Descriptors of Sepsis Using the Sepsis-3 Criteria: A Cohort Study in Critical Care Units Within the U.K. National Institute for Health Research Critical Care Health Informatics Collaborative*

Shah, Anoop D; MacCallum, Niall; Harris, Steve; Brealey, David; Palmer, Edward; Hetherington, James; Shi, Sinan; Pérez–Suárez, David; Ercole, Ari; Watkinson, Peter; Jones, Andrew; Ashworth, Simon; Beale, Richard; Brett, Stephen; Singer, Mervyn

doi:10.1097/ccm.0000000000005169

Cited by 22 publications

(44 citation statements)

References 33 publications

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“…Many advocate the use of clinical data to define the epidemiology of sepsis. 18 , 22 , 23 , 24 In a study of data from 7 health systems, Rhee et al 24 identified sepsis hospitalizations using clinical data such as blood cultures, antibiotic and vasopressor administration, mechanical ventilation, and laboratory test measures. The incidence of sepsis in the Rhee et al 24 study was 60 per 1000 hospitalizations; although lower than the Angus estimate and higher than the explicit sepsis estimate, this figure is very similar to the Martin et al 6 and SEP‐1 estimates observed in our study.…”

Section: Discussionmentioning

confidence: 99%

Updated estimates of sepsis hospitalizations at United States academic medical centers

et al. 2022

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Objective Sepsis is a major public health problem. Understanding the epidemiology of sepsis subtypes is important to quantify the magnitude of the problem and identify targets for system wide treatment strategies. We sought to describe the current national epidemiology of community‐acquired (CAS), hospital‐acquired (HAS) and healthcare‐associated sepsis (HCAS) hospitalizations among academic medical centers in the United States using current discharge diagnosis taxonomies. Methods Retrospective analysis of patient discharge data from the Vizient Clinical Data Base/Resource Manager. We identified sepsis hospitalizations using four ICD‐10 coding strategies: (1) “Martin” sepsis codes (21 ICD‐10 codes), (2) “Angus” sepsis codes (ICD‐10 infection + ICD‐10 organ dysfunction), (3) Medicare “SEP‐1” codes (28 ICD‐10 codes), and (4) “explicit sepsis” codes (ICD‐10 R65.20 and R65.21). Using present‐on‐admission flags for each diagnosis, we also distinguished: (1) community‐acquired sepsis (CAS), (2) hospital‐acquired sepsis (HAS), and (3) healthcare associated sepsis (HCAS). Results Among 22,655,240 hospitalizations, the number and incidence of sepsis hospitalizations were: (1) Martin (n = 1,718,257, 75.8 per 1000 hospitalizations), (2) Angus (n = 2,749,163, 121.3 per 1000), (3) SEP‐1 (n = 1,624,909, 71.7 per 1000), and (4) explicit sepsis (n = 655,853, 28.9 per 1000). CAS was the most common sepsis subtype. HAS exhibited higher adjusted mortality than CAS. ICU admission was highest for HAS (Martin, 1.5%; Angus, 1.5%; SEP‐1, 1.6%; Explicit, 1.9%). Conclusions These results illustrate the prevalence of sepsis at US academic medical centers using the most current sepsis classification taxonomies and discharge diagnosis codes. These results highlight important considerations when using hospital discharge data to characterize the epidemiology of sepsis.

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Section: Discussionmentioning

confidence: 99%

Updated estimates of sepsis hospitalizations at United States academic medical centers

et al. 2022

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“…However, at least three missing SOFA components resulted in discarding any identification of sepsis or not for that admission/day. Following [3], we define infection by an increase in the maximum rank of any antibiotics administered (or the number of antibiotics of maximum rank), according to the classification proposed by [14], where at least one antibiotic was given intravenously. This operationalisation of Sepsis-3 with a suspected infection identified only by antibiotic escalation is reliant on clinical judgement rather than a confirmed infection, which is a limitation of this approach.…”

