Validation of the National Early Warning Score in the prehospital setting

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.

Predictive Value of Physiological Values and Symptom Scores for Exacerbations in Bronchiectasis and Chronic Obstructive Pulmonary Disease With Frequent Exacerbations: Longitudinal Observational Cohort Study

BACKGROUND

COPD (chronic obstructive pulmonary disease) and bronchiectasis are common, and exacerbations contribute to their morbidity and mortality. Predictive factors for the frequency of future exacerbations include previous exacerbation frequency and airway colonization. Earlier treatment of exacerbations is likely to reduce severity.

OBJECTIVE

This study tested the hypothesis that, in a population with bronchiectasis, COPD, or both who have frequent exacerbations and airway colonization, changes in symptom scores or physiological variables within 10 days prior to an exacerbation would allow the prediction of the event.

METHODS

We performed a 6-month, longitudinal, observational, cohort study among 30 participants with bronchiectasis, COPD, or both; at least 2 exacerbations per year; and colonization with Pseudomonas aeruginosa or Haemophilus influenzae. Daily symptom and physiological data were collected, comprising pulse rate, blood pressure, oxygen saturation, peak flow rate, step count, weight, and temperature. Exacerbations (defined as the onset of new antibiotic use for respiratory symptoms) were collected, and predictive values for abnormal values in the 10 days prior to an exacerbation were calculated.

RESULTS

A total of 30 participants were recruited, collecting a total of 39,534 physiological and 25,334 symptom data points across 5358 participant-days; these included 78 exacerbations across 27 participants, with the remaining 3 participants not having exacerbations within the 6-month observation period. Peak flow rate, oxygen saturation, and weight were significantly different at the point of exacerbation (all P<.001), but no significant trends around exacerbation were noted and no clinically beneficial predictive value was found in the overall or individually adjusted model. Symptom scores tended to worsen for 10 days on either side of an exacerbation but were of insufficient magnitude for prediction, with area under the receiver operating characteristic curve values of ranging from 0.4 to 0.6.

CONCLUSIONS

Within this small cohort with bronchiectasis, COPD, or both and airway colonization, physiological and symptom variables did not show sufficient predictive value for exacerbations to be of clinical utility. The self-management education provided as standard of care may be superior to either of these approaches, but benefit in another or larger cohort cannot be excluded.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/resprot.6636.

Predictors of nursing home conveyances to emergency department

BACKGROUND

With increasing rates of patient conveyances from nursing homes to emergency departments worldwide, we aim to examine factors causing high rates of conveyances from nursing homes to the emergency department (ED) of an acute tertiary hospital.

METHODS

This was a prospective study involving presentation of ED attendances from nursing home residents during out-of-hours over a 23-month period from April 2020 to February 2022. Data was collected from a standardized manual form used by the Emergency Department to document nursing home conveyances.

RESULTS

A total of 338 pre-conveyance forms were reviewed. The most common reasons for conveyances to ED were neurological symptoms (16%), unstable hemodynamics (12%), fever (11%) and falls (10%). The peak conveyances occurred between 1600 and 1900 h on weekends. Respiratory rate, oxygenation requirements and high National Early Warning Score (NEWS) were significantly associated with increased conveyances to the emergency department. When the components of NEWS were analyzed individually, decision for ambulance conveyance to emergency department was significantly associated with respiratory rate (p < .001), oxygen saturation (p < .001), and the use of oxygen supplementation (p < .005).

CONCLUSIONS

Unstable hemodynamics and falls were among the leading factors for nursing home conveyances to the emergency department, which highlights the need to implement better fall prevention strategies and standardized parameters monitoring in nursing homes. Future research should focus on outcomes of conveyances and the characteristics of nursing home with higher conveyance rates. This would aid to assess the appropriateness of conveyances and to identify strategies to decrease preventable conveyances.

The accuracy of predicting hospital admission by emergency medical service and emergency department personnel compared to the prehospital MEWS: a prospective multicenter study

INTRODUCTION

Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient's disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission.

METHODS

A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients' need for hospital admission.

RESULTS

In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7-95.9% versus 60.6%, all p < 0.001).

CONCLUSIONS

Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

A prediction model for prehospital clinical deterioration: The use of early warning scores

BACKGROUND

Various prognosticative approaches to assist in recognizing clinical deterioration have been proposed. To date, early warning scores (EWSs) have been evaluated in hospital with limited research investigating their suitability in the prehospital setting. This study evaluated the predictive ability of established EWSs and other clinical factors for prehospital clinical deterioration.

METHODS

A retrospective cohort study investigating adult patients of all etiologies attended by Queensland Ambulance Service paramedics between January 1, 2018, and December 31, 2020, was conducted. With logistic regression, several models were developed to predict adverse event outcomes. The National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Queensland Adult Deterioration Detection System (Q-ADDS), and shock index were calculated from vital signs taken by paramedics.

RESULTS

A total of 1,422,046 incidents met the inclusion criteria. NEWS, MEWS, and Q-ADDS were found to have comparably high predictive ability with area under the receiver operating characteristic curve (AUC-ROC) between 70% and 90%, whereas shock index had relatively low AUC-ROC. Sensitivity was lower than specificity for all models. Although established EWSs performed well when predicting adverse events, these scores require complex calculations requiring multiple vital signs that may not be suitable for the prehospital setting.

CONCLUSIONS

This study found NEWS, MEWS, and Q-ADDS all performed well in the prehospital setting. Although a simple shock index is easier for paramedics to use in the prehospital environment, it did not perform comparably to established EWSs. Further research is required to develop suitably performing parsimonious solutions until established EWSs are integrated into technological solutions to be used by prehospital clinicians in real time.

