Three-Hour Bundle Compliance and Outcomes in Patients With Undiagnosed Severe Sepsis

Deis, Amanda S.; Whiles, Bristol B.; Brown, Alexandra R.; Satterwhite, Catherine Lindsey; Simpson, Steven Q.

doi:10.1016/j.chest.2017.09.031

Cited by 25 publications

(25 citation statements)

References 16 publications

(20 reference statements)

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“…A recent study has shown that nearly half of patients with severe sepsis (which is similar to the more recent definition of sepsis evaluated in this study), lack sepsis-specific International Classification of Disease (ICD) codes. This study additionally showed that patients with sepsis-specific ICD codes were more likely to receive appropriate sepsis treatment [29]. The significant advance lookahead time provided by this MLA allows for improved risk-stratification of patients, with increased monitoring or more frequent care team assessment for patients with high risk scores.…”

Section: Discussionmentioning

confidence: 73%

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Barton

Chettipally

Zhou

et al. 2019

Computers in Biology and Medicine

115

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Objective: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods. Materials and Methods: Retrospective data was drawn from databases at the University of California, San Francisco (UCSF) Medical Center and the Beth Israel Deaconess Medical Center (BIDMC). Adult patient encounters without sepsis on admission, and with at least one recording of each of six vital signs (SpO 2 , heart rate, respiratory rate, temperature, systolic and diastolic blood pressure) were included. We compared the performance of the machine learning algorithm (MLA) to that of commonly used scoring systems. Area under the receiver operating characteristic (AUROC) curve was our primary measure of accuracy. MLA performance was measured at sepsis onset and 24, and 48 hours prior sepsis onset. Results: The MLA achieved AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 hours prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89.

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Section: Discussionmentioning

confidence: 73%

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Barton

Chettipally

Zhou

et al. 2019

Computers in Biology and Medicine

115

View full text Add to dashboard Cite

show abstract

“…Next, we must consider whether this relationship between time to antibiotic and mortality causative or only a correlation, given that all other components of the sepsis care bundle may be potentially physiologically effective along with time to antibiotic and that the effectiveness of each component remains controversial in various settings [10, 20, 21]. Adherence to the sepsis care bundle also varies in each setting [8, 10, 22, 23]. For example, in a study among patients who received antibiotics as they met the criteria for severe sepsis, a relatively high compliance rate for the sepsis care bundle was reported if sepsis was definitively diagnosed (with diagnosis code) compared with undiagnosed sepsis (without diagnosis code) [22].…”

Section: Discussionmentioning

confidence: 99%

Implementation of earlier antibiotic administration in patients with severe sepsis and septic shock in Japan: a descriptive analysis of a prospective observational study

Abe

Kushimoto

Tokuda³

et al. 2019

Crit Care

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BackgroundTime to antibiotic administration is a key element in sepsis care; however, it is difficult to implement sepsis care bundles. Additionally, sepsis is different from other emergent conditions including acute coronary syndrome, stroke, or trauma. We aimed to describe the association between time to antibiotic administration and outcomes in patients with severe sepsis and septic shock in Japan.MethodsThis prospective observational study enrolled 1184 adult patients diagnosed with severe sepsis based on the Sepsis-2 criteria and admitted to 59 intensive care units (ICUs) in Japan between January 1, 2016, and March 31, 2017, as the sepsis cohort of the Focused Outcomes Research in Emergency Care in Acute Respiratory Distress Syndrome, Sepsis and Trauma (FORECAST) study. We compared the characteristics and in-hospital mortality of patients administered with antibiotics at varying durations after sepsis recognition, i.e., 0–60, 61–120, 121–180, 181–240, 241–360, and 361–1440 min, and estimated the impact of antibiotic timing on risk-adjusted in-hospital mortality using the generalized estimating equation model (GEE) with an exchangeable, within-group correlation matrix, with “hospital” as the grouping variable.ResultsData from 1124 patients in 54 hospitals were used for analyses. Of these, 30.5% and 73.9% received antibiotics within 1 h and 3 h, respectively. Overall, the median time to antibiotic administration was 102 min [interquartile range (IQR), 55–189]. Compared with patients diagnosed in the emergency department [90 min (IQR, 48–164 min)], time to antibiotic administration was shortest in patients diagnosed in ICUs [60 min (39–180 min)] and longest in patients transferred from wards [120 min (62–226)]. Overall crude mortality was 23.4%, where patients in the 0–60 min group had the highest mortality (28.0%) and a risk-adjusted mortality rate [28.7% (95% CI 23.3–34.1%)], whereas those in the 61–120 min group had the lowest mortality (20.2%) and risk-adjusted mortality rates [21.6% (95% CI 16.5–26.6%)]. Differences in mortality were noted only between the 0–60 min and 61–120 min groups.ConclusionsWe could not find any association between earlier antibiotic administration and reduction in in-hospital mortality in patients with severe sepsis.

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“…Studies cite wide variations in compliance, with some reporting it to be as low as 8.7%. 9,10 Of the 4 performance measures included in the 3-hour bundle, repeated lactate measurement was met least often. 10 As discussed, we are unable to state that a lactate-guided approach is inferior to a peripheral perfusion approach, and it is safe to say it is no better.…”

Section: Discussion Pointsmentioning

confidence: 99%

Lactate-Guided Resuscitation Strategies in Septic Shock: Differentiating the Bad From the Unexceptional

Spiegel

McNaughton

Radecki

2019

Annals of Emergency Medicine

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Three-Hour Bundle Compliance and Outcomes in Patients With Undiagnosed Severe Sepsis

Cited by 25 publications

References 16 publications

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Implementation of earlier antibiotic administration in patients with severe sepsis and septic shock in Japan: a descriptive analysis of a prospective observational study

Lactate-Guided Resuscitation Strategies in Septic Shock: Differentiating the Bad From the Unexceptional

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