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Raabe NJ, Valek AL, Griffith MP, Mills E, Waggle K, Srinivasa VR, Ayres AM, Bradford C, Creager HM, Pless LL, Sundermann AJ, Van Tyne D, Snyder GM, Harrison LH. Real-time genomic epidemiologic investigation of a multispecies plasmid-associated hospital outbreak of NDM-5-producing Enterobacterales infections. Int J Infect Dis 2024; 142:106971. [PMID: 38373647 PMCID: PMC11055495 DOI: 10.1016/j.ijid.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/05/2024] [Accepted: 02/14/2024] [Indexed: 02/21/2024] Open
Abstract
OBJECTIVES New Delhi metallo-β-lactamase (NDM) is an emergent mechanism of carbapenem resistance associated with high mortality and limited treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks. METHODS Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance performed weekly. To resolve NDM-encoding plasmids, we performed long-read sequencing and constructed hybrid assemblies. WGS data for suspected outbreaks was shared with the IP&C team for assessment and intervention. RESULTS We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included 7 bacterial species encoding the same NDM-5 plasmid. WGS surveillance and epidemiologic investigation characterized 10 horizontal plasmid transfer events and 6 bacterial transmission events between patients in varying hospital units. CONCLUSIONS Our investigation revealed a complex, multispecies outbreak of NDM involving multiple plasmid transfer and bacterial transmission events. We highlight the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.
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Affiliation(s)
- Nathan J Raabe
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Abby L Valek
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Emma Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashley M Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Claire Bradford
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Hannah M Creager
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lora L Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
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2
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Doyle H, Valek AL, Murillo T, Ayres AM, Slaughter J, Berg ML, Snyder GM. A novel approach to correcting attribution of Clostridioides difficile in a healthcare setting. Antimicrob Steward Healthc Epidemiol 2023; 3:e246. [PMID: 38156213 PMCID: PMC10753511 DOI: 10.1017/ash.2023.516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 12/30/2023]
Abstract
Objective To describe a novel attribution metric estimating the causal source location of healthcare-associated Clostridioides difficile and compare it with the current US National Healthcare Safety Network (NHSN) surveillance reporting standard. Design Quality improvement study. Setting Two acute care facilities. Methods A novel attribution metric assigned days of attribution to locations where patients were located for 14 days before and the day of their C. difficile diagnosis. We correlated the NHSN-assigned unit attribution with the novel attribution measure and compared the proportion of attribution assigned to inpatient units. Results During a 30-month period, there were 727 NHSN C. difficile healthcare-associated infections (HAIs) and 409 non-HAIs; the novel metric attributed 17,034 days. The correlation coefficients for NHSN and novel attributions among non-ICU units were 0.79 (95% CI, 0.76-0.82) and 0.74 (95% CI, 0.70-0.78) and among ICU units were 0.70 (95% CI, 0.63-0.76) and 0.69 (95% CI, 0.60-0.77) at facilities A and B, respectively. The distribution of difference in percent attribution showed higher inpatient unit attribution using NHSN measure than the novel attribution metric: 38% of ICU units and 15% of non-ICU units in facility A, and 20% of ICU units and 25% of non-ICU units in facility B had a median difference >0; no inpatient units showed a greater attribution using the novel attribution metric. Conclusion The novel attribution metric shifts attribution from inpatient units to other settings and correlates modestly with NHSN methodology of attribution. If validated, the attribution metric may more accurately target C. difficile reduction efforts.