Optimal timing for the Modified Early Warning Score for prediction of short-term critical illness in the acute care chain: a prospective observational study

INTRODUCTION

The Modified Early Warning Score (MEWS) is an effective tool to identify patients in the acute care chain who are likely to deteriorate. Although it is increasingly being implemented in the ED, the optimal moment to use the MEWS is unknown. This study aimed to determine at what moment in the acute care chain MEWS has the highest accuracy in predicting critical illness.

METHODS

Adult patients brought by ambulance to the ED at both locations of the Amsterdam UMC, a level 1 trauma centre, were prospectively included between 11 March and 28 October 2021. MEWS was calculated using vital parameters measured prehospital, at ED presentation, 1 hour and 3 hours thereafter, imputing for missing temperature and/or consciousness, as these values were expected not to deviate. Critical illness was defined as requiring intensive care unit admission, myocardial infarction or death within 72 hours after ED presentation. Accuracy in predicting critical illness was assessed using the area under the receiver operating characteristics curve (AUROC).

RESULTS

Of the 790 included patients, critical illness occurred in 90 (11.4%). MEWS based on vital parameters at ED presentation had the highest performance in predicting critical illness with an AUROC of 0.73 (95% CI 0.67 to 0.79) but did not significantly differ compared with other moments. Patients with an increasing MEWS over time are significantly more likely to become critical ill compared with patients with an improving MEWS.

CONCLUSION

The performance of MEWS is moderate in predicting critical illness using vital parameters measured surrounding ED admission. However, an increase of MEWS during ED admission is correlated with the development of critical illness. Therefore, early recognition of deteriorating patients at the ED may be achieved by frequent MEWS calculation. Further studies should investigate the effect of continuous monitoring of these patients at the ED.

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission.

METHODS

We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC).

RESULTS

Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85-0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN).

CONCLUSION

The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

Prehospital early warning scores for adults with suspected sepsis: the PHEWS observational cohort and decision-analytic modelling study

Background

Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department.

Objectives

To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment.

Design

Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness.

Setting

Two ambulance services and four acute hospitals in England.

Participants

Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded.

Interventions

Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation.

Main outcome measures

Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained.

Results

Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed.

Limitations

We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling.

Conclusions

No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis.

Study registration

This study is registered as Research Registry (reference: researchregistry5268).

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

Correlation of vital sign centiles with in-hospital outcomes among adults encountered by emergency medical services

BACKGROUND

Vital signs are a critical component of the prehospital assessment. Prior work has suggested that vital signs may vary in their distribution by age. These differences in vital signs may have implications on in-hospital outcomes or be utilized within prediction models. We sought to (1) identify empirically derived (unadjusted) cut points for vital signs for adult patients encountered by emergency medical services (EMS), (2) evaluate differences in age-adjusted cutoffs for vital signs in this population, and (3) evaluate unadjusted and age-adjusted vital signs measures with in-hospital outcomes.

METHODS

We used two multiagency EMS data sets to derive (National EMS Information System from 2018) and assess agreement (ESO, Inc., from 2019 to 2021) of vital signs cutoffs among adult EMS encounters. We compared unadjusted to age-adjusted cutoffs. For encounters within the ESO sample that had in-hospital data, we compared the association of unadjusted cutoffs and age-adjusted cutoffs with hospitalization and in-hospital mortality.

RESULTS

We included 13,405,858 and 18,682,684 encounters in the derivation and validation samples, respectively. Both extremely high and extremely low vital signs demonstrated stepwise increases in admission and in-hospital mortality. When evaluating age-based centiles with vital signs, a gradual decline was noted at all extremes of heart rate (HR) with increasing age. Extremes of systolic blood pressure at upper and lower margins were greater in older age groups relative to younger age groups. Respiratory rate (RR) cut points were similar for all adult age groups. Compared to unadjusted vital signs, age-adjusted vital signs had slightly increased accuracy for HR and RR but lower accuracy for SBP for outcomes of mortality and hospitalization.

CONCLUSIONS

We describe cut points for vital signs for adults in the out-of-hospital setting that are associated with both mortality and hospitalization. While we found age-based differences in vital signs cutoffs, this adjustment only slightly improved model performance for in-hospital outcomes.

The prehospital NEW score to assess septic shock in-hospital, 30-day and 90-day mortality

BACKGROUND

The early identification of sepsis presenting a high risk of deterioration is a daily challenge to optimise patient pathway. This is all the most crucial in the prehospital setting to optimize triage and admission into the appropriate unit: emergency department (ED) or intensive care unit (ICU). We report the association between the prehospital National Early Warning Score 2 (NEWS-2) and in-hospital, 30 and 90-day mortality of SS patients cared for in the pre-hospital setting by a mobile ICU (MICU).

METHODS

Septic shock (SS) patients cared for by a MICU between 2016, April 6th and 2021 December 31st were included in this retrospective cohort study. The NEWS-2 is based on 6 physiological variables (blood pressure, heart rate, respiratory rate, temperature, oxygen saturation prior oxygen supplementation, and level of consciousness) and ranges from 0 to 20. The Inverse Probability Treatment Weighting (IPTW) propensity method was applied to assess the association with in-hospital, 30 and 90-day mortality. A NEWS-2 ≥ 7 threshold was chosen for increased clinical deterioration risk definition and usefulness in clinical practice based on previous reports.

RESULTS

Data from 530 SS patients requiring MICU intervention in the pre-hospital setting were analysed. The mean age was 69 ± 15 years and presumed origin of sepsis was pulmonary (43%), digestive (25%) or urinary (17%) infection. In-hospital mortality rate was 33%, 30 and 90-day mortality were respectively 31% and 35%. A prehospital NEWS-2 ≥ 7 is associated with an increase in-hospital, 30 and 90-day mortality with respective RRa = 2.34 [1.39-3.95], 2.08 [1.33-3.25] and 2.22 [1.38-3.59]. Calibration statistic values for in-hospital mortality, 30-day and 90-day mortality were 0.54; 0.55 and 0.53 respectively.

