Identifying Severe Sepsis via Electronic Surveillance

Brandt, Bristol; Gartner, Amanda; Moncure, Michael; Cannon, Chad; Carlton, Elizabeth; Cleek, Carol; Wittkopp, Chris; Simpson, Steven Q.

doi:10.1177/1062860614541291

Cited by 23 publications

(18 citation statements)

References 17 publications

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“…For example, in a prospective pilot study, Buck 39 noticed that 40% of the patients identified by the alert received escalated care in the form of repeated evaluation by a physician, additional medications or intravenous fluids, laboratory tests, respiratory support, or transfer to the ICU. Additionally, in the pilot study mentioned earlier, Brandt et al 36 showed that the alert resulted in a diagnosis of sepsis approximately 27 minutes earlier when compared with the time of sepsis diagnosis based on chart review. In a before and after interventional study, Kurczewski et al 40 evaluated a tool that used at least two SIRS criteria to trigger the sepsis alert but modified it so that one of them had to include an abnormal WBC count or temperature.…”

Section: Sirs-based Screening Toolsmentioning

confidence: 97%

“…35 Several studies have investigated the diagnostic accuracy of SIRS-based screening tools (Table 2). In a prospective pilot study, Brandt et al 36 required the presence of infection, organ dysfunction, or altered mental status in the patient's active problem list prior to allowing an automated system to search for SIRS criteria. The alert was issued to a sepsis surveillance group consisting of an intensivist and critical care nurse, who performed a chart review to determine if the primary team should be notified.…”

Section: Sirs-based Screening Toolsmentioning

confidence: 99%

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Identifying Patients With Sepsis on the Hospital Wards

Bhattacharjee

Edelson

Churpek

2017

Chest

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Sepsis contributes to up to half of all deaths in hospitalized patients, and early interventions, such as appropriate antibiotics, have been shown to improve outcomes. Most research has focused on early identification and treatment of patients with sepsis in the ED and the ICU; however, many patients acquire sepsis on the general wards. The goal of this review is to discuss recent advances in the detection of sepsis in patients on the hospital wards. We discuss data highlighting the benefits and limitations of the systemic inflammatory response syndrome (SIRS) criteria for screening patients with sepsis, such as its low specificity, as well as newly described scoring systems, including the proposed role of the quick sepsis-related organ failure assessment (qSOFA) score. Challenges specific to detecting sepsis on the wards are discussed, and future directions that use big data approaches and automated alert systems are highlighted.

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Section: Sirs-based Screening Toolsmentioning

confidence: 97%

Section: Sirs-based Screening Toolsmentioning

confidence: 99%

Identifying Patients With Sepsis on the Hospital Wards

Bhattacharjee

Edelson

Churpek

2017

Chest

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“…Previous studies have shown that ICD-9 diagnosis codes for severe sepsis and septic shock provide a lower sensitivity for detection of such conditions (22,23,24). In one study, 48.4% of patients meeting clinical criteria for severe sepsis were not assigned a diagnosis code for severe sepsis (22).…”

Section: Limitationsmentioning

confidence: 99%

“…In one study, 48.4% of patients meeting clinical criteria for severe sepsis were not assigned a diagnosis code for severe sepsis (22). Also, patients identified with a diagnosis code tend to be septic patients with more severe illness (23,24).…”

Section: Limitationsmentioning

confidence: 99%

Increased Time to Initial Antimicrobial Administration Is Associated With Progression to Septic Shock in Severe Sepsis Patients

Whiles

Deis

Simpson

2017

Critical Care Medicine

Self Cite

149

101

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Objective To determine if time to initial antimicrobial is associated with progression of severe sepsis to septic shock. Design Retrospective cohort Setting 656 bed urban academic medical center Patients Emergency department patients ≥18 years of age with severe sepsis and/or septic shock and antimicrobial administration within 24 hours. Patients with shock on presentation were excluded. Interventions N/A. Measurements and Main Results We identified 3,929 severe sepsis patients, with overall mortality 12.8%. 984 (25.0%) patients progressed to septic shock. The median time to antimicrobial was 3.77 hours (IQR = 1.96 – 6.42) in those who progressed vs 2.76 hours (IQR = 1.60 – 4.82) in those who did not (p < 0.001). Multivariate logistic regression demonstrated that male sex (OR = 1.18; 95% CI, 1.01–1.36), Charlson Comorbidity Index (OR = 1.18; 95% CI, 1.11–1.27), number of infections (OR = 1.05; 95% CI, 1.02–1.08), and time to first antimicrobial (OR = 1.08; 95% CI, 1.06–1.10) were associated with progression. Each hour until initial antimicrobial administration was associated with a 8.0% increase in progression to septic shock. Additionally, time to broad spectrum antimicrobial was associated with progression (OR = 1.06; 95% CI, 1.05–1.08). Time to initial antimicrobial was also associated with in-hospital mortality (OR = 1.05; 95% CI, 1.03–1.07). Conclusions This study emphasizes the importance of early, broad spectrum antimicrobial administration in severe sepsis patients admitted through the emergency department, as longer time to initial antimicrobial administration is associated with increased progression of severe sepsis to septic shock and increased mortality.

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“…Automated systems for early diagnosis of sepsis using clinical-and laboratory-based data have been widely studied in the literature [7]- [11]. Through development of electronic health records and electronic surveillance systems for healthcare systems, a number of studies have evaluated models for automated sepsis detection and their effectiveness [7], [8], [10], [12], [13]. The studies mostly proposed automated systems of evaluating clinical data for prediction of sepsis using different machine learning algorithms such as support vector machine, k-nearest neighbor, decision trees, regression trees, random forests, logistic regression and lazy Bayesian rules [7], [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees

Du¹,

Sadr²,

Chazal³

2019

2019 Computing in Cardiology Conference (CinC)

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In this study, we developed an automatic algorithm that predicts onset of sepsis using hourly clinical data from patients in an ICU setting. We participated as team "Sepsyd" in the PhysioNet/Computing in Cardiology 2019 Challenge and were ranked 2nd with an official final test score of 0.345. Our developed system processed all the clinical input variables provided in the Challenge. We first applied a preprocessing step that applied a log transform to selected variables and imputed missing values of the variables. After preprocessing, a feature set was formed including the 40 preprocessed variables, 34 missing value flags, the changes in the time series in the vital signs variables and the variance of the vital signs variables. Following this, the features of the present hour were combined with the features of the past 5 to 8 hours of data. These combined features were then processed with a gradient boosting tree classifier to estimate the likelihood of a positive sepsis classification at each time step. We compared the utility score of a number of different system configurations using 3-fold cross validation on the training data. Our best system, assessed on the test set, used a maximum tree depth of 4, a look back of 5 hours, and processed the clinical input variables combined with the missing value flags.

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Identifying Severe Sepsis via Electronic Surveillance

Cited by 23 publications

References 17 publications

Identifying Patients With Sepsis on the Hospital Wards

Identifying Patients With Sepsis on the Hospital Wards

Increased Time to Initial Antimicrobial Administration Is Associated With Progression to Septic Shock in Severe Sepsis Patients

Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees

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