National Early Warning Score (NEWS) Outperforms Quick Sepsis-Related Organ Failure (qSOFA) Score for Early Detection of Sepsis in the Emergency Department

Early Recognition of Sepsis in Prehospital Settings: A Review of Screening Tools and Practices

Sepsis remains a global health challenge, with millions affected and high mortality rates. Early recognition is critical for improving outcomes, particularly in prehospital settings where timely interventions can significantly impact patient survival. This literature review examines screening tools used in prehospital environments, focusing on their predictive abilities, ease of use, and limitations in detecting sepsis. Tools such as the quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), National Early Warning Score 2 (NEWS2), Systemic Inflammatory Response Syndrome, and Monocyte Distribution Width offer varied strengths and applications in identifying sepsis. Research shows that NEWS and NEWS2 demonstrate higher sensitivity for predicting mortality, while qSOFA offers simplicity but may lack sensitivity outside of acute care settings. Differential diagnoses, such as pulmonary embolism and adrenal crisis, can mimic sepsis, making accurate assessment essential. The review highlights the role of Emergency Medical Services (EMS) and Family Nurse Practitioners in early detection and emphasizes the importance of evidence-based practices and clear protocols. This review aims to provide EMS and Nurse Practitioners with the knowledge and tools to recognize sepsis early, ensuring appropriate referrals and improving patient outcomes.

The smell of sepsis: Electronic nose measurements improve early recognition of sepsis in the ED

OBJECTIVE

Early recognition of sepsis is essential for timely initiation of adequate care. However, this is challenging as signs and symptoms may be absent or nonspecific. The cascade of events leading to organ failure in sepsis is characterized by immune-metabolic alterations. Volatile organic compounds (VOCs) are metabolic byproducts released in expired air. We hypothesize that measuring the VOC profile using electronic nose technology (eNose) could improve early recognition of sepsis.

MATERIAL AND METHODS

In this cohort study, bedside eNose measurements were collected prospectively from ED patients with suspected infections. Sepsis diagnosis was retrospectively defined based on Sepsis-3 criteria. eNose sensor data were used in a discriminant analysis to evaluate the predictive performance for early sepsis recognition. The dataset was randomly split into training (67 %) and validation (33 %) subsets. The derived discriminant function from the training subset was then applied to classify new observations in the validation subset. Model performance was evaluated using receiver operating characteristic (ROC) curves and predictive values.

RESULTS

We analyzed a total of 160 eNose measurements. The eNose measurements had an area under the ROC (AUROC) of 0.78 (95 % CI: 0.69-0.87) for diagnosing sepsis, with a sensitivity of 72 %, specificity of 73 %, and an overall accuracy of 73 %. The validation model showed an AUC of 0.83 (95 % CI: 0.71-0.94), sensitivity of 71 %, specificity of 83 %, and an accuracy of 80 %.

CONCLUSION

eNose measurements can identify sepsis among patients with a suspected infection at the ED.

CLINICAL TRIAL REGISTRATION

The study is embedded in the Acutelines data-biobank (www.acutelines.nl), registered in Clinicaltrials.gov (NCT04615065).

Modified National Early Warning Scores (MNEWS) for Predicting the Outcomes of Suspected Sepsis Patients; A Prospective Cohort Study

Introduction

The National Early Warning Score (NEWS) is commonly used to identify patients at high mortality risk. However, it has notable limitations. In this study, to enhance the accuracy, we revised it and evaluated the performance of modified NEWS (MNEWS) in predicting the outcomes of suspected sepsis patients.

Methods

This single-center, prospective cohort study was conducted on patients with suspected sepsis to evaluate the accuracy of MNEWS in predicting mortality, survival to discharge, vasopressor requirements, and the need for mechanical ventilation. The MNEWS comprises the NEWS variables plus age, chronic major organ dysfunction, malignancy, functional status, and specific infected organ involvement. Sensitivity, specificity, likelihood ratio (LR), and area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the MNEWS in predicting the studied outcomes.

Results

Of the 1,393 patients included in this study, 209 died. Mean MNEWS was significantly higher in non-survivors than survivors (19.8 vs. 14.9, p<0.001). The AUROC of MNEWS in predicting 30-day mortality was 0.82 (95% CI: 0.79-0.85). MNEWS ≥ 18 had the highest accuracy for 30-day mortality prediction with 76.1% sensitivity, 75% specificity, positive LR of 3.13, and AUROC of 0.76 (95% CI: 0.73-0.79). The AUROC of MNEWS ≥18 for predicting survival until discharge, need for vasopressors, and need for mechanical ventilation were 0.75 (95% CI: 0.72-0.78), 0.72 (95% CI: 0.69-0.75), and 0.76 (95% CI: 0.73-0.79), respectively. Additionally, MNEWS ≥18 demonstrated superior predictive performance, compared with NEWS ≥7 and qSOFA ≥2 for various clinical outcomes.