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Affiliation(s)
- Hunter Doyle
- Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, PA, USA
| | - Abby L. Valek
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Theresa Murillo
- Department of Infection Prevention and Control, UPMC Senior Communities, Pittsburgh, PA, USA
| | - Ashley M. Ayres
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Julie Slaughter
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Madeline L. Berg
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Graham M. Snyder
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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3
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Raabe NJ, Valek AL, Griffith MP, Mills E, Waggle K, Srinivasa VR, Ayres AM, Bradford C, Creager H, Pless LL, Sundermann AJ, Van Tyne D, Snyder GM, Harrison LH. Genomic Epidemiologic Investigation of a Multispecies Hospital Outbreak of NDM-5-Producing Enterobacterales Infections. medRxiv 2023:2023.08.31.23294545. [PMID: 37693518 PMCID: PMC10491379 DOI: 10.1101/2023.08.31.23294545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background New Delhi metallo-β-lactamase (NDM) represents an emergent mechanism of carbapenem resistance associated with high mortality and limited antimicrobial treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods like speciation, antimicrobial resistance testing, and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks. Methods Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance, which was performed weekly using the Illumina platform. To resolve NDM-encoding plasmids, we performed long-read Oxford Nanopore sequencing and constructed hybrid assemblies using Illumina and Nanopore sequencing data. Reports of relatedness between NDM-producing organisms and reactive WGS for suspected outbreaks were shared with the IP&C team for assessment and intervention. Findings We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included seven bacterial species and each encoded the same NDM-5 plasmid, which showed high homology to NDM plasmids previously observed in Asia. WGS surveillance and epidemiologic investigation characterized ten horizontal plasmid transfer events and six bacterial transmission events between patients housed in varying hospital units. Transmission prevention focused on enhanced observation and adherence to basic infection prevention measures. Interpretation Our investigation revealed a complex, multispecies outbreak of NDM that involved multiple plasmid transfer and bacterial transmission events, increasing the complexity of outbreak identification and transmission prevention. Our investigation highlights the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks. Funding This work was funded in part by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH) (R01AI127472) (R21AI1783691).
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Affiliation(s)
- Nathan J. Raabe
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
| | - Abby L. Valek
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Marissa P. Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Emma Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Ashley M. Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Claire Bradford
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Hannah Creager
- Department of Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street Pittsburgh, PA 15213
- Department of Pathology, University of Pittsburgh School of Medicine, 200 Lothrop St, S-417 BST, Pittsburgh, PA 15261
| | - Lora L. Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Alexander J. Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
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Snyder GM, Wagester S, Harris PL, Valek AL, Hodges JC, Bilderback AL, Kader F, Tanner CA, Metzger AP, DiNucci SE, Colaianne BV, Chung A, Zapf RL, Kip PL, Minnier TE. Development and implementation of a centralized surveillance infection prevention program in a multi-facility health system: A quality improvement project. Antimicrob Steward Healthc Epidemiol 2023; 3:e56. [PMID: 36970425 PMCID: PMC10031579 DOI: 10.1017/ash.2023.126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 03/24/2023]
Abstract
Objective: To develop, implement, and evaluate the effectiveness of a unique centralized surveillance infection prevention (CSIP) program. Design: Observational quality improvement project. Setting: An integrated academic healthcare system. Intervention: The CSIP program comprises senior infection preventionists who are responsible for healthcare-associated infection (HAI) surveillance and reporting, allowing local infection preventionists (LIPs) a greater portion of their time to non-surveillance patient safety activities. Four CSIP team members accrued HAI responsibilities at 8 facilities. Methods: We evaluated the effectiveness of the CSIP program using 4 measures: recovery of LIP time, efficiency of surveillance activities by LIPs and CSIP staff, surveys characterizing LIP perception of their effectiveness in HAI reduction, and nursing leaders’ perception of LIP effectiveness. Results: The amount of time spent by LIP teams on HAI surveillance was highly variable, while CSIP time commitment and efficiency was steady. Post-CSIP implementation, 76.9% of LIPs agreed that they spend adequate time on inpatient units, compared to 15.4% pre-CSIP; LIPs also reported more time to allot to non-surveillance activities. Nursing leaders reported greater satisfaction with LIP involvement with HAI reduction practices. Conclusion: CSIP programs are a little-reported strategy to ease burden on LIPs with reallocation of HAI surveillance. The analyses presented here will aid health systems in anticipating the benefit of CSIP programs.
