Faculty Opinions recommendation of Comparison of sepsis screening tools' ability to detect sepsis accurately.

Abstract Background There is little evidence of which sepsis screening tool to use in the ambulance setting. The primary aim of the current study was to compare the performance of NEWS2 (National Early Warning score 2) and RETTS (Rapid Emergency Triage and Treatment System) with respect to identification of sepsis among ambulance patients with clinically suspected infection. The secondary aim was to compare the performance of the novel Predict Sepsis screening tools with that of NEWS2, RETTS and clinical judgment. Methods Prospective cohort study of 323 adult ambulance patients with clinically suspected infection, transported to hospitals in Stockholm, during 2017/2018. The sensitivity, specificity, and AUC (Area Under the receiver operating Curve) were calculated and compared by using McNemar´s test and DeLong’s test. Results The prevalence of sepsis in the current study population was 44.6% (144 of 323 patients). No significant difference in AUC was demonstrated between NEWS2 ≥ 5 and RETTS ≥ orange. NEWS2 ≥ 7 demonstrated a significantly greater AUC than RETTS red. The Predict Sepsis screening tools ≥ 2 demonstrated the highest sensitivity (range 0.87–0.91), along with RETTS ≥ orange (0.83), but the lowest specificity (range 0.39–0.49). The AUC of NEWS2 (0.73) and the Predict Sepsis screening tools (range 0.75–0.77) was similar. Conclusions The results indicate that NEWS2 could be the better alternative for sepsis identification in the ambulance, as compared to RETTS. The Predict Sepsis screening tools demonstrated a high sensitivity and AUCs similar to that of NEWS2. However, these results need to be interpreted with caution as the Predict Sepsis screening tools require external validation. Trial registration: ClinicalTrials.gov, NCT03249597. Registered 15 August 2017—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03249597.

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Usefulness of sepsis screening tools and education in recognising the burden of sepsis on hospital wards

Critical Care ◽

10.1186/cc14083 ◽

2015 ◽

Vol 19 (Suppl 1) ◽

pp. P3 ◽

Cited By ~ 1

Author(s):

EJ Galtrey ◽

C Moss ◽

H Cahill

Keyword(s):

Screening Tools ◽

Sepsis Screening ◽

Hospital Wards

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Real World Patterns of Antimicrobial Use and Microbiology Investigations in Patients with Sepsis outside the Critical Care Unit: Secondary Analysis of Three Nation-Wide Point Prevalence Studies

Journal of Clinical Medicine ◽

10.3390/jcm8091337 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1337 ◽

Cited By ~ 5

Author(s):

Maja Kopczynska ◽

Ben Sharif ◽

Harry Unwin ◽

John Lynch ◽

Andrew Forrester ◽

...

Keyword(s):

Healthcare Providers ◽

Significant Proportion ◽

Secondary Analysis ◽

Real Life ◽

Point Prevalence ◽

Screening Tools ◽

Antibiotic Administration ◽

Prevalence Studies ◽

Acute Hospitals ◽

Sepsis Screening

Recent description of the microbiology of sepsis on the wards or information on the real-life antibiotic choices used in sepsis is lacking. There is growing concern of the indiscriminate use of antibiotics and omission of microbiological investigations in the management of septic patients. We performed a secondary analysis of three annual 24-h point-prevalence studies on the general wards across all Welsh acute hospitals in years 2016–2018. Data were collected on patient demographics, as well as radiological, laboratory and microbiological data within 48-h of the study. We screened 19,453 patients over the three 24 h study periods and recruited 1252 patients who fulfilled the entry criteria. 775 (64.9%) patients were treated with intravenous antibiotics. Only in 33.65% (421/1252) of all recruited patients did healthcare providers obtain blood cultures; in 25.64% (321/1252) urine cultures; in 8.63% (108/1252) sputum cultures; in 6.79% (85/1252) wound cultures; in 15.25% (191/1252) other cultures. Out of the recruited patients, 59.1% (740/1252) fulfilled SEPSIS-3 criteria. Patients with SEPSIS-3 criteria were significantly more likely to receive antibiotics than the non-septic cohort (p < 0.0001). In a multivariable regression analysis increase in SOFA score, increased number of SIRS criteria and the use of the official sepsis screening tool were associated with antibiotic administration, however obtaining microbiology cultures was not. Our study shows that antibiotics prescription practice is not accompanied by microbiological investigations. A significant proportion of sepsis patients are still at risk of not receiving appropriate antibiotics treatment and microbiological investigations; this may be improved by a more thorough implementation of sepsis screening tools.