CONCLUSION

A prehospital NEWS-2 ≥ 7 is associated with an increase in in-hospital, 30 and 90-day mortality of septic shock patients cared for by a MICU in the prehospital setting. Prospective studies are needed to confirm the usefulness of NEWS-2 to improve the prehospital triage and orientation to the adequate facility of sepsis.

Evaluating the performance of the National Early Warning Score in different diagnostic groups

BACKGROUND

The National Early Warning Score (NEWS) is used in hospitals across the UK to detect deterioration of patients within care pathways. It is used for most patients, but there are relatively few studies validating its performance in groups of patients with specific conditions.

METHODS

The performance of NEWS was evaluated against 36 other Early Warning Scores, in 123 patient groups, through use of the area under the receiver operating characteristic (AUROC) curve technique, to compare the abilities of each Early Warning Score to discriminate an outcome within 24hrs of vital sign recording. Outcomes evaluated were death, ICU admission, or a combined outcome of either death or ICU admission within 24 hours of an observation set.

RESULTS

The National Early Warning Score 2 performs either best or joint best within 120 of the 123 patient groups evaluated and is only outperformed in prediction of unanticipated ICU admission. When outperformed by other Early Warning Scores in the remaining 3 patient groups, the performance difference was marginal.

CONCLUSIONS

Consistently high performance indicates that NEWS is a suitable early warning score to use for all diagnostic groups considered by this analysis, and patients are not disadvantaged through use of NEWS in comparison to any of the other evaluated Early Warning Scores.

Prehospital National Early Warning Score as a predictor of massive transfusion in adult trauma patients

BACKGROUND

Previous studies have shown that an elevated prehospital National Early Warning Score (preNEWS) is associated with increased levels of adverse outcomes in patients with trauma. However, whether preNEWS is a predictor of massive transfusion (MT) in patients with trauma is currently unknown. This study investigated the accuracy of preNEWS in predicting MT and hospital mortality among trauma patients.

METHODS

We analyzed adult trauma patients who were treated and transported by emergency medical services (EMS) between January 2018 and December 2019. The main exposure was the preNEWS calculated for the scene. The primary outcome was the predictive ability for MT, and the secondary outcome was 24 h mortality. We compared the prognostic performance of preNEWS with the shock index, modified shock index, and reverse shock index, and reverse shock index multiplied by Glasgow Coma Scale in the prehospital setting.

RESULTS

In total, 41,852 patients were included, and 1456 (3.5%) received MT. preNEWS showed the highest area under the receiver operating characteristic (AUROC) curve for predicting MT (0.8504; 95% confidence interval [CI], 0.840-0.860) and 24 h mortality (AUROC 0.873; 95% CI, 0.863-0.883). The sensitivity of preNEWS for MT was 0.755, and the specificity of preNEWS for MT was 0.793. All indicies had a high negative predictive value and low positive predictive value.

CONCLUSION

preNEWS is a useful, rapid predictor for MT and 24 h mortality. Calculation of preNEWS would be helpful for making the decision at the scene such as transfer straightforward to trauma center and advanced treatment.

National Early Warning Scores Following Emergency Hospital Transfer: Implications for Care Home Residents

OBJECTIVE

Care home residents have high rates of hospital admission. The UK National Early Warning Score (NEWS2) standardizes the secondary care response to acute illness. However, the ability of NEWS2 to predict adverse health outcomes specifically for care home residents is unknown. This study explored the relationship between NEWS2 on admission to hospital and resident outcome 7 days later.

DESIGN

Repeated cross-sectional study.

SETTING AND PARTICIPANTS

Data on UK care home residents admitted to 160 hospitals in two 24-hour periods (2019 and 2020).

METHOD

Chi-squared and Kruskal-Wallis tests, and multinomial regression were used to explore the association between low (score ≤2), intermediate (3-4), high (5-6), and critically high (≥7) NEWS2 on admission and each of the following: discharge on day of admission, admission and discharge within 7 days, prolonged hospital admission (>7 days), and death.

RESULTS

From 665 resident admissions across 160 hospital sites, NEWS2 was low for 54%, intermediate for 18%, high for 13%, and critically high for 16%. The 7-day outcome was 10% same-day discharge, 47% admitted and subsequently discharged, 34% remained inpatients, and 8% died. There is a significant association between NEWS2 and these outcomes (P < .001). Compared with those with low NEWS2, residents with high and critically high NEWS2 had 3.6 and 9.5 times increased risk of prolonged hospitalization [relative risk ratio (RRR) 3.56; 95% CI 1.02-12.37; RRR 9.47; CI 2.20-40.67], respectively. The risk of death was approximately 14 times higher for residents with high NEWS2 (RRR 13.62; CI 3.17-58.49) and 54 times higher (RRR 53.50; CI 11.03-259.54) for critically high NEWS2.

CONCLUSION AND IMPLICATIONS

Higher NEWS2 measurements on admission are associated with an increased risk of hospitalization up to 7 days duration, prolonged admission, and mortality for care home residents. NEWS2 may have a role as an adjunct to acute care decision making for hospitalized residents.

