Computer Algorithms To Detect Bloodstream Infections

Trick, William E.; Zagorski, Brandon; Tokars, Jerome I.; Vernon, Michael O.; Welbel, Sharon F.; Wisniewski, Mary F.; Richards, Chesley L.; Weinstein, Robert A.

doi:10.3201/eid1009.030978

Cited by 139 publications

(113 citation statements)

References 22 publications

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“…[11][12][13][14] However, most institutions have focused on CLABSIs, because these seem more amenable to prevention interventions. The study by Trick et al 6 provided a solid step toward performing automated surveillance for CLABSI, but those investigators did not have access to central line data electronically. Our current study shows results comparable to, if not slightly better than, those of Trick and colleagues and has the advantage that surveillance could be done completely electronically.…”

Section: Discussionmentioning

confidence: 99%

“…18 This variability, combined with less comprehensive case finding, tends to lead toward underreporting of CLABSI by traditional surveillance. 6,[19][20][21] Thus, while electronic surveillance may report a higher rate than traditional surveillance, the "true rate" is probably somewhere in between. Since we did not have traditional manual surveillance in place in the study units, we were not able to validate this assumption as part of our study.…”

Section: Discussionmentioning

confidence: 99%

“…A preliminary study suggested that this approach was feasible, but the study was hampered by the lack of an electronic source of central line data. 6 We report here a pilot project to evaluate totally electronic surveillance of CLABSI.…”

mentioning

confidence: 99%

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Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Woeltje

McMullen

Butler

et al. 2011

Infect. Control Hosp. Epidemiol.

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"Electronic surveillance for healthcare-associated central line-associated bloodstream infections outside the intensive care unit." Infection Control and Hospital Epidemiology.32,11. 1086-1090. (2011 http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. The University of Chicago Press and The Society for Healthcare Epidemiology of America are collaborating with JSTOR to digitize, preserve and extend access to Infection Control and Hospital Epidemiology.http://www.jstor.org infection control and hospital epidemiology november 2011, vol. 32, no. 11 o r i g i n a l a r t i c l e background. Manual surveillance for central line-associated bloodstream infections (CLABSIs) by infection prevention practitioners is time-consuming and often limited to intensive care units (ICUs). An automated surveillance system using existing databases with patientlevel variables and microbiology data was investigated.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Woeltje

McMullen

Butler

et al. 2011

Infect. Control Hosp. Epidemiol.

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“…Positive blood culture results are simple to identify, are highly likely to represent infection, and are a well-validated metric. 9 In addition, this metric has been shown to decrease in re sponse to interventions to prevent MDRO infections in pub lished studies from institutions with high rates of MRSA 41'42 and VRE 43 bacteremia.…”

Section: Description Of Moro Metricsmentioning

confidence: 99%

Recommendations For Metrics For Multidrug-Resistant Organisms In Healthcare Settings: SHEA/HICPAC Position Paper

Cohen

Calfee

Fridkin

et al. 2008

Infect. Control Hosp. Epidemiol.

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161

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EXECUTIVE SUMMARYMonitoring multidrug-resistant organisms (MDROs) and the infections they cause in a healthcare setting is important to detect newly emerging antimicrobial resistance profiles, to identify vulnerable patient populations, and to assess the need for and effectiveness of interventions; however, it is unclear which metrics are the best, because most of the metrics are not standardized. This document describes useful and prac tical metrics and surveillance considerations for measuring MDROs and the infections they cause in the practice of in fection prevention and control in healthcare settings. These metrics are designed to aid healthcare workers in docu menting trends over time within their facility and should not be used for interfacility comparison.The following MDROs are addressed: (1) methicillin-re sistant Staphylococcus aureus; (2) vancomycin-resistant En terococcus species; (3) multidrug-resistant gram-negative ba cilli; and (4) vancomycin-resistant S. aureus. We convened a working group of experts that reviewed current practices, the peer-reviewed literature, and existing guidelines on surveil lance strategies and key metrics.We propose that healthcare facilities use the following 4 routine metrics to monitor MDROs and the infections they cause: (1) an MORO-specific line list for tracking patients who have acquired an MDRO; (2) an antibiogram for mon itoring susceptibility patterns of isolates recovered from pa tients; (3) the incidence of hospital-onset MDRO bacteremia, which is an objective, laboratory-based metric that is highly associated with invasive disease and does not require chart review to estimate infection burden; and ( 4) clinical culture results, to measure incidence of infection or colonization, to quantify the number of people whose MORO acquisition is healthcare associated. In addition, healthcare facilities may want to calculate both the overall prevalence of carriage and the prevalence of carriage at admission, the latter of which can be useful in detecting importation of methicillin-resistant S. aureus into healthcare facilities, to estimate the exposure burden. Active surveillance testing can augment and increase the accuracy of some metrics. Healthcare facilities not per forming active surveillance testing might wish to consider point-prevalence screening, to help assess how much the number of positive clinical culture results underestimates the hidden reservoir of MDROs. It is important to understand the limitations of all proxy metrics. Because of the paucity of published research findings focused on this area of study, most recommendations were based on opinion and were heavily influenced by the perceived usefulness and simplicity of the metric for assessing MDROs in the hospital setting and for determining the impact of interventions.

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“…Probability estimat ions through clinical parameter modelling needs to avoid the rapidly raised infection rates that are observable through infection detection algorith mically [19]. Instead of binary algorith mic grouping, total rates would be p roduced through summing of estimated probabilities [20].…”

Section: ) Discrete Data Capturingmentioning

confidence: 99%

Development of Infection Control Surveillance System for Intensive Care Unit: Data Requirements and Guidelines

Aljohani¹,

Nadeem²,

Abumelha³

et al. 2016

IJISA

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Abstract-Surveillance systems are useful in the identification of patients that contract infections during their hospitalization period. Despite still being at in fancy, electronic information control surveillance systems for Hospital Acquired Infect ions (HAIs) are imp roving and becoming more co mmonplace as the acceptance levels rise. There are crucial gaps in existing knowledge concerning the best ways for imp lementing electronic surveillance systems especially in the context of the Intensive Care Unit (ICU). To bridge this gap, the aim of this paper was to provide a comprehensive review of various electronic surveillance approaches and to highlight the requisite data components and offer guidelines. This rev iew revealed denominator, nu merator, and discrete data requirements and guidelines for the surveillance of four main ICU HAIs, including Central Line-Associated Bloodstream Infection (CLABSI), Urinary Tract Infect ion (UTI), Surgical Site Infect ions (SSIs) and Ventilator-Associated Conditions/Events (VACs/VAEs).

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Computer Algorithms To Detect Bloodstream Infections

Abstract: Automated bloodstream infection surveillance using electronic data is an accurate alternative to surveillance using manually collected data.

Cited by 139 publications

References 22 publications

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Electronic Surveillance for Healthcare-Associated Central Line—Associated Bloodstream Infections Outside the Intensive Care Unit

Recommendations For Metrics For Multidrug-Resistant Organisms In Healthcare Settings: SHEA/HICPAC Position Paper

Development of Infection Control Surveillance System for Intensive Care Unit: Data Requirements and Guidelines

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