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Verberk JDM, Aghdassi SJS, Abbas M, Nauclér P, Gubbels S, Maldonado N, Palacios-Baena ZR, Johansson AF, Gastmeier P, Behnke M, van Rooden SM, van Mourik MSM. Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization. J Hosp Infect 2022; 122:35-43. [PMID: 35031393 DOI: 10.1016/j.jhin.2021.12.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/04/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce. AIM We aimed to describe key aspects of the design of AS systems and implementation in European institutions and hospitals. METHODS An online survey was distributed via email in February/March 2019 among 1) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; 2) corresponding authors of peer-reviewed European publications on existing AS systems; and 3) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice. FINDINGS Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure. CONCLUSION AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods and the state of implementation.
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Affiliation(s)
- Janneke D M Verberk
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands; Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands.
| | - Seven J S Aghdassi
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Pontus Nauclér
- Department of Medicine Solna, Division of Infectious Disease, Karolinska Institutet, Stockholm, Sweden; Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Sophie Gubbels
- Department of Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark
| | - Natalia Maldonado
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (IBIS), Sevilla, Spain
| | - Zaira R Palacios-Baena
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (IBIS), Sevilla, Spain
| | - Anders F Johansson
- Department of Clinical microbiology and the Laboratory for Molecular Infection Medicine (MIMS), Umeå University, Umeå, Sweden
| | - Petra Gastmeier
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Behnke
- Institute of Hygiene and Environmental Medicine, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Stephanie M van Rooden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands; Department of Epidemiology and Surveillance, Centre for Infectious Diseases Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Maaike S M van Mourik
- Department of Medical Microbiology and Infection Prevention, University Medical Centre Utrecht, Utrecht, the Netherlands
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van Mourik MSM, van Rooden SM, Abbas M, Aspevall O, Astagneau P, Bonten MJM, Carrara E, Gomila-Grange A, de Greeff SC, Gubbels S, Harrison W, Humphreys H, Johansson A, Koek MBG, Kristensen B, Lepape A, Lucet JC, Mookerjee S, Naucler P, Palacios-Baena ZR, Presterl E, Pujol M, Reilly J, Roberts C, Tacconelli E, Teixeira D, Tängdén T, Valik JK, Behnke M, Gastmeier P. PRAISE: providing a roadmap for automated infection surveillance in Europe. Clin Microbiol Infect 2021; 27 Suppl 1:S3-S19. [PMID: 34217466 DOI: 10.1016/j.cmi.2021.02.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/24/2021] [Accepted: 02/27/2021] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Healthcare-associated infections (HAI) are among the most common adverse events of medical care. Surveillance of HAI is a key component of successful infection prevention programmes. Conventional surveillance - manual chart review - is resource intensive and limited by concerns regarding interrater reliability. This has led to the development and use of automated surveillance (AS). Many AS systems are the product of in-house development efforts and heterogeneous in their design and methods. With this roadmap, the PRAISE network aims to provide guidance on how to move AS from the research setting to large-scale implementation, and how to ensure the delivery of surveillance data that are uniform and useful for improvement of quality of care. METHODS The PRAISE network brings together 30 experts from ten European countries. This roadmap is based on the outcome of two workshops, teleconference meetings and review by an independent panel of international experts. RESULTS This roadmap focuses on the surveillance of HAI within networks of healthcare facilities for the purpose of comparison, prevention and quality improvement initiatives. The roadmap does the following: discusses the selection of surveillance targets, different organizational and methodologic approaches and their advantages, disadvantages and risks; defines key performance requirements of AS systems and suggestions for their design; provides guidance on successful implementation and maintenance; and discusses areas of future research and training requirements for the infection prevention and related disciplines. The roadmap is supported by accompanying documents regarding the governance and information technology aspects of implementing AS. CONCLUSIONS Large-scale implementation of AS requires guidance and coordination within and across surveillance networks. Transitions to large-scale AS entail redevelopment of surveillance methods and their interpretation, intensive dialogue with stakeholders and the investment of considerable resources. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.
