Electronic Prediction Rules for Methicillin-Resistant<i>Staphylococcus aureus</i>Colonization

Robicsek, Ari; Beaumont, Jennifer L.; Wright, Marc‐Oliver; Thomson, Richard B.; Kaul, Karen L.; Peterson, Lance R.

doi:10.1086/657631

Cited by 37 publications

(32 citation statements)

References 43 publications

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“…Figure 2 can be used to determine the cost of a potential AST program. It is important to understand that if universal surveillance is the initial AST intervention, one will quickly learn the risk factors for colonization in one's own patient population, and then a prediction rule can be developed so that even though all patients are "screened (by a computer algorithm)" at admission, only a portion who are at higher risk actually need to be tested for MRSA colonization (23). This is noted in line B of Fig.…”

Section: Resultsmentioning

confidence: 99%

“…2 where even though the table is constructed for 1,000 patients, only 500 are actually tested using a developed prediction rule. We implemented a prediction rule that was developed from our own universal surveillance program in January 2012 and now only test 50% of admissions, even though all are screened for MRSA risk using the prediction model (23). As can be seen from Fig.…”

Section: Resultsmentioning

confidence: 99%

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Methicillin-Resistant Staphylococcus aureus Control in the 21st Century: Laboratory Involvement Affecting Disease Impact and Economic Benefit from Large Population Studies

Peterson

Schora

2016

J Clin Microbiol

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Methicillin-resistant Staphylococcus aureus (MRSA) infection is a global health care problem. Large studies (e.g., >25,000 patients) show that active surveillance testing (AST) followed by contact precautions for positive patients is an effective approach for MRSA disease control. With this approach, the clinical laboratory will be asked to select what AST method(s) to use and to provide data monitoring outcomes of the infection prevention interventions. This minireview summarizes evidence for MRSA disease control, reviews the involvement of the laboratory, and provides examples of how to undertake a program cost analysis. Health care organizations with total MRSA clinical infections of >0.3/1,000 patient days or bloodstream infections of >0.03/ 1,000 patient days should implement a MRSA control plan.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Methicillin-Resistant Staphylococcus aureus Control in the 21st Century: Laboratory Involvement Affecting Disease Impact and Economic Benefit from Large Population Studies

Peterson

Schora

2016

J Clin Microbiol

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“…Potentially, the predictive precision of clonotyping for resistance and clinical outcome could be improved by incorporating host factors into the algorithm, including demographic characteristics and comorbidity, as well as previous antibiotic treatment. Recent studies, for example, have demonstrated that electronic health records (EHRs) can be used to identify high-risk patients for targeted relapse prevention strategies directed toward other pathogens (9,23). Extensive genome-wide analysis of the predominant clonotypes is likely to yield additional and improved genetic markers for molecular diagnostics.…”

Section: Figmentioning

confidence: 99%

Predictive Diagnostics for Escherichia coli Infections Based on the Clonal Association of Antimicrobial Resistance and Clinical Outcome

