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

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

Duration of Treatment for Pseudomonas aeruginosa Bacteremia: a Retrospective Study

Introduction: There is no consensus regarding optimal duration of antibiotic therapy for Pseudomonas aeruginosa bacteremia. We aimed to evaluate the impact of short antibiotic course. Methods: We present a retrospective multicen ter study including patients with P. aeruginosa bacteremia during 2009–2015. We evaluated outcomes of patients treated with short (6–- 10 days) versus long (11–15 days) antibiotic courses. The primary outcome was a composite of 30-day mortality or bacteremia recurrence and/or persistence. Univariate and inverse probability treatment-weighted (IPTW) adjusted multivariate analysis for the primary outcome was performed. To avoid immortal time bias, the landmark method was used. Results: We included 657 patients; 273 received a short antibiotic course and 384 a long course. There was no significant difference in baseline characteristics of patients. The com posite primary outcome occurred in 61/384 patients in the long-treatment group (16%) versus 32/273 in the short-treatment group (12%) (p = 0.131). Mortality accounted for 41/384 (11%) versus 25/273 (9%) of cases, respectively. Length of hospital stay was signif icantly shorter in the short group [median 13 days, interquartile range (IQR) 9–21 days, versus median 15 days, IQR 11–26 days, p = 0.002]. Ten patients in the long group dis continued antibiotic therapy owing to adverse events, compared with none in the short group. On univariate and multivariate analyses, dura tion of therapy was not associated with the primary outcome. Conclusions: In this retrospective study, 6–- 10 days of antibiotic course for P. aeruginosa bacteremia were as effective as longer courses in terms of survival and recurrence. Shorter ther apy was associated with reduced length of stay and less drug discontinuation

PRAISE: providing a roadmap for automated infection surveillance in Europe

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

Peripheral Oxygen Saturation Facilitates Assessment of Respiratory Dysfunction in the Sequential Organ Failure Assessment Score with Implications for the Sepsis-3 Criteria

OBJECTIVES: Sequential Organ Failure Assessment score is the basis of the Sepsis-3 criteria and requires arterial blood gas analysis to assess respiratory function. Peripheral oxygen saturation is a noninvasive alternative but is not included in neither Sequential Organ Failure Assessment score nor Sepsis-3. We aimed to assess the association between worst peripheral oxygen saturation during onset of suspected infection and mortality. DESIGN: Cohort study of hospital admissions from a main cohort and emergency department visits from four external validation cohorts between year 2011 and 2018. Data were collected from electronic health records and prospectively by study investigators. SETTING: Eight academic and community hospitals in Sweden and Canada. PATIENTS: Adult patients with suspected infection episodes. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main cohort included 19,396 episodes (median age, 67.0 [53.0-77.0]; 9,007 [46.4%] women; 1,044 [5.4%] died). The validation cohorts included 10,586 episodes (range of median age, 61.0-76.0; women 42.1-50.2%; mortality 2.3-13.3%). Peripheral oxygen saturation levels 96-95% were not significantly associated with increased mortality in the main or pooled validation cohorts. At peripheral oxygen saturation 94%, the adjusted odds ratio of death was 1.56 (95% CI, 1.10-2.23) in the main cohort and 1.36 (95% CI, 1.00-1.85) in the pooled validation cohorts and increased gradually below this level. Respiratory assessment using peripheral oxygen saturation 94-91% and less than 91% to generate 1 and 2 Sequential Organ Failure Assessment points, respectively, improved the discrimination of the Sequential Organ Failure Assessment score from area under the receiver operating characteristics 0.75 (95% CI, 0.74-0.77) to 0.78 (95% CI, 0.77-0.80; p < 0.001). Peripheral oxygen saturation/Fio2ratio had slightly better predictive performance compared with peripheral oxygen saturation alone, but the clinical impact was minor. CONCLUSIONS: These findings provide evidence for assessing respiratory function with peripheral oxygen saturation in the Sequential Organ Failure Assessment score and the Sepsis-3 criteria. Our data support using peripheral oxygen saturation thresholds 94% and 90% to get 1 and 2 Sequential Organ Failure Assessment respiratory points, respectively. This has important implications primarily for emergency practice, rapid response teams, surveillance, research, and resource-limited settings

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model—SepsisFinder—which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care

Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population : observational study using electronic health records data

Background: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. Methods: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by &gt;2 points) and the likelihood of infection by physician medical record review. Results: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (&gt;48 hour after admission) and related in-hospital mortality varied between wards. Conclusions: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards

Ceftazidime, carbapenems, or piperacillin-tazobactam as single definitive therapy for Pseudomonas aeruginosa bloodstream infection: a multisite retrospective study