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Affiliation(s)
- Maaike S M van Mourik
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, the Netherlands.
| | - Stephanie M van Rooden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Centre for Infectious Disease Epidemiology and Surveillance National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Mohamed Abbas
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Olov Aspevall
- Unit for Surveillance and Coordination, Public Health Agency of Sweden, Solna, Sweden
| | - Pascal Astagneau
- Centre for Prevention of Healthcare-Associated Infections, Assistance Publique - Hôpitaux de Paris & Faculty of Medicine, Sorbonne University, Paris, France
| | - Marc J M Bonten
- Department of Medical Microbiology and Infection Control, University Medical Center Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Elena Carrara
- Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Italy
| | - Aina Gomila-Grange
- Infectious Diseases Unit, Bellvitge Biomedical Research Institute (IDIBELL), Bellvitge University Hospital, Barcelona, Infectious Diseases Unit, Consorci Corporació Sanitària Parc Taulí, Barcelona, Spain
| | - Sabine C de Greeff
- Centre for Infectious Disease Epidemiology and Surveillance National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Sophie Gubbels
- Data Integration and Analysis Secretariat, Statens Serum Institut, Copenhagen, Denmark
| | - Wendy Harrison
- Healthcare Associated Infections, Antimicrobial Resistance and Prescribing Programme (HARP), Public Health Wales, UK
| | - Hilary Humphreys
- Department of Clinical Microbiology, The Royal College of Surgeons in Ireland, Department of Microbiology, Beaumont Hospital, Dublin, Ireland
| | | | - Mayke B G Koek
- Centre for Infectious Disease Epidemiology and Surveillance National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Brian Kristensen
- Department of Infectious Disease Epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark
| | - Alain Lepape
- Clinical Research Unit, Department of Intensive Care, Centre Hospitalier Universitaire Lyon Sud 69495, Pierre-Bénite, France
| | - Jean-Christophe Lucet
- Infection Control Unit, Hôpital Bichat-Claude Bernard Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Siddharth Mookerjee
- Infection Prevention and Control Department, Imperial College Healthcare NHS Trust, UK
| | - Pontus Naucler
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet and Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Zaira R Palacios-Baena
- Unit of Infectious Diseases, Clinical Microbiology and Preventive Medicine, Hospital Universitario Virgen Macarena, Institute of Biomedicine of Seville (I. BIS), Sevilla, Spain
| | - Elisabeth Presterl
- Department of Infection Control and Hospital Epidemiology, Medical University of Vienna, Austria
| | - Miquel Pujol
- Infectious Diseases Unit, Bellvitge Biomedical Research Institute (IDIBELL), Bellvitge University Hospital, Barcelona, Infectious Diseases Unit, Consorci Corporació Sanitària Parc Taulí, Barcelona, Spain
| | - Jacqui Reilly
- Safeguarding Health Through Infection Prevention Research Group, Institute for Applied Health Research, Glasgow Caledonian University, Scotland, UK
| | - Christopher Roberts
- Healthcare Associated Infections, Antimicrobial Resistance and Prescribing Programme (HARP), Public Health Wales, UK
| | - Evelina Tacconelli
- Infectious Diseases, Research Clinical Unit, DZIF Center, University Hospital Tübingen, Germany; Infectious Diseases Section, Department of Diagnostics and Public Health, University of Verona, Italy
| | - Daniel Teixeira
- Infection Control Programme, Geneva University Hospitals, Geneva, Switzerland
| | - Thomas Tängdén
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - John Karlsson Valik
- Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet and Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Michael Behnke
- National Reference Center for Surveillance of nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
| | - Petra Gastmeier
- National Reference Center for Surveillance of nosocomial Infections, Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Hygiene and Environmental Medicine, Berlin, Germany
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Cuff SM, Merola JP, Twohig JP, Eberl M, Gray WP. Toll-like receptor linked cytokine profiles in cerebrospinal fluid discriminate neurological infection from sterile inflammation. Brain Commun 2020; 2:fcaa218. [PMID: 33409494 PMCID: PMC7772097 DOI: 10.1093/braincomms/fcaa218] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 10/12/2020] [Accepted: 11/12/2020] [Indexed: 12/13/2022] Open
Abstract
