Comparison of Antibiotic Susceptibility Patterns of Bacterial Isolates Based on Time From Hospitalization and Culture Source: Implications for Hospital Antibiograms

Dickstein, Yaakov; Geffen, Yuval; Leibovici, Leonard

doi:10.1017/ice.2015.258

Cited by 3 publications

(3 citation statements)

References 9 publications

(10 reference statements)

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“…First, the use of the antibiogram does not capture changes in resistance patterns over time in a hospital unit, as it usually includes only the first isolate from each patient. 36 Second, reliance on clinical cultures to assess changes in resistance patterns is challenging due to the infrequency of positive clinical cultures; therefore, few studies include development of antimicrobial resistance based on surveillance cultures as a primary outcome. 37,38 The use of transmission dynamic modeling allows for the estimation of the impact of antimicrobial stewardship via experimentation with relevant parameters.…”

Section: Discussionmentioning

confidence: 99%

“…There are challenges in using traditional study methods and outcomes to examine the impact of antimicrobial stewardship. First, the use of the antibiogram does not capture changes in resistance patterns over time in a hospital unit, as it usually includes only the first isolate from each patient 36 . Second, reliance on clinical cultures to assess changes in resistance patterns is challenging due to the infrequency of positive clinical cultures; therefore, few studies include development of antimicrobial resistance based on surveillance cultures as a primary outcome 37 …”

Section: Discussionmentioning

confidence: 99%

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The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

Barnes

Rock

Harris

et al. 2017

Infect. Control Hosp. Epidemiol.

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OBJECTIVE Antibiotic resistance is a major threat to public health. Resistance is largely driven by antibiotic usage, which in many cases is unnecessary and can be improved. The impact of decreasing overall antibiotic usage on resistance is unknown and difficult to assess using standard study designs. The objective of this study was to explore the potential impact of reducing antibiotic usage on the transmission of multidrug-resistant organisms (MDROs). DESIGN We used agent-based modeling to simulate interactions between patients and healthcare workers (HCWs) using model inputs informed by the literature. We modeled the effect of antibiotic usage as (1) a microbiome effect, for which antibiotic usage decreases competing bacteria and increases the MDRO transmission probability between patients and HCWs and (2) a mutation effect that designates a proportion of patients who receive antibiotics to subsequently develop a MDRO via genetic mutation. SETTING Intensive care unit INTERVENTIONS Absolute reduction in overall antibiotic usage by experimental values of 10% and 25% RESULTS Reducing antibiotic usage absolutely by 10% (from 75% to 65%) and 25% (from 75% to 50%) reduced acquisition rates of high-prevalence MDROs by 11.2% (P<.001) and 28.3% (P<.001), respectively. We observed similar effect sizes for low-prevalence MDROs. CONCLUSIONS In a critical care setting, where up to 50% of antibiotic courses may be inappropriate, even a moderate reduction in antibiotic usage can reduce MDRO transmission. Infect Control Hosp Epidemiol 2017;38:663-669.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

Barnes

Rock

Harris

et al. 2017

Infect. Control Hosp. Epidemiol.

View full text Add to dashboard Cite

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“…Antibiograms have traditionally been high-level printed tabular summaries of local resistance patterns within an institution. These tables do not account for many factors known to influence antimicrobial susceptibility [ 11 , 12 ], even though such data are available in the electronic health record (EHR).…”

Section: Introductionmentioning

confidence: 99%

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis

et al. 2021

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Background There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. Objective The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. Methods We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. Results We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed remaining risk algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. Conclusions The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

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A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis (Preprint)

Müller¹,

Srinivasan²,

Abeles³

et al. 2020

Preprint

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BACKGROUND There is a pressing need for digital tools that can leverage big data to help clinicians select effective antibiotic treatments in the absence of timely susceptibility data. Clinical presentation and local epidemiology can inform therapy selection to balance the risk of antimicrobial resistance and patient risk. However, data and clinical expertise must be appropriately integrated into clinical workflows. OBJECTIVE The aim of this study is to leverage available data in electronic health records, to develop a data-driven, user-centered, clinical decision support system to navigate patient safety and population health. METHODS We analyzed 5 years of susceptibility testing (1,078,510 isolates) and patient data (30,761 patients) across a large academic medical center. After curating the data according to the Clinical and Laboratory Standards Institute guidelines, we analyzed and visualized the impact of risk factors on clinical outcomes. On the basis of this data-driven understanding, we developed a probabilistic algorithm that maps these data to individual cases and implemented iBiogram, a prototype digital empiric antimicrobial clinical decision support system, which we evaluated against actual prescribing outcomes. RESULTS We determined patient-specific factors across syndromes and contexts and identified relevant local patterns of antimicrobial resistance by clinical syndrome. Mortality and length of stay differed significantly depending on these factors and could be used to generate heuristic targets for an acceptable risk of underprescription. Combined with the developed <i>remaining risk</i> algorithm, these factors can be used to inform clinicians’ reasoning. A retrospective comparison of the iBiogram-suggested therapies versus the actual prescription by physicians showed similar performance for low-risk diseases such as urinary tract infections, whereas iBiogram recognized risk and recommended more appropriate coverage in high mortality conditions such as sepsis. CONCLUSIONS The application of such data-driven, patient-centered tools may guide empirical prescription for clinicians to balance morbidity and mortality with antimicrobial stewardship.

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Comparison of Antibiotic Susceptibility Patterns of Bacterial Isolates Based on Time From Hospitalization and Culture Source: Implications for Hospital Antibiograms

Cited by 3 publications

References 9 publications

The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis

A Risk-Based Clinical Decision Support System for Patient-Specific Antimicrobial Therapy (iBiogram): Design and Retrospective Analysis (Preprint)

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