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- d'Alessandro, Elisa, et al.
(författare)
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Thrombo-Inflammation in Cardiovascular Disease : An Expert Consensus Document from the Third Maastricht Consensus Conference on Thrombosis
- 2020
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Ingår i: Thrombosis and Haemostasis. - : Georg Thieme Verlag KG. - 0340-6245 .- 2567-689X. ; 120:4, s. 538-564
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Tidskriftsartikel (refereegranskat)abstract
- Thrombo-inflammation describes the complex interplay between blood coagulation and inflammation that plays a critical role in cardiovascular diseases. The third Maastricht Consensus Conference on Thrombosis assembled basic, translational, and clinical scientists to discuss the origin and potential consequences of thrombo-inflammation in the etiology, diagnostics, and management of patients with cardiovascular disease, including myocardial infarction, stroke, and peripheral artery disease. This article presents a state-of-the-art reflection of expert opinions and consensus recommendations regarding the following topics: (1) challenges of the endothelial cell barrier; (2) circulating cells and thrombo-inflammation, focused on platelets, neutrophils, and neutrophil extracellular traps; (3) procoagulant mechanisms; (4) arterial vascular changes in atherogenesis; attenuating atherosclerosis and ischemia/reperfusion injury; (5) management of patients with arterial vascular disease; and (6) pathogenesis of venous thrombosis and late consequences of venous thromboembolism.
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| 3. |
- Verberk, J.D.M., et al.
(författare)
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Automated surveillance systems for healthcare-associated infections : results from a European survey and experiences from real-life utilization
- 2022
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Ingår i: Journal of Hospital Infection. - : Elsevier. - 0195-6701 .- 1532-2939. ; 122, s. 35-43
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Tidskriftsartikel (refereegranskat)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: To describe key aspects of the design of AS systems and implementation in European institutions and hospitals.Methods: An online survey was distributed via e-mail in February/March 2019 among (i) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; (ii) corresponding authors of peer-reviewed European publications on existing AS systems; and (iii) 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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- van Mourik, Maaike S. M., et al.
(författare)
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PRAISE : providing a roadmap for automated infection surveillance in Europe
- 2021
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Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 27, s. S3-S19
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Tidskriftsartikel (refereegranskat)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. (C) 2021 Published by Elsevier Ltd on behalf of European Society of Clinical Microbiology and Infectious Diseases.
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| 7. |
- Behnke, Michael, et al.
(författare)
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Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections
- 2021
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Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 27:Suppl 1, s. S29-S39
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Healthcare-associated infections (HAI) are a major public health concern. Monitoring of HAI rates, with feedback, is a core component of infection prevention and control programmes. Digitalization of healthcare data has created novel opportunities for automating the HAI surveillance process to varying degrees. However, methods are not standardized and vary widely between different healthcare facilities. Most current automated surveillance (AS) systems have been confined to local settings, and practical guidance on how to implement large-scale AS is needed. Methods: This document was written by a task force formed in March 2019 within the PRAISE network (Providing a Roadmap for Automated Infection Surveillance in Europe), gathering experts in HAI surveillance from ten European countries. Results: The document provides an overview of the key e-health aspects of implementing an AS system of HAI in a clinical environment to support both the infection prevention and control team and information technology (IT) departments. The focus is on understanding the basic principles of storage and structure of healthcare data, as well as the general organization of IT infrastructure in surveillance networks and participating healthcare facilities. The fundamentals of data standardization, interoperability and algorithms in relation to HAI surveillance are covered. Finally, technical aspects and practical examples of accessing, storing and sharing healthcare data within a HAI surveillance network, as well as maintenance and quality control of such a system, are discussed. Conclusions: With the guidance given in this document, along with the PRAISE roadmap and governance documents, readers will find comprehensive support to implement large-scale AS in a surveillance network.
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- van Rooden, Stephanie M., et al.
(författare)
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Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections
- 2021
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Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 27:Supplement 1, s. S20-S28
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Surveillance of healthcare-associated infections (HAI) is increasingly automated by applying algorithms to routine-care data stored in electronic health records. Hitherto, initiatives have mainly been confined to single healthcare facilities and research settings, leading to heterogeneity in design. The PRAISE network – Providing a Roadmap for Automated Infection Surveillance in Europe – designed a roadmap to provide guidance on how to move automated surveillance (AS) from the research setting to large-scale implementation. Supplementary to this roadmap, we here discuss the governance aspects of automated HAI surveillance within networks, aiming to support both the coordinating centres and participating healthcare facilities as they set up governance structures and to enhance involvement of legal specialists.Methods: This article is based on PRAISE network discussions during two workshops. A taskforce was installed that further elaborated governance aspects for AS networks by reviewing documents and websites, consulting experts and organizing teleconferences. Finally, the article has been reviewed by an independent panel of international experts.Results: Strict governance is indispensable in surveillance networks, especially when manual decisions are replaced by algorithms and electronically stored routine-care data are reused for the purpose of surveillance. For endorsement of AS networks, governance aspects specifically related to AS networks need to be addressed. Key considerations include enabling participation and inclusion, trust in the collection, use and quality of data (including data protection), accountability and transparency.Conclusions: This article on governance aspects can be used by coordinating centres and healthcare facilities participating in an AS network as a starting point to set up governance structures. Involvement of main stakeholders and legal specialists early in the development of an AS network is important for endorsement, inclusivity and compliance with the laws and regulations that apply.
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| 9. |
- Verberk, Janneke D. M., et al.
(författare)
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The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery
- 2023
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Ingår i: Antimicrobial Resistance and Infection Control. - 2047-2994. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundIn patients who underwent colorectal surgery, an existing semi-automated surveillance algorithm based on structured data achieves high sensitivity in detecting deep surgical site infections (SSI), however, generates a significant number of false positives. The inclusion of unstructured, clinical narratives to the algorithm may decrease the number of patients requiring manual chart review. The aim of this study was to investigate the performance of this semi-automated surveillance algorithm augmented with a natural language processing (NLP) component to improve positive predictive value (PPV) and thus workload reduction (WR).MethodsRetrospective, observational cohort study in patients who underwent colorectal surgery from January 1, 2015, through September 30, 2020. NLP was used to detect keyword counts in clinical notes. Several NLP-algorithms were developed with different count input types and classifiers, and added as component to the original semi-automated algorithm. Traditional manual surveillance was compared with the NLP-augmented surveillance algorithms and sensitivity, specificity, PPV and WR were calculated.ResultsFrom the NLP-augmented models, the decision tree models with discretized counts or binary counts had the best performance (sensitivity 95.1% (95%CI 83.5-99.4%), WR 60.9%) and improved PPV and WR by only 2.6% and 3.6%, respectively, compared to the original algorithm.ConclusionsThe addition of an NLP component to the existing algorithm had modest effect on WR (decrease of 1.4-12.5%), at the cost of sensitivity. For future implementation it will be a trade-off between optimal case-finding techniques versus practical considerations such as acceptability and availability of resources.
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