Tracking the National Early Warning Score 2 from Prehospital Care to the Emergency Department: A Prospective, Ambulance-Based, Observational Study

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event.Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value.Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively.Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between "at the emergency scene" and "pre-evacuation" presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

Accuracy of Trauma on Scene Triage Screening Tool (Shock Index, Reverse Shock Index Glasgow Coma Scale and National Early Warning Score) to Predict the Severity of Emergency Department Triage: A Retrospective Cross-Sectional Study

Introduction

Prehospital trauma care includes on-scene assessments, essential treatment, and facilitating transfer to an appropriate trauma center to deliver optimal care for trauma patients. While the Simple Triage and Rapid Treatment (START), Revised Triage Sieve (rTS), and National Early Warning Score (NEWS) tools are user-friendly in a prehospital setting, there is currently no standardized on-scene triage protocol in Thailand Emergency Medical Service (EMS). Therefore, this study aims to evaluate the precision of these tools (SI, rSIG, and NEWS) in predicting the severity of trauma patients who are transferred to the emergency department (ED).

Methods

This study was a retrospective cross-sectional and diagnostic research conducted on trauma patients transferred by EMS to the ED of Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand, from January 2015 to September 2022. We compared the on-scene triage tool (SI, rSIG, and NEWS) and ED triage tool (Emergency Severity Index) parameters, massive transfusion protocol (MTP), and intensive care unit (ICU) admission with the area under ROC (univariable analysis) and diagnostic odds ratio (multivariable logistic regression analysis). The optimal cut-off threshold for the best parameter was determined by selecting the value that produced the highest area under the ROC curve.

Results

A total of 218 patients were traumatic patients transported by EMS to the ED, out of which 161 were classified as ESI levels 1-2, while the remaining 57 patients were categorized as levels 3-5 on the ESI triage scale. We found that NEWS was a more accurate triage tool to discriminate the severity of trauma patients than rSIG and SI. The area under the ROC was 0.74 (95% CI 0.70-0.79) (OR 18.98, 95% CI 1.06-337.25), 0.65 (95% CI 0.59-0.70) (OR 1.74, 95% CI 0.17-18.09) and 0.58 (95% CI 0.52-0.65) (OR 0.28, 95% CI 0.04-1.62), respectively (P-value <0.001). The cut point of NEWS to discriminate ESI levels 1-2 and levels 3-5 was >6 points.

Conclusion

NEWS is the best on-scene triage screening tool to predict the severity at the emergency department, massive transfusion protocol (MTP), and intensive care unit (ICU) admission compared with other triage tools SI and rSIG.

The impact of pharmacist early active consultation (PEAC) on multidrug resistance organism treatment outcomes: A prospective historically controlled study

Background and aim: Infectious disease (ID) consultation can improve multidrug-resistant organism (MDRO) treatment outcomes. However, the impact of clinical pharmacists' ID consultation on MDRO therapy, especially early initiation, has not been reported. In this study, we try to explore the impact of the pharmacist early active consultation (PEAC) on MDRO patient management. Methods: We conducted a prospective historical controlled study based on PEAC in MDRO patients. The retrospective control group was patients hospitalized 18 months before the PEAC initiation, and the prospective PEAC group was patients hospitalized 18 months after the PEAC initiation. Primary endpoint was 30-day all-cause mortality. Secondary outcomes were MDRO clinical outcome, duration of antibiotic use, length of stay, antibiotic consumption and antibiotic costs. Further subgroup analysis of secondary outcomes was performed by the condition at admission, MDRO pathogenicity and MDRO clinical outcome. Results: 188 MDRO patients were included. After adjusting for potential predictors, PEAC reduced the 30-day all-cause mortality by 70% (HR 0.30, 95% CI 0.09-0.96, p = 0.042). PEAC group had clinical improvement than control group (89.47% vs. 65.59%, p < 0.001), especially in patients with non-severe clinical conditions at admission (98.41% vs. 70.18%, p < 0.001). However, no significant differences were found between groups in length of stay, antibiotics consumption, and antibiotics costs. Conclusion: Early active pharmacy ID consultation can reduce 30-day all-cause mortality and improve clinical outcomes in MDRO patients.

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.

Evaluation of plasma lactate parameters for predicting mortality of septic patients

Objective

To compare the accuracy of serum lactate parameters, including lactate peak concentration (LACpeak), lactate time area (LACarea), and lactate clearance (LC) for predicting mortality of the septic patients, and to compare with the predictive accuracy of National Early Warning Score (NEWS) and Sequential Organ Failure Assessment (SOFA) scores.

Methods

This study retrospectively screened the septic patients admitted to the ICU in the Medical Information Mart for Intensive Care IV (MIMIC-IV) from 2008 to 2019. The baseline data and outcomes of patients were gathered. The subjects were divided into the non-survival group and the survival group. SOFA, NEWS, LACpeak, and LACarea were recorded. The LC was calculated 6 h after LACpeak. The above parameters were compared by the T-test and Mann-Whitney U test, and odds ratios were calculated adjusting for age and sex. The receiver operating characteristic curves (ROCs) of subjects were plotted according to SOFA, NEWS, LACpeak, and LACarea within 24h, and LC at 6h of ICU admission. The Areas under the ROC curve (AUCs), sensitivity, and specificity were compared with R version 4.1.1.

Results

1,169 septic patients were involved, and 366 (31.3%) patients died within 28 days. Compared to the survival group, the LACpeak of the non-survival group was higher [4.85 (3.2, 7.9) vs. 3.4 (2.6, 5.25) mmol/L, adjusted odds ratio 1.18, P < 0.001], and the LACarea of the non-survivals was higher than the survivals too [18.44 (10.36, 27.63) vs. 13.65 (9.01, 21.73), adjusted odds ratio 1.03, P < 0.001)]. The LC of the survivals at 6 h after LACpeak was significantly higher than that of the non-survivals [0.26 (0.14.0.42) vs. 0.19 (0.10, 0.33), adjusted odds ratio 0.06, P < 0.01]. Within 24h of ICU admission, the AUCs of mortality prediction in descending order were NEWS [0.73 (0.70, 0.76)], SOFA [0.69 (0.66, 0.73)], LACpeak [0.64 (0.61, 0.68)], and LACarea [0.60 (0.56, 0.63)]. There were 204 patients with 6-hour LC after LACpeak the AUCs of LACarea, LACpeak and LC were 0.73(0.65, 0.80), 0.71(0.62,0.78) and 0.65 (0.56, 0.73), respectively.

