Sepsis Early Alert Tool: Early recognition and timely management in the emergency department

Order Set Usage is Associated With Lower Hospital Mortality in Patients With Sepsis

The Surviving Sepsis Campaign recommends standard operating procedures for patients with sepsis. Real-world evidence about sepsis order set implementation is limited.

OBJECTIVES

To estimate the effect of sepsis order set usage on hospital mortality.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

Fifty-four acute care hospitals in the United States from December 1, 2020 to November 30, 2022 involving 104,662 patients hospitalized for sepsis.

MAIN OUTCOMES AND MEASURES

Hospital mortality.

RESULTS

The sepsis order set was used in 58,091 (55.5%) patients with sepsis. Initial mean sequential organ failure assessment score was 0.3 lower in patients for whom the order set was used than in those for whom it was not used (2.9 sd [2.8] vs 3.2 [3.1], p < 0.01). In bivariate analysis, hospital mortality was 6.3% lower in patients for whom the sepsis order set was used (9.7% vs 16.0%, p < 0.01), median time from emergency department triage to antibiotics was 54 minutes less (125 interquartile range [IQR, 68-221] vs 179 [98-379], p < 0.01), and median total time hypotensive was 2.1 hours less (5.5 IQR [2.0-15.0] vs 7.6 [2.5-21.8], p < 0.01) and septic shock was 3.2% less common (22.0% vs 25.4%, p < 0.01). Order set use was associated with 1.1 fewer median days of hospitalization (4.9 [2.8-9.0] vs 6.0 [3.2-12.1], p < 0.01), and 6.6% more patients discharged to home (61.4% vs 54.8%, p < 0.01). In the multivariable model, sepsis order set use was independently associated with lower hospital mortality (odds ratio 0.70; 95% CI, 0.66-0.73).

CONCLUSIONS AND RELEVANCE

In a cohort of patients hospitalized with sepsis, order set use was independently associated with lower hospital mortality. Order sets can impact large-scale quality improvement efforts.

Improving Initial Sepsis Management Through a Nurse-Driven Rapid Response Team Protocol

BACKGROUND

Rapid identification and timely management of sepsis improve survival. Therefore, a bundled approach to care is recommended.

LOCAL PROBLEM

In an acute care area of the study institution, a 2016 internal evaluation of 27 patients with sepsis showed a median time to first-dose antibiotic administration of 269 minutes, with no patients receiving antibiotics within the 60-minute target time. Additionally, only one-third of patients received appropriate fluid resuscitation (30-mL/kg bolus of intravenous crystalloids). Given poor bundle compliance, a nurse-driven rapid response team protocol for suspected sepsis was implemented. The purpose of this project was to assess the protocol's impact on the timeliness of treatment for sepsis.

METHODS

This retrospective quality improvement evaluation involved patients aged 18 years or older for whom the suspected sepsis protocol was initiated during their acute care area admission. The evaluation focused on improvements in time to intravenous antibiotic administration and volume of fluid resuscitation compared with before protocol implementation. The protocol empowers the rapid response team to initiate sepsis management and includes pertinent laboratory tests, blood cultures, intravenous broad-spectrum antibiotic administration, and a crystalloid bolus (30 mL/kg) if indicated.

RESULTS

A total of 32 patients were evaluated. Time to first-dose antibiotic administration was reduced by half (from 269 to 135 minutes). Eighteen patients met criteria for fluid resuscitation, with twice as many receiving appropriate fluid volumes compared with before protocol implementation.

CONCLUSION

Implementation of the suspected sepsis protocol demonstrates the substantial role nurses have in optimizing patient care, especially in the timely treatment of sepsis.

Effectiveness of automated alerting system compared to usual care for the management of sepsis

There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.

Early detection of sepsis using artificial intelligence: a scoping review protocol

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.

METHODS

The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O'Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.

