens were aminoglycoside sensitive. Conclusions: Compliance with our FQRE screening program and antimicrobial prophylaxis protocol was moderate. Although pre- and postimplementation differences in BSI rates were not statistically significant, the high failure rate among patients receiving aminoglycosides was concerning and led to a change in TRPB prophylaxis guidelines. Reasons for increased BSI risk among FQRE+ patients may include prophylaxis agent, dose, timing, or other confounding factors associated with drug-resistant pathogens. Facilities implementing FQRE screening protocols should evaluate the efficacy of their program and periodically review screening compliance, prophylaxis dosing and timing adherence, and impact on patient-level outcomes.
Funding: None
Disclosures: None
A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals: 2014 Updates
- Part of
- Deborah S. Yokoe, Deverick J. Anderson, Sean M. Berenholtz, David P. Calfee, Erik R. Dubberke, Katherine D. Eilingson, Dale N. Gerding, Janet P. Haas, Keith S. Kaye, Michael Klompas, Evelyn Lo, Jonas Marschall, Leonard A. Mermel, Lindsay E. Nicolle, Cassandra D. Salgado, Kristina Bryant, David Classen, Katrina Crist, Valerie M. Deloney, Neil O. Fishman, Nancy Foster, Donald A. Goldmann, Eve Humphreys, John A. Jernigan, Jennifer Padberg, Trish M. Perl, Kelly Podgorny, Edward J. Septimus, Margaret VanAmringe, Tom Weaver, Robert A. Weinstein, Robert Wise, Lisa L. Maragakis
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 35 / Issue S2 / September 2014
- Published online by Cambridge University Press:
- 10 May 2016, pp. S21-S31
- Print publication:
- September 2014
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Since the publication of “A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals” in 2008, prevention of healthcare-associated infections (HAIs) has become a national priority. Despite improvements, preventable HAIs continue to occur. The 2014 updates to the Compendium were created to provide acute care hospitals with up-to-date, practical, expert guidance to assist in prioritizing and implementing their HAI prevention efforts. They are the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise, including the Centers for Disease Control and Prevention(CDC), the Institute for Healthcare Improvement (IHI), the Pediatric Infectious Diseases Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), and the Surgical Infection Society (SIS).
A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals: 2014 Updates
- Deborah S. Yokoe, Deverick J. Anderson, Sean M. Berenholtz, David P. Calfee, Erik R. Dubberke, Katherine D. Ellingson, Dale N. Gerding, Janet P. Haas, Keith S. Kaye, Michael Klompas, Evelyn Lo, Jonas Marschall, Leonard A. Mermel, Lindsay E. Nicolle, Cassandra D. Salgado, Kristina Bryant, David Classen, Katrina Crist, Valerie M. Deloney, Neil O. Fishman, Nancy Foster, Donald A. Goldmann, Eve Humphreys, John A. Jernigan, Jennifer Padberg, Trish M. Perl, Kelly Podgorny, Edward J. Septimus, Margaret VanAmringe, Tom Weaver, Robert A. Weinstein, Robert Wise, Lisa L. Maragakis
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 35 / Issue 8 / August 2014
- Published online by Cambridge University Press:
- 10 May 2016, pp. 967-977
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- August 2014
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Since the publication of “A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals” in 2008, prevention of healthcare-associated infections (HAIs) has become a national priority. Despite improvements, preventable HAIs continue to occur. The 2014 updates to the Compendium were created to provide acute care hospitals with up-to-date, practical, expert guidance to assist in prioritizing and implementing their HAI prevention efforts. They are the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise, including the Centers for Disease Control and Prevention (CDC), the Institute for Healthcare Improvement (IHI), the Pediatric Infectious Diseases Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), and the Surgical Infection Society (SIS).
Infect Control Hosp Epidemiol 2014;35(8):967–977
Central Line-Associated Infections as Defined by the Centers for Medicare and Medicaid Services' Hospital-Acquired Condition versus Standard Infection Control Surveillance Why Hospital Compare Seems Conflicted
- Rebekah W. Moehring, Russell Staheli, Becky A. Miller, Luke Francis Chen, Daniel John Sexton, Deverick John Anderson
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- Journal:
- Infection Control & Hospital Epidemiology / Volume 34 / Issue 3 / March 2013
- Published online by Cambridge University Press:
- 02 January 2015, pp. 238-244
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- March 2013
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Objective.
To evaluate the concordance of case-finding methods for central line-associated infection as defined by Centers for Medicare and Medicaid Services (CMS) hospital-acquired condition (HAC) compared with traditional infection control (IC) methods.
Setting.One tertiary care and 2 community hospitals in North Carolina.
Patients.Adult and pediatric hospitalized patients determined to have central line infection by either case-finding method.
Methods.We performed a retrospective comparative analysis of infection detected using HAC versus standard IC central line–associated bloodstream infection surveillance from October 1, 2007, through December 31, 2009. One billing and 2 IC databases were queried and matched to determine the number and concordance of cases identified by each method. Manual review of 25 cases from each discordant category was performed. Sensitivity and positive predictive value (PPV) were calculated using IC as criterion standard.
Results.A total of 1,505 cases were identified: 844 by International Classification of Diseases, Ninth Revision (ICD-9), and 798 by IC. A total of 204 cases (24%) identified by ICD-9 were deemed not present at hospital admission by coders. Only 112 cases (13%) were concordant. HAC sensitivity was 14% and PPV was 55% compared with IC. Concordance was low regardless of hospital type. Primary reasons for discordance included differences in surveillance and clinical definitions, clinical uncertainty, and poor documentation.
Conclusions.The case-finding method used by CMS HAC and the methods used for traditional IC surveillance frequently do not agree. This can lead to conflicting results when these 2 measures are used as hospital quality metrics.