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Heil EL, Justo JA, Bork JT. Improving the Efficiency of Antimicrobial Stewardship Action in Acute Care Facilities. Open Forum Infect Dis 2023; 10:ofad412. [PMID: 37674632 PMCID: PMC10478156 DOI: 10.1093/ofid/ofad412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/28/2023] [Indexed: 09/08/2023] Open
Abstract
Inpatient antimicrobial stewardship (AS) programs are quality improvement programs tasked with improving antibiotic practices by augmenting frontline providers' antibiotic prescription. Prospective audit and feedback (PAF) and preauthorization (PRA) are essential activities in the hospital that can be resource intensive for AS teams. Improving efficiency in AS activities is needed when there are limited resources or when programs are looking to expand tasks beyond PAF and PRA, such as broad education or guideline development. Guidance on the creation and maintenance of alerts for the purpose of PAF reviews, modifications of antibiotic restrictions for PRA polices, and overall initiative prioritization strategies are reviewed. In addition, daily prioritization tools, such as the tiered approach, scoring systems, and regression modeling, are available for stewards to prioritize their daily workflow. Using these tools and guidance, AS programs can be productive and impactful in the face of resource limitation or competing priorities in the hospital.
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Affiliation(s)
- Emily L Heil
- Department of Practice, Sciences and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore, Maryland, USA
| | - Julie Ann Justo
- Department of Clinical Pharmacy and Outcomes Sciences, College of Pharmacy, University of South Carolina, Columbia, South Carolina, USA
- Department of Pharmacy, Prisma Health Richland Hospital, Columbia, South Carolina, USA
| | - Jacqueline T Bork
- Division of Infectious Diseases, Institute of Human Virology, Department of Medicine, School of Medicine, University of Maryland, Baltimore, Maryland, USA
- Veterans Affairs (VA) Maryland Health Care System, Baltimore, Maryland, USA
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Whitney L, Armstrong‐James D, Lyster HS, Reed AK, Dunning J, Nwankwo L, Cheong J. Antifungal stewardship in solid‐organ transplantation: What is needed? Transpl Infect Dis 2022; 24:e13894. [DOI: 10.1111/tid.13894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/15/2022] [Indexed: 11/06/2022]
Affiliation(s)
| | | | - Haifa S. Lyster
- Department of Heart and Lung Transplantation The Royal Brompton and Harefield NHS Foundation Trust, Harefield Hospital Harefield Middlesex UK
| | - Anna K. Reed
- Department of Lung Transplantation Royal Brompton and Harefield National Health Service (NHS) Foundation Trust London UK
| | - John Dunning
- Department of Lung Transplantation Royal Brompton and Harefield National Health Service (NHS) Foundation Trust London UK
| | - Lisa Nwankwo
- Department of Pharmacy Royal Brompton & Harefield NHS Foundation Trust London UK
| | - Jamie Cheong
- Department of Pharmacy Royal Brompton & Harefield NHS Foundation Trust London UK
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So M, Nakamachi Y, Thursky K. Auditing tools for antimicrobial prescribing in solid organ transplant recipients: The why, the how, and an assessment of current options. Transpl Infect Dis 2022; 24:e13905. [DOI: 10.1111/tid.13905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/17/2022] [Accepted: 06/29/2022] [Indexed: 01/15/2023]
Affiliation(s)
- Miranda So
- Sinai Health‐University Health Network Antimicrobial Stewardship Program, University Health Network Toronto Ontario Canada
- Leslie Dan Faculty of Pharmacy University of Toronto Toronto Ontario Canada
| | - Yoshiko Nakamachi
- Sinai Health‐University Health Network Antimicrobial Stewardship Program, University Health Network Toronto Ontario Canada
| | - Karin Thursky
- National Centre for Antimicrobial Stewardship, Peter Doherty Institute for Infection and Immunity University of Melbourne Melbourne Victoria Australia
- Royal Melbourne Hospital Melbourne Victoria Australia
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Goodman KE, Heil EL, Claeys KC, Banoub M, Bork JT. Real-World Antimicrobial Stewardship Experience in a Large Academic Medical Center: Using Statistical and Machine Learning Approaches to Identify Intervention “Hotspots” in an Antibiotic Audit and Feedback Program. Open Forum Infect Dis 2022; 9:ofac289. [PMID: 35873287 PMCID: PMC9297307 DOI: 10.1093/ofid/ofac289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/07/2022] [Indexed: 11/14/2022] Open
Abstract
ABSTRACT
Background
Prospective audit with feedback (PAF) is an impactful strategy for antimicrobial stewardship program (ASP) activities. However, because PAF requires reviewing large numbers of antimicrobial orders on a case-by-case basis, PAF programs are highly resource-intensive. The current study aimed to identify predictors of ASP intervention (i.e., feedback), and to build models to identify orders that can be safely bypassed from review, to make PAF programs more efficient.