Conclusions

The predictive accuracy of LC was not superior to LACpeak and LACarea for the mortality of the septic patients and the predictive value of all the above lactate parameters for mortality maybe not better than SOFA and NEWS.

One-on-one comparison between qCSI and NEWS scores for mortality risk assessment in patients with COVID-19

OBJECTIVE

To compare the predictive value of the quick COVID-19 Severity Index (qCSI) and the National Early Warning Score (NEWS) for 90-day mortality amongst COVID-19 patients.

METHODS

Multicenter retrospective cohort study conducted in adult patients transferred by ambulance to an emergency department (ED) with suspected COVID-19 infection subsequently confirmed by a SARS-CoV-2 test (polymerase chain reaction). We collected epidemiological data, clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and use of supplemental oxygen) and hospital variables. The primary outcome was cumulative all-cause mortality during a 90-day follow-up, with mortality assessment monitoring time points at 1, 2, 7, 14, 30 and 90 days from ED attendance. Comparison of performances for 90-day mortality between both scores was carried out by univariate analysis.

RESULTS

From March to November 2020, we included 2,961 SARS-CoV-2 positive patients (median age 79 years, IQR 66-88), with 49.2% females. The qCSI score provided an AUC ranging from 0.769 (1-day mortality) to 0.749 (90-day mortality), whereas AUCs for NEWS ranging from 0.825 for 1-day mortality to 0.777 for 90-day mortality. At all-time points studied, differences between both scores were statistically significant (p < .001).

CONCLUSION

Patients with SARS-CoV-2 can rapidly develop bilateral pneumonias with multiorgan disease; in these cases, in which an evacuation by the EMS is required, reliable scores for an early identification of patients with risk of clinical deterioration are critical. The NEWS score provides not only better prognostic results than those offered by qCSI at all the analyzed time points, but it is also better suited for COVID-19 patients.KEY MESSAGESThis work aims to determine whether NEWS is the best score for mortality risk assessment in patients with COVID-19.AUCs for NEWS ranged from 0.825 for 1-day mortality to 0.777 for 90-day mortality and were significantly higher than those for qCSI in these same outcomes.NEWS provides a better prognostic capacity than the qCSI score and allows for long-term (90 days) mortality risk assessment of COVID-19 patients.

Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review

Background and Objectives

Patient assessment and possible deterioration prediction are a healthcare priority. Increasing demand for outpatient emergency care services requires the implementation of simple, quick, and effective systems of patient evaluation and stratification. The purpose of this review is to identify the most effective Early Warning Score (EWS) for the early detection of the risk of complications when screening emergency outpatients for a potentially serious condition.

Materials and Methods

Systematic review of the bibliography made in 2022. Scientific articles in Spanish and English were collected from the databases and search engines of Pubmed, Cochrane, and Dialnet, which were published between 2017 and 2021 about EWSs and their capacity to predict complications.

Results

For analysis eleven articles were selected. Eight dealt with the application of different early warning scores in outpatient situations, concluding that all the scoring systems they studied were applicable. Three evaluated the predictive ability of various scoring systems and found no significant differences in their results. The eight articles evaluated the suitability of NEWS/NEWS2 to outpatient conditions and concluded it was the most suitable in pre-hospital emergency settings.

Conclusions

The early warning scores that were studied can be applied at the pre-hospital level, as they can predict patient mortality in the short term (24 or 48 h) and support clinical patient evaluation and medical decision making. Among them, NEWS2 is the most suitable for screening potentially deteriorating medical emergency outpatients.

A systematic review of the discrimination and absolute mortality predicted by the National Early Warning Scores according to different cut-off values and prediction windows

BACKGROUND

Although early warning scores were intended to simply identify patients in need of life-saving interventions, prediction has become their commonest metric. This review examined variation in the ability of the National Early Warning Scores (NEWS) in adult patients to predict absolute mortality at different times and cut-offs values.

METHOD

Following PRISMA guidelines, all studies reporting NEWS and NEWS2 providing enough information to fulfil the review's aims were included.

RESULTS

From 121 papers identified, the average area under the Receiver Operating Characteristic curve (AUC) for mortality declined from 0.90 at 24-hours to 0.76 at 30-days. Studies with a low overall mortality had a higher AUC for 24-hour mortality, as did general ward patients compared to patients seen earlier in their treatment. 24-hour mortality increased from 1.8% for a NEWS ≥3 to 7.8% for NEWS ≥7. Although 24-hour mortality for NEWS <3 was only 0.07% these deaths accounted for 9% of all deaths within 24-hours; for NEWS <7 24-hour mortality was 0.23%, which accounted for 44% of all 24-hour deaths. Within 30-days of a NEWS recording 22% of all deaths occurred in patients with a NEWS <3, 52% in patients with a NEWS <5, and 75% in patient with a NEWS <7.

CONCLUSION

NEWS reliably identifies patients most and least likely to die within 24-hours, which is what it was designed to do. However, many patients identified to have a low risk of imminent death die within 30-days. NEWS mortality predictions beyond 24-hours are unreliable.

The use of early warning system scores in prehospital and emergency department settings to predict clinical deterioration: A systematic review and meta-analysis

Background

It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration of patients when applied in the Emergency Department (ED) or prehospital setting.