et al. 2013

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The ability to identify bacterial pathogens at the subspecies level in clinical diagnostics is currently limited. We investigated whether splitting Escherichia coli species into clonal groups (clonotypes) predicts antimicrobial susceptibility or clinical outcome. A total of 1,679 extraintestinal E. coli isolates (collected from 2010 to 2012) were collected from one German and 5 U.S. clinical microbiology laboratories. Clonotype identity was determined by fumC and fimH (CH) sequencing. The associations of clonotype with antimicrobial susceptibility and clinical variables were evaluated. CH typing divided the isolates into >200 CH clonotypes, with 93% of the isolates belonging to clonotypes with >2 isolates. Antimicrobial susceptibility varied substantially among clonotypes but was consistent across different locations. Clonotype-guided antimicrobial selection significantly reduced "drug-bug" mismatch compared to that which occurs with the use of conventional empirical therapy. With trimethoprim-sulfamethoxazole and fluoroquinolones, the drug-bug mismatch was predicted to decrease 62% and 78%, respectively. Recurrent or persistent urinary tract infection and clinical sepsis were significantly correlated with specific clonotypes, especially with CH40-30 (also known as H30), a recently described clonotype within sequence type 131 (ST131). We were able to clonotype directly from patient urine samples within 1 to 3 h of obtaining the specimen. In E. coli, subspecies-level identification by clonotyping can be used to significantly improve empirical predictions of antimicrobial susceptibility and clinical outcomes in a timely manner. Bacterial species identification is essential for the correct diagnosis of disease and to optimize the empirical choice of antimicrobial treatment before the results of culturing and susceptibility testing are available (up to 2 to 3 days) (1, 2). However, even within a single bacterial species, there is substantial strain-tostrain variation in antimicrobial susceptibilities and virulence (3), and the increasing prevalence of antimicrobial-resistant and multidrug-resistant bacterial pathogens is one of the greatest challenges in clinical medicine today (4, 5). Thus, subspecies-, strain-, or clonal group-level identification might provide significant advantages for the diagnosis of bacterial infections.Escherichia coli is a leading extraintestinal (found especially in the urine and blood) pathogen in the United States, causing millions of infections and tens of thousands of deaths each year (6). As a clonal species, E. coli contains a limited number of genetically related lineages (i.e., clonotypes) (10). Although several E. coli clonotypes with distinctive antimicrobial susceptibility patterns have been described (11)(12)(13)(14)(15), the use of clonotyping as a general predictive marker for antimicrobial susceptibility among unselected extraintestinal clinical E. coli isolates has not been reported. Additionally, the two most-commonly used clonal typing methods for E. coli, multilocus sequ...

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“…Within the EHR, data elements, including laboratory values, demographic data, medical history, infusional drug administration, and oral chemotherapy prescriptions, are stored in discrete structured fields and entered into a data warehouse. 3 The data warehouse is a clearinghouse for data from the clinical, laboratory, and billing systems for the institution in an Oracle-based platform. Our institution has a robust bioinformatics infrastructure with the ability to generate reports from the data warehouse by using Cognos software as a reporting tool that allows us to address both the process and outcomes of cancer care.…”

Section: Epic Ehr Impact On Quality and Researchmentioning

confidence: 99%

Effect of an Electronic Health Record on the Culture of an Outpatient Medical Oncology Practice in a Four-Hospital Integrated Health Care System: 5-Year Experience

Brockstein

Hensing

Carro

et al. 2011

JOP

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The electronic health record (EHR) was adopted into the NorthShore University HealthSystem, a four-hospital integrated health system located in suburban Chicago, in 2003. By 2005, all chemotherapy and medicine order entry was conducted through the EHR, completing the incorporation of a fully paperless EHR in our hospital-based oncology practice in both the inpatient and outpatient settings. The use of the EHR has dramatically changed our practice environment by improving efficiency, patient safety, research productivity, and operations, while allowing evaluation of adherence to established quality measures and incorporation of new quality improvement initiatives. The reach of the EHR has been substantial and has influenced every aspect of care at our institution over the short period since its implementation. In this article, we describe subjective and objective measures, outcomes, and achievements of our 5-year EHR experience.

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Electronic Prediction Rules for Methicillin-ResistantStaphylococcus aureusColonization

Cited by 37 publications

References 43 publications

Methicillin-Resistant Staphylococcus aureus Control in the 21st Century: Laboratory Involvement Affecting Disease Impact and Economic Benefit from Large Population Studies

Methicillin-Resistant Staphylococcus aureus Control in the 21st Century: Laboratory Involvement Affecting Disease Impact and Economic Benefit from Large Population Studies

Predictive Diagnostics for Escherichia coli Infections Based on the Clonal Association of Antimicrobial Resistance and Clinical Outcome

Effect of an Electronic Health Record on the Culture of an Outpatient Medical Oncology Practice in a Four-Hospital Integrated Health Care System: 5-Year Experience

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