Conclusions

The MNEWS was similar to the NEWS in overall predictive accuracy for 30-day mortality but exhibited a higher predictive accuracy than did the qSOFA score. Notably, MNEWS ≥18 was a significant indicator of 30-day mortality risk, as well as the likelihood of requiring vasopressors, survival to discharge, and 7-day mortality.

Utilisation of the National Early Warning Score (NEWS) and Assessment of Patient Outcomes Following Cardiac Surgery

Objectives: The national early warning score (NEWS) was introduced to improve the detection of, and standardise the assessment of, the severity of acute illness in the National Health Service (NHS). We assessed whether the recommended threshold trigger score of 5 or more in a Critical Care Outreach Team (CCOT) review could accurately predict patients at risk of deterioration following cardiac surgery and patient outcomes. Methods: We investigated adult cardiac surgery patients between October 2019 and December 2021. NEWS 2 parameters triggering CCOT referrals and NEWS 2 parameters < 5 versus ≥5 were compared, and the resulting patient outcomes were evaluated. Results: Over this period, 3710 patients underwent surgery, of whom 162 (4.4%) initiated 193 calls to the CCOT. The mean number of NEWS 2 parameters on CCOT activation was 6.14 ± 2.43 (NEWS 0-16); 34 (20.98%) activations were from patients with NEWS 2 < 5. Low oxygen saturation (SpO2) (59.3%) and oxygen therapy (83.3%) were the most common physiological parameters raising the score. CCOT activations led to 38 transfers from the ward to the high-dependency unit (HDU) and 18 transfers to the intensive therapy unit (ITU). Cardiac arrest calls were initiated in 12 (7.40%) patients and two culminated in death. Fourteen (8.64%) had emergency resternotomy. The in-hospital mortality rate was 10.5% (17/162) in patients referred to CCOT versus 3.9% (139/3548) in patients who were not (p < 0.001). The in-hospital mortality in patients with NEWS 2 < 5 vs. NEWS ≥ 5 was 17.6% (6/34) versus 8.6% (11/128) (p = 0.126). Conclusions: There was no difference in in-hospital mortality in patients below or above a NEWS 2 of 5, but there was a significant difference in in-hospital mortality in patients reviewed by the CCOT (p < 0.001). Tailoring the threshold score specifically for the cardiac surgical cohort, in conjunction with clinician involvement, may improve outcomes.

Validation and comparison of triage-based screening strategies for sepsis

OBJECTIVE

This study sought to externally validate and compare proposed methods for stratifying sepsis risk at emergency department (ED) triage.

METHODS

This nested case/control study enrolled ED patients from four hospitals in Utah and evaluated the performance of previously-published sepsis risk scores amenable to use at ED triage based on their area under the precision-recall curve (AUPRC, which balances positive predictive value and sensitivity) and area under the receiver operator characteristic curve (AUROC, which balances sensitivity and specificity). Score performance for predicting whether patients met Sepsis-3 criteria in the ED was compared to patients' assigned ED triage score (Canadian Triage Acuity Score [CTAS]) with adjustment for multiple comparisons.

RESULTS

Among 2000 case/control patients, 981 met Sepsis-3 criteria on final adjudication. The best performing sepsis risk scores were the Predict Sepsis version #3 (AUPRC 0.183, 95 % CI 0.148-0.256; AUROC 0.859, 95 % CI 0.843-0.875) and Borelli scores (AUPRC 0.127, 95 % CI 0.107-0.160, AUROC 0.845, 95 % CI 0.829-0.862), which significantly outperformed CTAS (AUPRC 0.038, 95 % CI 0.035-0.042, AUROC 0.650, 95 % CI 0.628-0.671, p < 0.001 for all AUPRC and AUROC comparisons). The Predict Sepsis and Borelli scores exhibited sensitivity of 0.670 and 0.678 and specificity of 0.902 and 0.834, respectively, at their recommended cutoff values and outperformed Systemic Inflammatory Response Syndrome (SIRS) criteria (AUPRC 0.083, 95 % CI 0.070-0.102, p = 0.052 and p = 0.078, respectively; AUROC 0.775, 95 % CI 0.756-0.795, p < 0.001 for both scores).