Methods

This systematic review (SR) and meta-analysis assessed the predictive abilities of five commonly used EWS scores (National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS), and Cardiac Arrest Risk Triage (CART)). Outcomes of interest included admission to intensive care unit (ICU), and 3-to-30-day mortality following hospital admission. Using DerSimonian and Laird random-effects models, pooled estimates were calculated according to the EWS score cut-off points, outcomes, and study setting. Risk of bias was evaluated using the Newcastle-Ottawa scale. Meta-regressions investigated between-study heterogeneity. Funnel plots tested for publication bias. The SR is registered in PROSPERO (CRD42020191254).

Results

Overall, 11,565 articles were identified, of which 20 were included. In the ED setting, MEWS, and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (95% Confidence Interval (CI) 2.35–6.99) to 6.48 (95% CI 1.83–22.89), p = 0.757. MEWS at a cut-off point ≥3 had a similar DOR when predicting ICU admission (5.54 (95% CI 2.02–15.21)). MEWS ≥5 and NEWS ≥7 had DORs of 3.05 (95% CI 2.00–4.65) and 4.74 (95% CI 4.08–5.50), respectively, when predicting 30-day mortality in patients presenting with sepsis in the ED. In the prehospital setting, the EWS scores significantly predicted 3-day mortality but failed to predict 30-day mortality.

Conclusion

EWS scores’ predictability of clinical deterioration is improved when the score is applied to patients treated in the hospital setting. However, the high thresholds used and the failure of the scores to predict 30-day mortality make them less suited for use in the prehospital setting.

Comparison of Five Triage Tools for Identifying Mortality Risk and Injury Severity of Multiple Trauma Patients Admitted to the Emergency Department in the Daytime and Nighttime: A Retrospective Study

Effective triage tools are indispensable for doctors to make a prompt decision for the treatment of multiple trauma patients in emergency departments (EDs). The Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), standardized early warning score (SEWS), Modified Rapid Emergency Medicine Score (mREMS), and Revised Trauma Score (RTS) are five common triage tools proposed for trauma management. However, few studies have compared these tools in a multiple trauma cohort and investigated the influence of nighttime admission on the performance of these tools. This retrospective study was aimed at evaluating and comparing the performance of MEWS, NEWS, SEWS, mREMS, and RTS for identifying the mortality risk and trauma severity of patients with multiple trauma admitted to the ED during the daytime and nighttime. Retrospective data were collected from the medical records of patients with multiple trauma admitted in the daytime or nighttime to calculate scores for each triage tool. Logistic regression analysis was conducted on each triage tool for identifying in-hospital mortality and severe trauma (injury severity score > 15) in the daytime and nighttime. The performance of the tools was evaluated and compared by calculating area under the receiver operating characteristic curve (AUROC) of the retrospective logistic model of each tool. We collected data for 1,818 admissions, including 1,070 daytime and 748 nighttime admissions. A comparison of performance for identifying in-hospital mortality between daytime and nighttime yielded the following results (AUROC): MEWS (0.95 vs. 0.93, p = 0.384), NEWS (0.95 vs. 0.94, p = 0.708), SEWS (0.95 vs. 0.94, p = 0.683), mREMS (0.94 vs. 0.92, p = 0.286), and RTS (0.93 vs. 0.93, p = 0.87). Similarly, a comparison of performance for identifying trauma severity between daytime and nighttime yielded the following results (AUROC): MEWS (0.78 vs. 0.78, p = 0.95), NEWS (0.8 vs. 0.8, p = 0.885), SEWS (0.78 vs. 0.78, p = 0.818), mREMS (0.75 vs. 0.69, p = 0.019), and RTS (0.75 vs. 0.74, p = 0.619). All five scores are excellent triage tools (AUROC ≥ 0.9) for identifying in-hospital mortality for both daytime and nighttime admissions. However, they have only moderate effectiveness (AUROC < 0.9) at identifying severe trauma. The NEWS is the best triage tool for identifying severe trauma for both daytime and nighttime admissions. The MEWS, NEWS, SEWS, and RTS exhibited no significant differences in performance for identifying in-hospital mortality or severe trauma during the daytime or nighttime. However, the mREMS was better at identifying severe trauma during the daytime.

Differences in goals of care discussion outcomes among healthcare professionals: an observational cross-sectional study

BACKGROUND

Goals of care discussions ensure patients receive the care that they want. Recent studies have recognized the opportunity for allied health professionals, such as nurses, in facilitating goals of care discussions. However, the outcomes of such interventions are not well studied.

AIM

To compare the outcomes of goals of care discussions led by physicians and nurses.

DESIGN

This is a retrospective cohort study of patients admitted to an Internal Medicine unit from January 2018 to August 2019. A comprehensive chart review was performed on a random sample of patients. Patient's decision to accept or refuse cardiopulmonary resuscitation was recorded and analyzed. Analysis was stratified by patients' comorbidity burden and illness severity.

SETTING/PARTICIPANTS

The study took place at a tertiary care center and included 200 patients. Patients aged ⩾ 18 were included. Patients who have had pre-existing goals of care documentation were excluded.

RESULTS

About 52% of the goals of care discussions were completed by nurses and 48% by physicians. Patients were more likely to accept cardiopulmonary resuscitation in nurse-led discussions compared to physician-led ones (80.8% vs 61.4%, p = 0.003). Multiple regression showed that patients with higher comorbidity burden (OR 0.71, 95% CI: 0.62-0.82), more severe illness (OR 0.89, 95% CI 0.88-0.99), and physician-led goals of care discussions (OR 0.30, 95% CI: 0.15-0.62) were less likely to accept cardiopulmonary resuscitation.

