Improved diagnosis and management of sepsis and bloodstream infection

Sepsis is a severe organ dysfunction triggered by infections, and a leading cause of hospitalization and death. Concurrent bloodstream infection (BSI) is common and around one third of sepsis patients have positive blood cultures. Prompt diagnosis and treatment is crucial, but there is a trade-off between the negative effects of over diagnosis and failure to recognize sepsis in time. The emerging crisis of antimicrobial resistance has made bacterial infections more difficult to treat, especially gram-negative pathogens such as Pseudomonas aeruginosa. The overall aim with this thesis was to improve diagnosis, assess the influence of time to antimicrobial treatment and explore prognostic bacterial virulence markers in sepsis and BSI. The papers are based on observational data from 7 cohorts of more than 100 000 hospital episodes. In addition, whole genome sequencing has been performed on approximately 800 invasive P. aeruginosa isolates collected from centers in Europe and Australia. Paper I showed that automated surveillance of sepsis incidence using the Sepsis-3 criteria is feasible in the non-ICU setting, with examples of how implementing this model generates continuous epidemiological data down to the ward level. This information can be used for directing resources and evaluating quality-of-care interventions. In Paper II, evidence is provided for using peripheral oxygen saturation (SpO2) to diagnose respiratory dysfunction in sepsis, proposing the novel thresholds 94% and 90% to get 1 and 2 SOFA points, respectively. This has important implications for improving sepsis diagnosis, especially when conventional arterial blood gas measurements are unavailable. Paper III verified that sepsis surveillance data can be utilized to develop machine learning screening tools to improve early identification of sepsis. A Bayesian network algorithm trained on routine electronic health record data predicted sepsis onset within 48 hours with better discrimination and earlier than conventional NEWS2 outside the ICU. The results suggested that screening may primarily be suited for the early admission period, which have broader implications also for other sepsis screening tools. Paper IV demonstrated that delays in antimicrobial treatment with in vitro pathogen coverage in BSI were associated with increased mortality after 12 hours from blood culture collection, but not at 1, 3, and 6 hours. This indicates a time window where clinicians should focus on the diagnostic workup, and proposes a target for rapid diagnostics of blood cultures. Finally, Paper V showed that the virulence genotype had some influence on mortality and septic shock in P. aeruginosa BSI, however, it was not a major prognostic determinant. Together these studies contribute to better understanding of the sepsis and BSI populations, and provide several suggestions to improve diagnosis and timing of treatment, with implications for clinical practice. Future works should focus on the implementation of sepsis surveillance, clinical trials of time to antimicrobial treatment and evaluating the prognostic importance of bacterial genotype data in larger populations from diverse infection sources and pathogens

Electronically assisted surveillance systems of healthcare-associated infections:a systematic review

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

Electronically assisted surveillance systems of healthcare-associated infections: A systematic review

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

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

BackgroundSurveillance 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.ObjectivesTo give insight in the performance characteristics of different approaches to EASS and the quality of the studies designed to evaluate them.MethodsIn 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.ResultsA 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.ConclusionsElectronically 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

Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections

PRAISE network: Maaike S. M. van Mourik, Stephanie M.van Rooden, Mohamed Abbas, Olov Aspevall, Pascal Astagneau, Marc J. M. Bonten, Elena Carrara, Aina Gomila-Grange, Sabine C. de Greeff , Sophie Gubbels, Wendy Harrison, Hilary Humphreys, Anders Johansson, Mayke B. G. Koek, Brian Kristensen, Alain Lepape, Jean-Christophe Lucet, Siddharth Mookerjee, Pontus Naucler, Zaira R. Palacios-Baena, Elisabeth Presterl, Miquel Pujol, Jacqui Reilly, Christopher Roberts, Evelina Tacconelli, Daniel Teixeira, Thomas Tängdén, John Karlsson Valik, Michael Behnke, PetraGastmeier.[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.This network has been supported under the 7th transnational call within the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR), Network Call on Surveillance (2018) and was thereby funded by ZonMw (grant 549007001). This project also received support from the COMBACTE MAGNET EPI-Net project funded by the Innovative Medicines Initiative Joint Undertaking under grant agreement 115523 | 115620 | 115737 | 777362, resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. J.K.V. was supported by grants from Region Stockholm and Vinnova.Peer reviewe