CONCLUSIONS

The Predict Sepsis and Borelli scores exhibited improved performance including increased specificity and positive predictive values for sepsis identification at ED triage compared to CTAS and SIRS criteria.

Heart rate variability in acute pancreatitis: a narrative review

BACKGROUND AND OBJECTIVE

Acute pancreatitis (AP) is a complex inflammatory disorder with potential systemic repercussions including sepsis, multiple organ failure and mortality. As such, the development of a prognostic tool to assess the complications and severity of AP is critical as urgent medical intervention is warranted in cases of severe AP to prevent complications and reduce mortality. Despite the plethora of scoring systems such as the Acute Physiology and Chronic Health Evaluation II (APACHE II) score available for prognostication of AP, they often require manual invasive blood testing and lack the ability to monitor the dynamic progression of the disease. To this end, heart rate variability (HRV), a measure of the autonomic nervous system's modulation on cardiac activity, has emerged as a promising tool. Having been previously posited as a tool to monitor the progression of cardiovascular and neurological conditions, the use of HRV as a risk stratification tool for AP is highly plausible. Therefore, this study aims to synthesize the existing literature regarding the usage of HRV as a tool for the prognostication and monitoring of AP.

METHODS

A comprehensive literature search was conducted in PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus and Embase from inception to December 2023. Articles with mentions of AP and HRV were reviewed, and the complications of AP and its effects on HRV parameters were analyzed.

KEY CONTENT AND FINDINGS

Early studies on the use of HRV in AP have revealed the association of decreased HRV parameters with the development of subsequent complications, reflecting the suppression of sympathetic activity as a predominant driving force. In addition, HRV has also been shown to outperform other established scoring systems in predicting outcomes of the complications of AP, but more studies are needed to validate its accuracy.

CONCLUSIONS

Preliminary studies have shown that certain parameters of HRV may be used to predict the severity of AP and prognosticate outcomes. Although HRV monitoring demonstrates potential to be superior to existing scoring systems in AP, more research is needed to validate its use as a prognostic tool. Nevertheless, the prospective utility of HRV monitoring in predicting the onset and outcomes of AP and its complications remains optimistic.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

A customised down-sampling machine learning approach for sepsis prediction

OBJECTIVE

Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests.

METHODS

Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC.

RESULTS

With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC.

CONCLUSION

Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings.

Early sepsis care with the National Early Warning Score 2-guided Sepsis Hour-1 Bundle in the emergency department: hybrid type 1 effectiveness-implementation pilot stepped wedge randomised controlled trial (NEWS-1 TRIPS) protocol

INTRODUCTION

Early sepsis treatment in the emergency department (ED) is crucial to improve patient survival. Despite international promulgation, the uptake of the Surviving Sepsis Campaign (SSC) Hour-1 Bundle (lactate measurement, blood culture, broad-spectrum antibiotics, 30 mL/kg crystalloid for hypotension/lactate ≥4 mmol/L and vasopressors for hypotension during/after fluid resuscitation within 1 hour of sepsis recognition) is low across healthcare settings. Delays in sepsis recognition and a lack of high-quality evidence hinder its implementation. We propose a novel sepsis care model (National Early Warning Score, NEWS-1 care), in which the SSC Hour-1 Bundle is triggered objectively by a high NEWS-2 (≥5). This study aims to determine the feasibility of a full-scale type 1 hybrid effectiveness-implementation trial on the NEWS-1 care in multiple EDs.

METHODS AND ANALYSIS

We will conduct a pilot type 1 hybrid trial and prospectively recruit 200 patients from 4 public EDs in Hong Kong cluster randomised in a stepped wedge design over 10 months. All study sites will start with an initial period of standard care and switch in random order at 2-month intervals to the NEWS-1 care unidirectionally. The implementation evaluation will employ mixed methods guided by the Reach, Effectiveness, Adoption, Implementation and Maintenance framework, which includes qualitative and quantitative data from focus group interviews, staff survey and clinical record reviews. We will analyse the 14 feasibility outcomes as progression criteria to a full-scale trial, including trial acceptability to patients and staff, patient and staff recruitment rates, accuracy of sepsis screening, protocol adherence, accessibility to follow-up data, safety and preliminary clinical impacts of the NEWS1 care, using descriptive statistics.