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Point-of-care lactate measurement for suspected sepsis in the prehospital environment: are we missing the point at the sharp end?

British Journal of Healthcare Management ◽

10.12968/bjhc.2019.0071 ◽

2020 ◽

Vol 26 (4) ◽

pp. 1-9

Author(s):

Bryan Lightowler

Keyword(s):

Point Of Care ◽

Vital Signs ◽

Screening Tools ◽

Handheld Devices ◽

Services Research ◽

Suspected Sepsis ◽

Lactate Measurement ◽

Life Threatening ◽

Lactate Levels ◽

Sepsis Screening

Expecting ambulance clinicians to dependably differentiate the life-threatening organ dysfunction caused by sepsis from an inflammatory response to a non-infectious aetiology, relying upon vital signs and a physical examination of the patient alone, must be considered unrealistic. Although lactate measurement has been integrated into numerous prehospital sepsis screening tools, it is not yet measured routinely within UK ambulance services. Research has generally focused on whether handheld point-of-care lactate measurement devices are as accurate as laboratory analysis of venous or arterial samples. The weight of literature has concluded negatively in relation to this. However, there is potential for handheld devices to be used independently to monitor trends in lactate elimination or accumulation to inform decisions on the efficacy of prehospital interventions, or simply to report categorical data in terms of whether lactate levels are elevated or not. This offers UK paramedics the opportunity to improve sepsis care through the enhanced assessment of risk and acuity, the identification of patients with cryptic shock, more aggressive fluid resuscitation and advanced notification to receiving units.

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Comparison of Manual and Automated Sepsis Screening Tools in a Pediatric Emergency Department

PEDIATRICS ◽

10.1542/peds.2020-022590 ◽

2021 ◽

Vol 147 (2) ◽

pp. e2020022590

Author(s):

Matthew Eisenberg ◽

Eli Freiman ◽

Andrew Capraro ◽

Kate Madden ◽

Michael C. Monuteaux ◽

...

Keyword(s):

Emergency Department ◽

Pediatric Emergency ◽

Screening Tools ◽

Pediatric Emergency Department ◽

Sepsis Screening

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Sepsis Screening Tools

The Southwest Respiratory and Critical Care Chronicles ◽

10.12746/swrccc2013.0103.027 ◽

2013 ◽

Vol 1 (3) ◽

pp. 12-18

Author(s):

Amanda Venable

Keyword(s):

Screening Tools ◽

Sepsis Screening

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Sepsis Epidemiology and the Performance of Sepsis Screening Tools in an ED in Belize

Journal of Emergency Medicine ◽

10.1016/j.jemermed.2020.07.070 ◽

2020 ◽

Vol 59 (5) ◽

pp. 768

Author(s):

M. Jaung ◽

S. Gunter ◽

I. Espina ◽

M. Bolgiano ◽

D. Huynh ◽

...

Keyword(s):

Screening Tools ◽

Sepsis Screening

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Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

Journal of Clinical Medicine ◽

10.3390/jcm8111906 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1906 ◽

Cited By ~ 8

Author(s):

Jau-Woei Perng ◽

I-Hsi Kao ◽

Chia-Te Kung ◽

Shih-Chiang Hung ◽

Yi-Horng Lai ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Deep Learning ◽

Mortality Prediction ◽

Screening Tools ◽

Learning Methods ◽

Suspected Infection ◽

Machine Learning Methods ◽

Critical Patients ◽

Sepsis Screening

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

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