Rapid determination of an infective aetiology causing neurological inflammation in the cerebrospinal fluid can be challenging in clinical practice. Post-surgical nosocomial infection is difficult to diagnose accurately, as it occurs on a background of altered cerebrospinal fluid composition due to the underlying pathologies and surgical procedures involved. There is additional diagnostic difficulty after external ventricular drain or ventriculoperitoneal shunt surgery, as infection is often caused by pathogens growing as biofilms, which may fail to elicit a significant inflammatory response and are challenging to identify by microbiological culture. Despite much research effort, a single sensitive and specific cerebrospinal fluid biomarker has yet to be defined which reliably distinguishes infective from non-infective inflammation. As a result, many patients with suspected infection are treated empirically with broad-spectrum antibiotics in the absence of definitive diagnostic criteria. To begin to address these issues, we examined cerebrospinal fluid taken at the point of clinical equipoise to diagnose cerebrospinal fluid infection in 14 consecutive neurosurgical patients showing signs of inflammatory complications. Using the guidelines of the Infectious Diseases Society of America, six cases were subsequently characterized as infected and eight as sterile inflammation. Twenty-four contemporaneous patients with idiopathic intracranial hypertension or normal pressure hydrocephalus were included as non-inflamed controls. We measured 182 immune and neurological biomarkers in each sample and used pathway analysis to elucidate the biological underpinnings of any biomarker changes. Increased levels of the inflammatory cytokine interleukin-6 and interleukin-6-related mediators such as oncostatin M were excellent indicators of inflammation. However, interleukin-6 levels alone could not distinguish between bacterially infected and uninfected patients. Within the patient cohort with neurological inflammation, a pattern of raised interleukin-17, interleukin-12p40/p70 and interleukin-23 levels delineated nosocomial bacteriological infection from background neuroinflammation. Pathway analysis showed that the observed immune signatures could be explained through a common generic inflammatory response marked by interleukin-6 in both nosocomial and non-infectious inflammation, overlaid with a toll-like receptor-associated and bacterial peptidoglycan-triggered interleukin-17 pathway response that occurred exclusively during infection. This is the first demonstration of a pathway dependent cerebrospinal fluid biomarker differentiation distinguishing nosocomial infection from background neuroinflammation. It is especially relevant to the commonly encountered pathologies in clinical practice, such as subarachnoid haemorrhage and post-cranial neurosurgery. While requiring confirmation in a larger cohort, the current data indicate the potential utility of cerebrospinal fluid biomarker strategies to identify differential initiation of a common downstream interleukin-6 pathway to diagnose nosocomial infection in this challenging clinical cohort.
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Affiliation(s)
- Simone M Cuff
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Joseph P Merola
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Jason P Twohig
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Matthias Eberl
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - William P Gray
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
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Streefkerk HRA, Verkooijen RP, Bramer WM, Verbrugh HA. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review. ACTA ACUST UNITED AC 2020; 25. [PMID: 31964462 PMCID: PMC6976884 DOI: 10.2807/1560-7917.es.2020.25.2.1900321] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Surveillance of healthcare-associated infections (HAI) is the basis of each infection control programme and, in case of acute care hospitals, should ideally include all hospital wards, medical specialties as well as all types of HAI. Traditional surveillance is labour intensive and electronically assisted surveillance systems (EASS) hold the promise to increase efficiency. Objectives To give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them. Methods In this systematic review, online databases were searched and studies that compared an EASS with a traditional surveillance method were included. Two different indicators were extracted from each study, one regarding the quality of design (including reporting efficiency) and one based on the performance (e.g. specificity and sensitivity) of the EASS presented. Results A total of 78 studies were included. The majority of EASS (n = 72) consisted of an algorithm-based selection step followed by confirmatory assessment. The algorithms used different sets of variables. Only a minority (n = 7) of EASS were hospital-wide and designed to detect all types of HAI. Sensitivity of EASS was generally high (> 0.8), but specificity varied (0.37–1). Less than 20% (n = 14) of the studies presented data on the efficiency gains achieved. Conclusions Electronically assisted surveillance of HAI has yet to reach a mature stage and to be used routinely in healthcare settings. We recommend that future studies on the development and implementation of EASS of HAI focus on thorough validation, reproducibility, standardised datasets and detailed information on efficiency.