BACKGROUND: The optimal antibiotic regimen for Pseudomonas aeruginosa bacteremia is controversial. Although β-lactam monotherapy is common, data to guide the choice between antibiotics are scarce. We aimed to compare ceftazidime, carbapenems, and piperacillin-tazobactam as definitive monotherapy. METHODS: A multinational retrospective study (9 countries, 25 centers) including 767 hospitalized patients with P. aeruginosa bacteremia treated with β-lactam monotherapy during 2009-2015. The primary outcome was 30-day all-cause mortality. Univariate and multivariate, including propensity-adjusted, analyses were conducted introducing monotherapy type as an independent variable. RESULTS: Thirty-day mortality was 37/213 (17.4%), 42/210 (20%), and 55/344 (16%) in the ceftazidime, carbapenem, and piperacillin-tazobactam groups, respectively. Type of monotherapy was not significantly associated with mortality in either univariate, multivariate, or propensity-adjusted analyses (odds ratio [OR], 1.14; 95% confidence interval [CI], 0.52-2.46, for ceftazidime; OR, 1.3; 95% CI, 0.67-2.51, for piperacillin-tazobactam, with carbapenems as reference in propensity adjusted multivariate analysis; 542 patients). No significant difference between antibiotics was demonstrated for clinical failure, microbiological failure, or adverse events. Isolation of P. aeruginosa with new resistance to antipseudomonal drugs was significantly more frequent with carbapenems (36/206 [17.5%]) versus ceftazidime (25/201 [12.4%]) and piperacillin-tazobactam (28/332 [8.4%] (P = .007). CONCLUSIONS: No significant difference in mortality, clinical, and microbiological outcomes or adverse events was demonstrated between ceftazidime, carbapenems, and piperacillin-tazobactam as definitive treatment of P. aeruginosa bacteremia. Higher rates of resistant P. aeruginosa after patients were treated with carbapenems, along with the general preference for carbapenem-sparing regimens, suggests using ceftazidime or piperacillin-tazobactam for treating susceptible infection

Risk factors for mortality among patients with Pseudomonas aeruginosa bacteraemia: a retrospective multicentre study

This study aimed to evaluate risk factors for 30-day mortality among hospitalised patients with Pseudomonas aeruginosa bacteraemia, a highly fatal condition. A retrospective study was conducted between 1 January 2009 and 31 October 2015 in 25 centres (9 countries) including 2396 patients. Univariable and multivariable analyses of risk factors were conducted for the entire cohort and for patients surviving ≥48 h. A propensity score for predictors of appropriate empirical therapy was introduced into the analysis. Of the 2396 patients, 636 (26.5%) died within 30 days. Significant predictors (odds ratio and 95% confidence interval) of mortality in the multivariable analysis included patient-related factors: age (1.02, 1.01–1.03); female sex (1.34, 1.03–1.77); bedridden functional capacity (1.99, 1.24–3.21); recent hospitalisation (1.43, 1.07–1.92); concomitant corticosteroids (1.33, 1.02–1.73); and Charlson comorbidity index (1.05, 1.01–1.93). Infection-related factors were multidrug-resistant Pseudomonas (1.52, 1.15–2.1), non-urinary source (2.44, 1.54–3.85) and Sequential Organ Failure Assessment (SOFA) score (1.27, 1.18–1.36). Inappropriate empirical therapy was not associated with increased mortality (0.81, 0.49–1.33). Among 2135 patients surviving ≥48 h, hospital-acquired infection (1.59, 1.21–2.09), baseline endotracheal tube (1.63, 1.13–2.36) and ICU admission (1.53, 1.02–2.28) were additional risk factors. Risk factors for mortality among patients with P. aeruginosa were mostly irreversible. Early appropriate empirical therapy was not associated with reduced mortality. Further research should be conducted to explore subgroups that may not benefit from broad-spectrum antipseudomonal empirical therapy. Efforts should focus on prevention of infection, mainly hospital-acquired infection and multidrug-resistant pseudomonal infection

Duration of Treatment for Pseudomonas aeruginosa Bacteremia: a Retrospective Study.

There is no consensus regarding optimal duration of antibiotic therapy for Pseudomonas aeruginosa bacteremia. We aimed to evaluate the impact of short antibiotic course. We present a retrospective multicenter study including patients with P. aeruginosa bacteremia during 2009-2015. We evaluated outcomes of patients treated with short (6-10 days) versus long (11-15 days) antibiotic courses. The primary outcome was a composite of 30-day mortality or bacteremia recurrence and/or persistence. Univariate and inverse probability treatment-weighted (IPTW) adjusted multivariate analysis for the primary outcome was performed. To avoid immortal time bias, the landmark method was used. We included 657 patients; 273 received a short antibiotic course and 384 a long course. There was no significant difference in baseline characteristics of patients. The composite primary outcome occurred in 61/384 patients in the long-treatment group (16%) versus 32/273 in the short-treatment group (12%) (p = 0.131). Mortality accounted for 41/384 (11%) versus 25/273 (9%) of cases, respectively. Length of hospital stay was significantly shorter in the short group [median 13 days, interquartile range (IQR) 9-21 days, versus median 15 days, IQR 11-26 days, p = 0.002]. Ten patients in the long group discontinued antibiotic therapy owing to adverse events, compared with none in the short group. On univariate and multivariate analyses, duration of therapy was not associated with the primary outcome. In this retrospective study, 6-10 days of antibiotic course for P. aeruginosa bacteremia were as effective as longer courses in terms of survival and recurrence. Shorter therapy was associated with reduced length of stay and less drug discontinuation