CONCLUSIONS

There was a significant difference between the outcomes of goals of care discussions led by nurses and physicians. Patients were more likely to accept aggressive resuscitative measures in nurse-led goals of care discussions. Further research efforts are needed to identify the factors contributing to this discrepancy, and to devise ways of improving goals of care discussion delivery.

Epidemiology of emergency ambulance service calls related to COVID-19 in Scotland: a national record linkage study

BACKGROUND

COVID-19 has overwhelmed health services across the world; its global death toll has exceeded 5.3 million and continues to grow. There have been almost 15 million cases of COVID-19 in the UK. The need for rapid accurate identification, appropriate clinical care and decision making, remains a priority for UK ambulance service. To support identification and conveyance decisions of patients presenting with COVID-19 symptoms the Scottish Ambulance Service introduced the revised Medical Priority Dispatch System Protocol 36, enhanced physician led decision support and prehospital clinical guidelines. This study aimed to characterise the impact of these changes on the pathways and outcomes of people attended by the SAS) with potential COVID-19.

METHODS

A retrospective record linkage cohort study using National Data collected from NHS Scotland over a 5 month period (April-August 2020).

RESULTS

The SAS responded to 214,082 emergency calls during the study time period. The positive predictive value of the Protocol 36 to identify potentially COVID-19 positive patients was low (17%). Approximately 60% of those identified by Protocol 36 as potentially COVID-19 positive were conveyed. The relationship between conveyance and mortality differed between Protocol 36 Covid-19 positive calls and those that were not. In those identified by Protocol 36 as Covid-19 negative, 30 day mortality was higher in those not conveyed (not conveyed 9.2%; conveyed 6.6%) but in the Protocol 36 Covid-19 positive calls, mortality was higher in those conveyed (not conveyed 4.3% conveyed 8.8%). Thirty-day mortality rates of those with COVID-19 diagnosed through virology was between 28.8 and 30.2%.

CONCLUSION

The low positive predictive value (17%) of Protocol 36 in identifying potential COVID-19 in patients emphasises the importance of ambulance clinicians approaching each call as involving COVID-19, reinforcing the importance of adhering to existing policy and continued use of PPE at all calls. The non-conveyance rate of people that were categorised as COVID-19 negative was higher than in the preceding year in the same service. The reasons for the higher rates of non-conveyance and the relationship between non conveyance rates and death at 3 and 30 days post index call are unknown and would benefit from further study.

National Early Warning Score 2: transcultural adaptation to Brazilian Portuguese

OBJECTIVE

Cross-cultural adaptation of the National Early Warning Score 2 to Brazilian Portuguese.

METHODS

A methodological study of a cross-cultural adaptation of a scale, based on the Beaton et al. framework, authorized by the Royal College of Physicians. Judges from nine Brazilian states, nurses and physicians evaluated the semantic, idiomatic, cultural, and conceptual equivalence between the original instrument and the translated versions. The nurses, working in inpatient or emergency units, conducted the pilot test, applying the final version to three case studies. Psychometric tests were used for data analysis: Content Validity Index (CVI), Kappa Coefficient, and Cronbach's Alpha.

RESULTS

The adaptation showed a mean CVI of 0.98 and perfect/almost perfect inter-rater agreement, with scores above 0.80. The consistency of the scale was 0.712.

CONCLUSION

The process of cross-cultural adaptation of the scale to Brazilian Portuguese was successful, providing Brazilian professionals with an instrument aligned with patient safety.

Effect of the First Wave of the Belgian COVID-19 Pandemic on Physician-Provided Prehospital Critical Care in the City of Antwerp (Belgium)

INTRODUCTION

There is evidence to suggest that patients delayed seeking urgent medical care during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. A delay in health-seeking behavior could increase the disease severity of patients in the prehospital setting. The combination of COVID-19-related missions and augmented disease severity in the prehospital environment could result in an increase in the number and severity of physician-staffed prehospital interventions, potentially putting a strain on this highly specialized service.

STUDY OBJECTIVE

The aim was to investigate if the COVID-19 pandemic influences the frequency of physician-staffed prehospital interventions, prehospital mortality, illness severity during prehospital interventions, and the distribution in the prehospital diagnoses.

METHODS

A retrospective, multicenter cohort study was conducted on prehospital charts from March 14, 2020 through April 30, 2020, compared to the same period in 2019, in an urban area. Recorded data included demographics, prehospital diagnosis, physiological parameters, mortality, and COVID-status. A modified National Health Service (NHS) National Early Warning Score (NEWS) was calculated for each intervention to assess for disease severity. Data were analyzed with univariate and descriptive statistics.

RESULTS

There was a 31% decrease in physician-staffed prehospital interventions during the period under investigation in 2020 as compared to 2019 (2019: 644 missions and 2020: 446 missions), with an increase in prehospital mortality (OR = 0.646; 95% CI, 0.435 - 0.959). During the study period, there was a marked decrease in the low and medium NEWS groups, respectively, with an OR of 1.366 (95% CI, 1.036 - 1.802) and 1.376 (0.987 - 1.920). A small increase was seen in the high NEWS group, with an OR of 0.804 (95% CI, 0.566 - 1.140); 2019: 80 (13.67%) and 2020: 69 (16.46%). With an overall decrease in cases in all diagnostic categories, a significant increase was observed for respiratory illness (31%; P = .004) and cardiac arrest (54%; P < .001), combined with a significant decrease for intoxications (-58%; P = .007). Due to the national test strategy at that time, a COVID-19 polymerase chain reaction (PCR) result was available in only 125 (30%) patients, of which 20 (16%) were positive.

CONCLUSION

The frequency of physician-staffed prehospital interventions decreased significantly. There was a marked reduction in interventions for lower illness severity and an increase in higher illness severity and mortality. Further investigation is needed to fully understand the reasons for these changes.

