Implementation of a multicenter performance improvement program for early detection and treatment of severe sepsis in general medical–surgical wards

Schorr, Christa; Odden, Andrew; Evans, Laura; Escobar, Gabriel J.; Gandhi, Shivani; Townsend, Sean R.; Levy, Mitchell M.

doi:10.1002/jhm.2656

Cited by 36 publications

(40 citation statements)

References 13 publications

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“…In this issue of the Journal of Hospital Medicine, we examine early detection from various perspectives but around a common theme that usually gets less attention in the academic literature: implementation. The article by Schorr et al 23 describes a disease-specific approach that can be instantiated using either electronic or paper tools. Escobar et al 24 describe the quantitative as well as the electronic architecture of an early warning system (EWS) pilot at 2 hospitals that are part of an integrated healthcare delivery system.…”

Section: Articles In This Issuementioning

confidence: 99%

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Escobar

Dellinger

2016

Journal of Hospital Medicine

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Patients who deteriorate outside the intensive care unit (ICU) are known to have elevated mortality and morbidity. Rapid response teams (RRTs) were developed to address such deterioration. It has not been possible to establish that RRTs employing manual detection methods have definitively improved hospital outcomes. Because of this, automated early detection systems based on data from modern electronic medical records have been developed. This article attempts to establish a conceptual framework for early detection, prevention, and mitigation of critical illness in hospitalized patients outside the ICU. Taking a step back from the now voluminous clinical and statistical literature on early warning systems, this article focuses on definitional issues (What is early detection? What does it aim to detect, and how?). Particular attention is given to how early detection systems interface with patients who are near the end of life. Also addressed are problems of how one quantifies benefit, which includes consideration of downsides such as potential harms of early detection. Journal of Hospital Medicine 2016;11:S5–S10. © 2016 Society of Hospital Medicine

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Section: Articles In This Issuementioning

confidence: 99%

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Escobar

Dellinger

2016

Journal of Hospital Medicine

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“…Quality improvement programs implemented at individual health systems have improved outcomes for patients with sepsis. 9 , 10 However, overall compliance with recommended treatment remains poor. Deep learning is a suite of novel machine learning methods that have achieved performance on many challenging tasks.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for early detection of sepsis: an internal and temporal validation study

et al. 2020

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Objective Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice. Materials and Methods We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP–RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed. Results The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP–RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP–RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP–RNN outperform all 7 clinical risk score and machine learning comparisons. Conclusions We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

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“…It has been widely demonstrated that management and clinical outcomes in patients presenting bacteraemia improve when infectious diseases specialists participate 15. Furthermore, interventions on patients with sepsis have also been shown to be effective in terms of clinical results 16. However, no literature can be found focused specifically on ASP interventions in patients with negative BCx.…”

Section: Discussionmentioning

confidence: 99%

Opportunities for antibiotic optimisation and outcome improvement in patients with negative blood cultures: study protocol for a cluster-randomised crossover trial, the NO-BACT study

et al. 2019

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IntroductionPatients with negative blood cultures (BCx) represent 85%–90% of all patients with BCx taken during hospital admission. This population usually includes a heterogeneous group of patients admitted with infectious diseases or febrile syndromes that require a blood culture. There is very little evidence of the clinical characteristics and antibiotic treatment given to these patients.Methods and analysisIn a preliminary exploratory prospective cohort study of patients with BCx taken, the clinical/therapeutic characteristics and outcomes/antimicrobial stewardship opportunities of a population of patients with negative BCx will be analysed. In the second phase, using a cluster randomised crossover design, the implementation of an antimicrobial stewardship intervention targeting patients with negative BCx will be evaluated in terms of quality of antimicrobial use (duration and de-escalation), length of hospital stay and mortality.Ethics and disseminationThis study has been and registered with clinicaltrials.gov. The findings of our study may support the implementation in clinical practice of an antimicrobial stewardship intervention to optimise the use of antibiotics in patients with negative BCx. The results of this study will be published in peer-reviewed journals and disseminated at national and international conferences.Trial registration number NCT03535324.

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Implementation of a multicenter performance improvement program for early detection and treatment of severe sepsis in general medical–surgical wards

Cited by 36 publications

References 13 publications

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Early detection, prevention, and mitigation of critical illness outside intensive care settings

Machine learning for early detection of sepsis: an internal and temporal validation study

Opportunities for antibiotic optimisation and outcome improvement in patients with negative blood cultures: study protocol for a cluster-randomised crossover trial, the NO-BACT study

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