ETHICS AND DISSEMINATION

The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

NEWS2 is Superior to qSOFA in Detecting Sepsis with Organ Dysfunction in the Emergency Department

Early administration of antibiotics is associated with better survival in sepsis, thus screening and early detection for sepsis is of clinical importance. Current risk stratification scores used for bedside detection of sepsis, for example Quick Sequential Organ Failure Assessment (qSOFA) and National Early Warning Score 2 (NEWS2), are primarily validated for death and intensive care. The primary aim of this study was to compare the diagnostic accuracy of qSOFA and NEWS2 for a composite outcome of sepsis with organ dysfunction, infection-related mortality within <72 h, or intensive care due to an infection. Retrospective analysis of data from two prospective, observational, multicentre, convenience trials of sepsis biomarkers at emergency departments were performed. Cohort A consisted of 526 patients with a diagnosed infection, 288 with the composite outcome. Cohort B consisted of 645 patients, of whom 269 had a diagnosed infection and 191 experienced the composite outcome. In Cohort A and B, NEWS2 had significantly higher area under receiver operating characteristic curve (AUC), 0.80 (95% CI 0.75-0.83) and 0.70 (95% CI 0.65-0.74), than qSOFA, AUC 0.70 (95% CI 0.66-0.75) and 0.62 (95% CI 0.57-0.67) p < 0.01 and, p = 0.02, respectively for the composite outcome. NEWS2 was superior to qSOFA for screening for sepsis with organ dysfunction, infection-related mortality or intensive care due to an infection both among infected patients and among undifferentiated patients at emergency departments.

Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

IMPORTANCE

While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.

OBJECTIVES

To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.

DESIGN, SETTING, AND PARTICIPANTS

Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.

MAIN OUTCOMES AND MEASURES

The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.

RESULTS

Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.

CONCLUSIONS AND RELEVANCE

Machine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.

Impact of Electronic Physician Order-Set on Antibiotic Ordering Time in Septic Patients in the Emergency Department

BACKGROUND

Sepsis is a serious medical condition that can lead to organ dysfunction and death. Research shows that each hour delay in antibiotic administration increases mortality. The Surviving Sepsis Campaign Bundles created standards to assist in the timely treatment of patients with suspected sepsis to improve outcomes and reduce mortality.

OBJECTIVE

This article determines if the use of an electronic physician order-set decreases time to antibiotic ordering for patients with sepsis in the emergency department (ED).

METHODS

A retrospective chart review was performed on adult patients who presented to the ED of four community hospitals from May to July 2016. Patients with severe sepsis and/or septic shock were included. Primary outcome was the difference in time to antibiotic ordering in patients whose physicians utilized the order-set versus those whose physicians did not. Secondary outcomes included differences in time to antibiotic administration, time to lactate test, hospital length of stay, and posthospitalization disposition. The institution's Quality Improvement Committee approved the project.

RESULTS

Forty-five of 123 patients (36.6%) with sepsis had physicians who used the order-set. Order-set utilization reduced the mean time to ordering antibiotics by 20 minutes (99 minutes, 95% confidence interval [CI]: 69-128 vs. 119 minutes, 95% CI: 91-147), but this finding was not statistically significant. Mean time to antibiotic administration (145 minutes, 95% CI: 108-181 vs. 182 minutes, 95% CI: 125-239) and median time to lactate tests (12 minutes, 95% CI: 0-20 vs. 19 minutes, 95% CI: 8-34), although in the direction of the hypotheses, were not significantly different.

CONCLUSION

Utilization of the order-set was associated with a potentially clinically significant, but not statistically significant, reduced time to antibiotic ordering in patients with sepsis. Electronic order-sets are a promising tool to assist hospitals with meeting the Centers for Medicare and Medicaid Services core measure.

Analysis of a new best-practice advisory on time to initiation of antibiotics in surgical intensive care unit patients with septic shock

Background

Early administration of antibiotics in septic shock is associated with decreased mortality. Promptly identifying sepsis and eliciting a response are necessary to reduce time to antibiotic administration.

Methods

A best-practice advisory was introduced in the surgical intensive care unit to identify patients with septic shock and promote timely action. The best-practice advisory is triggered by blood culture orders and vasopressor administration within 24 h. The nurse or provider who triggers the alert may send an automatic notification to the intensive care unit resident, clinical pharmacist, and charge nurse, prompting bedside response and closer evaluation. Patients who met best-practice advisory criteria in the surgical intensive care unit from May 2016 through March 2017 were included. Outcomes included changes in antibiotics within 24 h, response to best-practice advisory, and time-to-antibiotics. Time-to-antibiotics was compared between a retrospective pre-intervention period and a six-month prospective post-intervention period defined by launch of the new best-practice advisory in September 2016. Data were analyzed by chi square, Mann-Whitney U, and Kruskal-Wallis.