Computer Algorithms To Detect Bloodstream Infections

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

Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections

National Healthcare Safety Network (NHSN) overview

Called also: Patient safety component manual.The NHSN is a secure, Internet-based surveillance system that expands and integrates patient, and healthcare personnel, safety surveillance systems managed by the Division of Healthcare Quality Promotion (DHQP) at the Centers for Disease Control and Prevention. Facilities that participate in certain reporting programs operated by the Centers for Medicare and Medicaid Services (CMS) can do so through use of NHSN. Furthermore, some U.S. states use NHSN as a means for healthcare facilities to submit data on healthcare-associated infections (HAIs) mandated through their specific state legislation.NHSN enables healthcare facilities to collect and use data about HAIs, adherence to clinical practices known to prevent HAIs, the incidence or prevalence of multidrug- resistant organisms within their organizations, trends and coverage of healthcare personnel safety and vaccination, and adverse events related to the transfusion of blood and blood products.The NHSN includes five components: Patient Safety, Long-term Care Facility, Outpatient Dialysis, Healthcare Personnel Safety, and Biovigilance (Figure 1).pcsmanual_2016.pd

National Healthcare Safety Network (NHSN) patient safety component manual

The NHSN is a secure, Internet-based surveillance system that expands and integrates patient and healthcare personnel safety surveillance systems managed by the Division of Healthcare Quality Promotion (DHQP) at the Centers for Disease Control and Prevention. In addition, facilities that participate in certain reporting programs operated by the Centers for Medicare and Medicaid Services (CMS) can do so through use of NHSN. Furthermore, some U.S. states use NHSN as a means for healthcare facilities to submit data on healthcare-associated infections (HAIs) mandated through their specific state legislation.NHSN enables healthcare facilities to collect and use data about HAIs, adherence to clinical practices known to prevent HAIs, the incidence or prevalence of multidrug-resistant organisms within their organizations, trends and coverage of healthcare personnel safety and vaccination, and adverse events related to the transfusion of blood and blood products.The NHSN includes five components: Patient Safety, Healthcare Personnel Safety, Biovigilance, Dialysis, and Long-term Care Facility (Figure 1).pcsmanual_current.pdfChapter 1: National Healthcare Safety Network (NHSN) Overview -- Chapter 2: Identifying Healthcare-associated Infections (HAI) for NHSN Surveillance -- Chapter 3: Patient Safety Monthly Reporting Plan and Annual Surveys -- Chapter 4: Bloodstream Infection Event (Central Line-Associated Bloodstream Infection and non-central line-associated Bloodstream Infection) -- Chapter 5: Central Line Insertion Practices (CLIP) Adherence Monitoring -- Chapter 6: Pneumonia (Ventilator-associated [VAP] and non-ventilator-associated Pneumonia [PNEU]) Event -- Chapter 7: Urinary Tract Infection (Catheter-Associated Urinary Tract Infection [CAUTI] and non-catheter-associated Urinary Tract Infection [UTI]) and Other Urinary System Infection (USI) Events -- Chapter 9: Surgical Site Infection (SSI) Event -- Chapter 10: Ventilator-Associated Event (VAE) -- Chapter 12: Multidrug-Resistant Organism & Clostridium difficile Infection (MDRO/CDI) Module -- Chapter 15: CDC Locations and Descriptions and Instructions for Mapping Patient Care Locations -- Chapter 16: General Key terms -- Chapter 17: CDC/NHSN Surveillance Definitions for Specific Types of Infections