Section: Implementation Sepsis-3 In Amsterdam Umc Databasementioning

confidence: 99%

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation

et al. 2022

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Sepsis is a major healthcare problem with substantial mortality and a common reason for admission to the intensive care unit (ICU). For this reason, the management of sepsis is an important area of ICU research. A number of large-scale, freely-accessible ICU databases are available for observational research and the robust identification of septic patients in such data sets is crucial for research purposes, particularly for comparative studies between critical care sub-populations which may vary around the world. However, data structures are poorly standardised due to inevitable variances in clinical electronic health record system vendor and implementation as well as research database design choices. Robust and well-documented cohort selection (such as patients with sepsis) is crucial for reproducible research. In this work, we operationalise the Sepsis-3 definition on the AmsterdamUMCdb, a recently published large European ICU database, publishing open-access code for wider use by critical care researchers. Subjects Human and Biomedical Sciences, Medical Informatics, Physiology STATEMENT OF NEEDSepsis is defined as life-threatening organ dysfunction resulting from a dysregulated host response to infection [1] and is a primary cause of critical illness and mortality. Early identification and treatment, which may include complex organ-support on the intensive care unit (ICU), is crucial for survival [2]. Historically, it has been been difficult to quantify both its incidence and mortality rate within the ICU, as it is a heterogeneous syndrome characterised by a wide-ranging infectious agent, infection site, treatment history and host response. Multiple organ dysfunction in septic patients follows from post-infection dysregulation in the immunology, biochemistry and physiology of the patient, and this in turn leads to morbidity and mortality. The third consensus definition of sepsis, by the Sepsis-3 Task Force [1], recommended a revised definition better aligned to this concept, explicitly incorporating mortality in order to ameliorate previous limitations and to allow for greater consistency in operationalising the definition criteria across different centres [3]. The Sepsis-3 clinical criteria are an acute increase in Sequential Organ Failure Assessment (SOFA) [4] of at least 2 points, accompanying a suspected or documented infection, with the criteria for septic shock further requiring both use of vasopressors and a lactate level >2 mmol/L.

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“…The study by Shah et al (13) provides further evidence that Sepsis-3 criteria can be successfully operationalized using EHR data, including in multicenter settings. The promise of this approach is clear: with the growing national and international adoption of EHR systems, consistent electronic application of Sepsis-3 criteria could generate standardized and risk-adjusted estimates of sepsis incidence, outcomes, and trends.…”

mentioning

confidence: 97%

“…Underscoring this challenge, a recent medical record review-based study demonstrated wide variability in the sensitivity and specificity of commonly used EHR-based definitions of infection (14). Notably, the study by Shah et al (13) used a novel definition based on new antibiotics or antibiotic escalation alone, without the concurrent requirement for obtaining blood or clinical cultures that most prior studies have used. This is an approach optimized for sensitivity but may come at the cost of specificity given the proclivity of many providers to err on the side of treating empirically when the diagnosis of infection is possible but unconfirmed, particularly in critically ill patients (15).…”

mentioning

confidence: 99%

“…Ultimately, however, the study by Shah et al (13) represents another important step away from flawed sepsis surveillance based on administrative data and toward more objective and accurate surveillance using EHR data. Ongoing refinement, validation, and standardization of this approach may soon enable better local, national, and international estimates of the burden of sepsis.…”

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confidence: 99%

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Conducting Sepsis Surveillance by Applying Sepsis-3 Criteria to Electronic Health Record Data: Promises and Potential Pitfalls*

Rhee

2021

Critical Care Medicine

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Descriptors of Sepsis Using the Sepsis-3 Criteria: A Cohort Study in Critical Care Units Within the U.K. National Institute for Health Research Critical Care Health Informatics Collaborative*

Abstract: Supplemental Digital Content is available in the text.

Cited by 22 publications

References 33 publications

Updated estimates of sepsis hospitalizations at United States academic medical centers

Updated estimates of sepsis hospitalizations at United States academic medical centers

Sepsis-3 criteria in AmsterdamUMCdb: open-source code implementation

Conducting Sepsis Surveillance by Applying Sepsis-3 Criteria to Electronic Health Record Data: Promises and Potential Pitfalls*

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