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Affiliation(s)
- H Roel A Streefkerk
- Albert Schweitzer Hospital/Rivas group Beatrix hospital/Regionaal Laboratorium medische Microbiologie, Dordrecht/Gorinchem, the Netherlands.,Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
| | - Roel Paj Verkooijen
- Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Wichor M Bramer
- Medical Library, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Henri A Verbrugh
- Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
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Malheiro R, Rocha-Pereira N, Duro R, Pereira C, Alves CL, Correia S. Validation of a semi-automated surveillance system for surgical site infections: Improving exhaustiveness, representativeness, and efficiency. Int J Infect Dis 2020; 99:355-361. [PMID: 32777583 DOI: 10.1016/j.ijid.2020.07.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/14/2020] [Accepted: 07/19/2020] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVES To assess whether electronic records data could improve the efficiency, exhaustiveness, and representativeness of SSI surveillance by selecting a group of high-risk patients for manual review. METHODS Colorectal surgeries (2016-2018) and cholecystectomies (2017-2018) were selected. Post-surgical antibiotic use, positive culture, C-reactive protein (CRP) values, body temperature, leukocyte count, surgical re-intervention, admission to the emergency room, and hospital readmission were retrieved. For representativeness, procedures registered in HAI-Net were compared with non-included procedures, and the validity of each variable (or combination) was tested considering the presence of SSI as the gold standard. The proportion of procedures flagged for manual review by each criterion was estimated. RESULTS Little more than 50% of procedures were included in HAI-Net (SSI risk: 10.6% for colorectal and 2.9% for cholecystectomies). Non-included procedures showed higher proportions of infection markers. Antibiotic use and CRP >100 mg/dl presented the highest sensitivity for both surgical groups, while antibiotic use achieved the highest positive predictive value in both groups (22% and 21%, respectively) and flagged fewer colorectal procedures (47.7%). CONCLUSIONS Current SSI surveillance has major limitations. Thus, the reported incidence seems unreliable and underestimated. Antibiotic use appears to be the best criterion to select a sub-sample of procedures for manual review, improving the exhaustiveness and efficiency of the system.
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Affiliation(s)
- Rui Malheiro
- Eastern Porto Public Health Unit (ACES Porto Oriental), Administração Regional de Saúde, Porto, Portugal; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.
| | - Nuno Rocha-Pereira
- Infection and Antimicrobial Resistance Control and Prevention Unit, Hospital Epidemiology Centre, Centro Hospitalar Universitário S. João, Porto, Portugal; Infectious Diseases Department, Centro Hospitalar Universitário S. João, Porto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
| | - Raquel Duro
- Infection and Antimicrobial Resistance Control and Prevention Unit, Hospital Epidemiology Centre, Centro Hospitalar Universitário S. João, Porto, Portugal; Infectious Diseases Department, Centro Hospitalar Universitário S. João, Porto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
| | - Cláudia Pereira
- Infection and Antimicrobial Resistance Control and Prevention Unit, Hospital Epidemiology Centre, Centro Hospitalar Universitário S. João, Porto, Portugal
| | - Carlos Lima Alves
- Infection and Antimicrobial Resistance Control and Prevention Unit, Hospital Epidemiology Centre, Centro Hospitalar Universitário S. João, Porto, Portugal; Faculty of Medicine, University of Porto, Porto, Portugal
| | - Sofia Correia
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Department of Public Health and Forensic Sciences, and Medical Education, Faculdade de Medicina Universidade do Porto, Porto, Portugal
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Surveillance of external ventricular drainage-associated meningitis and ventriculitis in German intensive care units. Infect Control Hosp Epidemiol 2020; 41:452-457. [PMID: 31918776 DOI: 10.1017/ice.2019.367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE In light of the infection risk associated with external ventricular drainage (EVD), we decided to establish the surveillance of EVD-associated meningitis/ventriculitis in German intensive care units (ICUs) in the framework of the German national nosocomial infection surveillance system (KISS). Here, we present the current reference data and subsequent risk-factor analysis for EVD-associated meningitis/ventriculitis rates. METHODS The surveillance method corresponds with the surveillance methods for device-associated infections recommended by the National Healthcare Safety Network (NHSN). All ICUs participating for at least 1 month from 2008 to 2016 in the module ICU-KISS were included in the reference dataset and the multivariate analysis. RESULTS Current reference data (2008-2016) are based on input from 157 ICUs. The mean EVD-associated meningitis/ventriculitis rate per 1,000 EVD days was 3.96, with little variation between neurosurgical, surgical, interdisciplinary (hospitals with >400 beds), and neurological ICUs. In total, 893 EVD-associated meningitis/ventriculitis cases and 225,351 EVD days were included in the risk-factor analysis. After multivariate analysis, 2 factors remained significant: (1) stay in an ICU labeled other than neurosurgical, surgical, interdisciplinary (>400 beds), and neurological as a protective factor and (2) EVD utilization rate above the 75th quantile as a risk factor for acquisition of EVD-associated meningitis/ventriculitis. CONCLUSIONS EVD-associated meningitis and ventriculitis are frequent complications of care in intensive care patients at risk. A long hospital stay and/or the presence of the EVD puts the patient at high risk for pathogen acquisition with subsequent infection.