Methods
We performed a retrospective cross-sectional study of inpatient antimicrobial orders reviewed by the University of Maryland Medical Center’s PAF program between 2017–2019. We evaluated the relationship between antimicrobial and patient characteristics with ASP intervention using multivariable logistic regression models. Separately, we built prediction models for ASP intervention using statistical and machine learning approaches and evaluated performance on held-out data.
Results
Across 17,503 PAF reviews, 4,219 (24%) resulted in intervention. In adjusted analyses, a clinical pharmacist on the ordering unit or receipt of an ID consult were associated with 17% and 56% lower intervention odds, respectively (aORs 0.83 and 0.44, P values ≤ 0.001). Fluoroquinolones had the highest adjusted intervention odds (aOR 3.22, 95% CI: 2.63–3.96). A machine learning classifier (C-statistic 0.76) reduced reviews by 49% while achieving 78% sensitivity. A “workflow simplified” regression model that restricted to antimicrobial class and clinical indication variables, two strong machine-learning-identified predictors, reduced reviews by one-third while achieving 81% sensitivity.
Conclusions
Prediction models substantially reduced PAF review caseloads while maintaining high sensitivities. Our results and approach may offer a blueprint for other ASPs.
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Affiliation(s)
- Katherine E Goodman
- Correspondence: Katherine E. Goodman, JD, PhD, University of Maryland School of Medicine, 10 S Pine St, Baltimore, MD 21201, USA ()
| | - Emily L Heil
- Department of Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | - Kimberly C Claeys
- Department of Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | - Mary Banoub
- Department of Pharmacy, University of Maryland Medical Center, Baltimore, Maryland, USA
| | - Jacqueline T Bork
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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Jones G, Amoah J, Klein EY, Leeman H, Smith A, Levin S, Milstone AM, Dzintars K, Cosgrove SE, Fabre V. Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia. Open Forum Infect Dis 2021; 8:ofab291. [PMID: 34189181 PMCID: PMC8231365 DOI: 10.1093/ofid/ofab291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/01/2021] [Indexed: 11/12/2022] Open
Abstract
Background Community-acquired pneumonia (CAP) is a major driver of hospital antibiotic use. Efficient methods to identify patients treated for CAP in real time using the electronic health record (EHR) are needed. Automated identification of these patients could facilitate systematic tracking, intervention, and feedback on CAP-specific metrics such as appropriate antibiotic choice and duration. Methods Using retrospective data, we identified suspected CAP cases by searching for patients who received CAP antibiotics AND had an admitting International Classification of Diseases, Tenth Revision (ICD-10) code for pneumonia OR chest imaging within 24 hours OR bacterial urinary antigen testing within 48 hours of admission (denominator query). We subsequently explored different structured and natural language processing (NLP)–derived data from the EHR to identify CAP cases. We evaluated combinations of these electronic variables through receiver operating characteristic (ROC) curves to assess which best identified CAP cases compared to cases identified by manual chart review. Exclusion criteria were age <18 years, absolute neutrophil count <500 cells/mm3, and admission to an oncology unit. Results Compared to the gold standard of chart review, the area under the ROC curve to detect CAP was 0.63 (95% confidence interval [CI], .55–.72; P < .01) using structured data (ie, laboratory and vital signs) and 0.83 (95% CI, .77–.90; P < .01) when NLP-derived data from radiographic reports were included. The sensitivity and specificity of the latter model were 80% and 81%, respectively. Conclusions Creating an electronic tool that effectively identifies CAP cases in real time is possible, but its accuracy is dependent on NLP-derived radiographic data.
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Affiliation(s)
- George Jones
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Joe Amoah
- Department of Pediatrics, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Eili Y Klein
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hannah Leeman
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aria Smith
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Scott Levin
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aaron M Milstone
- Department of Pediatrics, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kathryn Dzintars
- Department of Pharmacy, The Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Sara E Cosgrove
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Valeria Fabre
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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