Results

During the first six months of best-practice advisory implementation, 191 alerts were triggered by 97 unique patients. Alert notification was transmitted in 79 best-practice advisories (41%), with pharmacist bedside response in 53 (67%). New antibiotics were started within 24 h following 83 best-practice advisories (43%). There was a trend toward decreased time-to-antibiotics following implementation of the best-practice advisory (7.4 vs. 4.2 h, p = 0.057). Compared to the entire cohort, time-to-antibiotics was shorter when the team was notified and when a pharmacist responded to the bedside (4.2 vs. 1.6 vs. 1.2 hours).

Conclusions

A new best-practice advisory has been effective at eliciting a rapid response and reducing the time-to-antibiotics in surgical intensive care unit patients with septic shock. Team notification and pharmacist response are associated with decreased time-to-antibiotics.

Multidisciplinary analysis of invasive meningococcal disease as a framework for continuous quality and safety improvement in regional Australia

Background

System factors in a regional Australian health district contributed to avoidable care deviations from invasive meningococcal disease (IMD) management guidelines. Traditional root cause analysis (RCA) is not well-suited to IMD, focusing on individual cases rather than system improvements. As IMD requires complex care across healthcare silos, it presents an opportunity to explore and address system-based patient safety issues.

Context

Baseline assessment of IMD cases (2005–2006) identified inadequate triage, lack of senior clinician review, inconsistent vital sign recording and laboratory delays as common issues, resulting in antibiotic administration delays and inappropriate or premature discharge.

Methods

Clinical governance, in partnership with clinical and public health services, established a multidisciplinary Meningococcal Reference Group (MRG) to routinely review management of all IMD cases. The MRG comprised representatives from primary care, acute care, public health, laboratory medicine and clinical governance. Baseline data were compared with two subsequent evaluation points (2011–2012 and 2013–2015).

Interventions

Phase I involved multidisciplinary process mapping and development of a standardised audit tool from national IMD management guidelines. Phase II involved formalisation of group processes and advocacy for operational change. Phase III focused on dissemination of findings to clinicians and managers.

Results

Greatest care improvements were observed in the final evaluation. Median antibiotic delay decreased from 72 to 42 min and proportion of cases triaged appropriately improved from 38% to 75% between 2013 and 2015. Increasing fatal outcomes were attributed to the emergence of more virulent meningococcal serotypes.

Conclusions

The MRG was a key mechanism for identifying system gaps, advocating for change and enhancing communication and coordination across services. Employing IMD case review as a focus for district-level process reflection presents an innovative patient safety approach, combining the strengths of prospective hazard analysis with more traditional RCA methodologies.

Evaluation of a model to improve collection of blood cultures in patients with sepsis in the emergency room

Sepsis begins outside of the hospital for nearly 80% of patients and the emergency room (ER) represents the first contact with the health care system. This study evaluates a project to improve collection of blood cultures (BCs) in patients with sepsis in the ER consisting of staff education and completion of the appropriate BC pre-analytical phase. A retrospective observational study performed to analyse the data on BC collection in the ER before and after a three-phase project. The first phase (1 January to 30 June 2015) before the intervention consisted of evaluation of data on BCs routinely collected in the ER. The second phase (1 July to 31 December 2015) was the intervention phase in which educational courses on sepsis recognition and on pre-analytical phase procedures (including direct incubation) were provided to ER staff. The third phase (1 January to 30 June 2016; after the intervention) again consisted of evaluation. Before the intervention, out of 24,738 admissions to the ER, 103 patients (0.4%) were identified as septic and had BCs drawn (359 BC bottles); 19 out of 103 patients (18.4%) had positive BCs. After the intervention, out of 24,702 admissions, 313 patients (1.3%) had BCs drawn (1,242 bottles); of these, 96 (30.7%) had positive BCs. Comparing the first and third periods, an increase in the percentage of patients with BCs collected (from 0.4% to 1.3% respectively, p < 0.0001) and an increase in the percentages of patients with true-positive BCs (from 0.08% to 0.39% of all patients evaluated respectively, p < 0.0001) were observed. The isolation of bacteria by BCs increased 3.25-fold after project implementation. These results can be principally ascribed to an improved awareness of sepsis in the staff associated with improved pre-analytical phase procedures in BC collection.