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Dorresteijn KR, Jellema K, van de Beek D, Brouwer MC. Factors and measures predicting external CSF drain-associated ventriculitis. Neurology 2019; 93:964-972. [DOI: 10.1212/wnl.0000000000008552] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/29/2019] [Indexed: 12/18/2022] Open
Abstract
ObjectiveTo determine the diagnostic value of clinical factors and biochemical or microbiological measures for diagnosing a drain-associated ventriculitis, we summarized the available evidence.MethodsWe performed a systematic review and meta-analysis of studies of patients with external ventricular CSF drains who developed drain-associated ventriculitis by searching MEDLINE, EMBASE, and CENTRAL electronic database. We reported the occurrence of abnormal test results in patients with and without drain-associated ventriculitis. For continuous variables, we recalculated mean values presented in multiple studies.ResultsWe identified 42 articles published between 1984 and 2018 including 3,035 patients with external CSF drains of whom 697 (23%) developed drain-associated bacterial ventriculitis. Indications for drain placement were subarachnoid, intraventricular or cerebral hemorrhage or hemorrhage not further specified (69%), traumatic brain injury (13%), and obstructive hydrocephalus secondary to a brain tumor (10%). Fever was present in 116 of 162 patients with ventriculitis (72%) compared with 80 of 275 (29%) patients without ventriculitis. The CSF cell count was increased for 74 of 80 patients (93%) with bacterial ventriculitis and 30 of 95 patients (32%) without ventriculitis. CSF culture was positive in 125 of 156 episodes classified as ventriculitis (80%), and CSF Gram stain was positive in 44 of 81 patients (54%). In patients with ventriculitis, PCR on ribosomal RNA was positive on 54 of 78 CSF samples (69%).ConclusionClinical factors and biochemical and microbiological measures have limited diagnostic value in differentiating between ventriculitis and sterile inflammation in patients with external CSF drains. Prospective well-designed diagnostic accuracy studies in drain-associated ventriculitis are needed.
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Mulder T, Kluytmans-van den Bergh MF, van Mourik MS, Romme J, Crolla RM, Bonten MJ, Kluytmans JA. A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery. Infect Control Hosp Epidemiol 2019; 40:574-578. [PMID: 30868984 PMCID: PMC6536899 DOI: 10.1017/ice.2019.36] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/04/2019] [Accepted: 02/05/2019] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Surveillance of surgical site infections (SSIs) is important for infection control and is usually performed through retrospective manual chart review. The aim of this study was to develop an algorithm for the surveillance of deep SSIs based on clinical variables to enhance efficiency of surveillance. DESIGN Retrospective cohort study (2012-2015). SETTING A Dutch teaching hospital. PARTICIPANTS We included all consecutive patients who underwent colorectal surgery excluding those with contaminated wounds at the time of surgery. All patients were evaluated for deep SSIs through manual chart review, using the Centers for Disease Control and Prevention (CDC) criteria as the reference standard. ANALYSIS We used logistic regression modeling to identify predictors that contributed to the estimation of diagnostic probability. Bootstrapping was applied to increase generalizability, followed by assessment of statistical performance and clinical implications. RESULTS In total, 1,606 patients were included, of whom 129 (8.0%) acquired a deep SSI. The final model included postoperative length of stay, wound class, readmission, reoperation, and 30-day mortality. The model achieved 68.7% specificity and 98.5% sensitivity and an area under the receiver operator characteristic (ROC) curve (AUC) of 0.950 (95% CI, 0.932-0.969). Positive and negative predictive values were 21.5% and 99.8%, respectively. Applying the algorithm resulted in a 63.4% reduction in the number of records requiring full manual review (from 1,606 to 590). CONCLUSIONS This 5-parameter model identified 98.5% of patients with a deep SSI. The model can be used to develop semiautomatic surveillance of deep SSIs after colorectal surgery, which may further improve efficiency and quality of SSI surveillance.