ETHICS AND DISSEMINATION

The institutional review boards of all study sites approved this study. This study will establish the feasibility of a full-scale hybrid trial. We will disseminate the findings through peer-reviewed publications, conference presentations and educational activities.

TRIAL REGISTRATION NUMBER

NCT05731349.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND

This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED).

METHODS

We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points.

RESULTS

We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70-0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37-0.52) and 0.88 (95%CI 0.85-0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.

CONCLUSION

NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data

BACKGROUND

Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning.

METHODS

Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment.

RESULTS

DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346).

CONCLUSIONS

The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.

Predicting ICU admission and death in the Emergency Department: A comparison of six early warning scores

AIM

To compare the ability of the most used Early Warning Scores (EWS) to identify adult patients at risk of poor outcomes in the emergency department (ED).

METHODS

Single-center, retrospective observational study. We evaluated the digital records of consecutive ED admissions in patients ≥ 18 years from 2010 to 2019 and calculated NEWS, NEWS2, MEWS, RAPS, REMS, and SEWS based on parameters measured on ED arrival. We assessed the discrimination and calibration performance of each EWS in predicting death/ICU admission within 24 hours using ROC analysis and visual calibration. We also measured the relative weight of clinical and physiological derangements that identified patients missed by EWS risk stratification using neural network analysis.

RESULTS

Among 225,369 patients assessed in the ED during the study period, 1941 (0.9%) were admitted to ICU or died within 24 hours. NEWS was the most accurate predictor (area under the receiver operating characteristic [AUROC] curve 0.904 [95% CI 0.805-0.913]), followed by NEWS2 (AUROC 0.901). NEWS was also well calibrated. In patients judged at low risk (NEWS < 2), 359 events occurred (18.5% of the total). Neural network analysis revealed that age, systolic BP, and temperature had the highest relative weight for these NEWS-unpredicted events.

CONCLUSIONS

NEWS is the most accurate EWS for predicting the risk of death/ICU admission within 24 h from ED arrival. The score also had a fair calibration with few events occurring in patients classified at low risk. Neural network analysis suggests the need for further improvements by focusing on the prompt diagnosis of sepsis and the development of practical tools for the measurement of the respiratory rate.

Evaluation of qSOFA Combined with Inflammatory Mediators for Diagnosing Sepsis and Predicting Mortality among Emergency Department

BACKGROUND

There are no guidelines in China or worldwide that clearly recommend indicators for the early diagnosis of sepsis in the emergency department. Simple and unified joint diagnostic criteria are also scarce. We compare the Quick Sequential Organ Failure Assessment (qSOFA) score and inflammatory mediator concentrations in patients with normal infection, sepsis, and sepsis death.

METHODS

This study used a prospective and consecutive manner, including 79 patients with sepsis in the Emergency Department of Shenzhen People's Hospital from December 2020 to June 2021, and 79 patients with common infections (non-sepsis) matched by age and sex during the same period. The sepsis patients were then divided into a sepsis survival group (n=67) and a sepsis death group (n=12) based on whether they survived within 28 days. The baseline characteristics, qSOFA scores, the concentrations of tumor necrosis factor-α(TNF-α), interleukin (IL) -6, IL-1b, IL-8, IL-10, procalcitonin (PCT), high-sensitivity C-reactive protein (HSCRP) and other indicators were collected in all subjects.

RESULTS

PCT and qSOFA were independent risk factors for predicting sepsis in the emergency department. The AUC value of PCT was the largest (0.819) among all diagnostic indicators of sepsis, with a cut-off value of 0.775ng/ml and sensitivity and specificity of 0.785 and 0.709, respectively. The AUC of qSOFA combined PCT was the largest (0.842) in the combination of the 2 indicators, and the sensitivity and specificity were 0.722 and 0.848, respectively. IL-6 was an independent risk factor for predicting death within 28 days. IL-8 had the largest AUC value (0.826) among all indicators predicting sepsis death, with a cut-off value of 215 pg/ml and sensitivity and specificity of 0.667 and 0.895, respectively. Among the combination of two indicators, qSOFA combined with IL-8 had the largest AUC value (0.782) and sensitivity and specificity of 0.833 and 0.612, respectively.

CONCLUSIONS

QSOFA and PCT are independent risk factors for sepsis, and qSOFA combined with PCT may be an ideal combination for early diagnosis of sepsis in the emergency department. IL-6 is an independent risk factor for death within 28 days of sepsis, and qSOFA combined with IL-8 may be an ideal combination for early prediction of death within 28 days in sepsis patients in the emergency department.