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Affiliation(s)
- Graham M. Snyder
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Author for correspondence: Graham M. Snyder, MD, MS, Falk Medical Building, 3601 Fifth Avenue, Suite 150, Pittsburgh, PA15213. E-mail:
| | | | | | - Abby L. Valek
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, Pennsylvania
| | | | | | | | - Colleen A. Tanner
- Quality and Risk Management, UPMC Passavant, McCandless, Pennsylvania
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5
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Snyder GM, Wagester S, Harris PL, Valek AL, Hodges JC, Bilderback AL, Kader F, Tanner CA, Metzger AP, DiNucci SE, Colaianne BV, Chung A, Zapf RL, Kip PL, Minnier TE. Healthcare-associated infections during the coronavirus disease 2019 (COVID-19) pandemic and the modulating effect of centralized surveillance. Antimicrob Steward Healthc Epidemiol 2023; 3:e72. [PMID: 37113196 PMCID: PMC10127231 DOI: 10.1017/ash.2023.139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 04/29/2023]
Abstract
We analyzed efficacy of a centralized surveillance infection prevention (CSIP) program in a healthcare system on healthcare-associated infection (HAI) rates amid the coronavirus disease 2019 (COVID-19) pandemic. HAI rates were variable in CSIP and non-CSIP facilities. Central-line-associated bloodstream infection (CLABSI), C. difficile infection (CSI), and surgical-site infection (SSI) rates were negatively correlated with COVID-19 intensity in CSIP facilities.
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Affiliation(s)
- Graham M. Snyder
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Author for correspondence: Graham M. Snyder, MD, MS, Falk Medical Building, 3601 Fifth Avenue, Suite 150, Pittsburgh, PA15213. E-mail:
| | | | | | - Abby L. Valek
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, Pennsylvania
| | | | | | | | - Colleen A. Tanner
- Quality and Risk Management, UPMC Passavant, McCandless, Pennsylvania
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Berg ML, Ayres AM, Weber DR, McCullough M, Crall VD, Lewis CL, Valek AL, Vincent LA, Penzelik J, Sasinoski CA, Cheng AL, Bradford CF, Bell EO, Edwards KM, Castronova IA, Brady MB, Slaughter J, Oleksiuk LM, Snyder GM. Diagnostic stewardship for Clostridioides difficile testing in an acute care hospital: A quality improvement intervention. ASHE 2023; 3:e67. [PMID: 37113206 PMCID: PMC10127245 DOI: 10.1017/ash.2023.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 04/09/2023]
Abstract
Abstract
Objective:
To evaluate the impact of a diagnostic stewardship intervention on Clostridioides difficile healthcare-associated infections (HAI).
Design:
Quality improvement study.
Setting:
Two urban acute care hospitals.
Interventions:
All inpatient stool testing for C. difficile required review and approval prior to specimen processing in the laboratory. An infection preventionist reviewed all orders daily through chart review and conversations with nursing; orders meeting clinical criteria for testing were approved, orders not meeting clinical criteria were discussed with the ordering provider. The proportion of completed tests meeting clinical criteria for testing and the primary outcome of C. difficile HAI were compared before and after the intervention.
Results:
The frequency of completed C. difficile orders not meeting criteria was lower [146 (7.5%) of 1,958] in the intervention period (January 10, 2022–October 14, 2022) than in the sampled 3-month preintervention period [26 (21.0%) of 124; P < .001]. C. difficile HAI rates were 8.80 per 10,000 patient days prior to the intervention (March 1, 2021–January 9, 2022) and 7.69 per 10,000 patient days during the intervention period (incidence rate ratio, 0.87; 95% confidence interval, 0.73–1.05; P = .13).
Conclusions:
A stringent order-approval process reduced clinically nonindicated testing for C. difficile but did not significantly decrease HAIs.
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