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Affiliation(s)
- Tessa Mulder
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Marjolein F.Q. Kluytmans-van den Bergh
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
- Amphia Academy Infectious Disease Foundation, Amphia Hospital, Breda, The Netherlands
- Department of Infection Control, Amphia Hospital, Breda, The Netherlands
| | - Maaike S.M. van Mourik
- Department of Medical Microbiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Jannie Romme
- Department of Infection Control, Amphia Hospital, Breda, The Netherlands
| | | | - Marc J.M. Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
- Department of Medical Microbiology, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Jan A.J.W. Kluytmans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
- Department of Infection Control, Amphia Hospital, Breda, The Netherlands
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Electronic surveillance and using administrative data to identify healthcare associated infections. Curr Opin Infect Dis 2018; 29:394-9. [PMID: 27257794 DOI: 10.1097/qco.0000000000000282] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
PURPOSE OF REVIEW Traditional surveillance of healthcare associated infections (HCAI) is time consuming and error-prone. We have analysed literature of the past year to look at new developments in this field. It is divided into three parts: new algorithms for electronic surveillance, the use of administrative data for surveillance of HCAI, and the definition of new endpoints of surveillance, in accordance with an automatic surveillance approach. RECENT FINDINGS Most studies investigating electronic surveillance of HCAI have concentrated on bloodstream infection or surgical site infection. However, the lack of important parameters in hospital databases can lead to misleading results. The accuracy of administrative coding data was poor at identifying HCAI. New endpoints should be defined for automatic detection, with the most crucial step being to win clinicians' acceptance. SUMMARY Electronic surveillance with conventional endpoints is a successful method when hospital information systems implemented key changes and enhancements. One requirement is the access to systems for hospital administration and clinical databases.Although the primary source of data for HCAI surveillance is not administrative coding data, these are important components of a hospital-wide programme of automated surveillance. The implementation of new endpoints for surveillance is an approach which needs to be discussed further.
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Liu YJ, Shao LH, Zhang J, Fu SJ, Wang G, Chen FZ, Zheng F, Ma RP, Liu HH, Dong XM, Ma LX. The combination of decoy receptor 3 and soluble triggering receptor expressed on myeloid cells-1 for the diagnosis of nosocomial bacterial meningitis. Ann Clin Microbiol Antimicrob 2015; 14:17. [PMID: 25857356 PMCID: PMC4373519 DOI: 10.1186/s12941-015-0078-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2015] [Accepted: 03/12/2015] [Indexed: 11/10/2022] Open
Abstract
Background Early diagnosis and appropriate antibiotic treatment can significantly reduce mortality of nosocomial bacterial meningitis. However, it is a challenge for clinicians to make an accurate and rapid diagnosis of bacterial meningitis. This study aimed at determining whether combined biomarkers can provide a useful tool for the diagnosis of bacterial meningitis. Methods A retrospective study was carried out. Cerebrospinal fluid (CSF) levels of decoy receptor 3 (DcR3) and soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) were detected by enzyme-linked immunosorbent assay (ELISA). Results The patients with bacterial meningitis had significantly elevated levels of the above mentioned biomarkers. The two biomarkers were all risk factors with bacterial meningitis. The biomarkers were constructed into a “bioscore”. The discriminative performance of the bioscore was better than that of each biomarker, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.842 (95% confidence intervals (CI) 0.770–0.914; p< 0.001). Conclusions Combined measurement of CSF DcR3 and sTREM-1 concentrations improved the prediction of nosocomial bacterial meningitis. The combined strategy is of interest and the validation of that improvement needs further studies.
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