Pre-hospital suPAR, lactate and CRP measurements for decision-making: a prospective, observational study of patients presenting non-specific complaints

Background

In the pre-hospital setting, non-urgent patients with non-specific chief complaints pose assessment challenges for the emergency medical systems (EMS). Severely ill patients should be identified among these patients, and unnecessary transport to the emergency department (ED) should be avoided. Unnecessary admissions burden EDs, deplete EMS resources and can even be harmful to patients, especially elderly patients. Therefore, tools for facilitating pre-hospital decision-making are needed. They could be based on vital signs or point-of-care laboratory biomarkers. In this study, we examined whether the biomarker soluble urokinase plasminogen activator receptor (suPAR), either alone or combined with C-reactive protein (CRP) and/or lactate, could predict discharge from the ED and act as a pre-hospital support tool for non-conveyance decision-making.

Methods

This was a prospective, observational study of adult patients with normal or near-normal vital signs transported by an EMS to an ED with a code referring to deteriorated general condition. The levels of suPAR, CRP and lactate in the patients’ pre-hospital blood samples were analysed. The values of hospitalized patients were compared to those of discharged patients to determine whether these biomarkers could predict direct discharge from the ED.

Results

A total of 109 patients (median age: 81 years) were included in the study. Of those, 52% were hospitalized and 48% were discharged from the ED. No statistically significant association was found between suPAR and the ED discharge vs hospitalization outcome (OR: 1.04, 95% CI 0.97–1.13, AUROC: 0.58, 95% CI 0.47–0.69). Adding CRP (AUROC: 0.64, 95% CI 0.54–0.75) or lactate (AUROC: 0.60, 95% CI 0.49–0.71) to the regression models did not improve their diagnostic accuracy. None of the patients with a suPAR value of less than 2 ng/ml were admitted to hospital, while 64% of the patients with a suPAR value of more than 6 ng/ml were hospitalized.

Conclusion

Pre-hospital suPAR measurements alone or combined with CRP and/or lactate measurements could not predict the ED discharge or hospital admission of 109 non-urgent EMS patients with non-specific chief complaints and normal or near-normal vital signs.

National Early Warning Scores and COVID-19 deaths in care homes: an ecological time-series study

OBJECTIVES

To investigate whether National Early Warning Scores (NEWS/NEWS2) could contribute to COVID-19 surveillance in care homes.

SETTING

460 care home units using the same software package to collect data on residents, from 46 local authority areas in England.

PARTICIPANTS

6464 care home residents with at least one NEWS recording.

EXPOSURE MEASURE

29 656 anonymised person-level NEWS from 29 December 2019 to 20 May 2020 with component physiological measures: systolic blood pressure, respiratory rate, pulse rate, temperature and oxygen saturation. Baseline values for each measure calculated using 80th and 20th centile scores before March 2020.

OUTCOME MEASURE

Cross-correlation comparison of time series with Office for National Statistics weekly reported registered deaths of care home residents where COVID-19 was the underlying cause of death, and all other deaths (excluding COVID-19) up to 10 May 2020.

RESULTS

Deaths due to COVID-19 were registered from 23 March 2020 in the local authority areas represented in the study. Between 23 March 2020 and 10 May 2020, there were 5753 deaths (1532 involving COVID-19 and 4221 other causes). We observed a rise in the proportion of above-baseline NEWS beginning 16 March 2020, followed 2 weeks later by an increase in registered deaths (cross-correlation of r=0.82, p<0.05 for a 2 week lag) in corresponding local authorities. The proportion of above-baseline oxygen saturation, respiratory rate and temperature measurements also increased approximately 2 weeks before peaks in deaths.

CONCLUSIONS

NEWS could contribute to COVID-19 disease surveillance in care homes during the pandemic. Oxygen saturation, respiratory rate and temperature could be prioritised as they appear to signal rise in mortality almost as well as NEWS. This study reinforces the need to collate data from care homes, to monitor and protect residents' health. Further work using individual level outcome data is needed to evaluate the role of NEWS in the early detection of resident illness.

Use of the National Early Warning Score in community nursing: a scoping review

People in the UK are living longer and with multi-morbidities, increasing the size, complexity and acuity of Community Nursing caseloads. Nurses visiting infrequently and inconsistently on a task-focused basis need an objective method by which to identify / quantify physical deterioration for early treatment avoiding crisis and hospital admission. The National Early Warning Score (NEWS), is the most recognised tool for identification of deterioration in acute settings but is not validated for community use. Using published frameworks for scoping review and evaluation, this study aims to explore the current evidence for use of NEWS in community settings. Although there is work to be done, particularly in terms of frequency of scoring and response, this study identifies benefits in communication and prioritisation of care as well as sensitivity, particularly in predicting poor outcomes. The identified barriers to use include integration into practice and perceived dissonance with clinical judgement.

Service evaluation comparing Acute Care at Home for older people service and conventional service within an acute hospital care of elderly ward

AIMS AND OBJECTIVES

This study evaluated the impact of a consultant-led Acute Care at Home service in comparison with conventional hospital admission to a care of elderly ward.

BACKGROUND

Globally, there has been an increased demand for healthcare services caused by population growth and a rise in chronic conditions and an ageing population. Acute Care at Home services offer acute, hospital-level care in a person's own home. Five services have been commissioned across Northern Ireland since 2014 with limited research investigating their feasibility and effectiveness.

DESIGN

Quantitative design, using service evaluation methodology.

METHODS

A 1-year retrospective chart review was undertaken exploring admission demographics and post-discharge clinical outcomes of patients admitted to a Northern Ireland, Care of the Elderly ward (n = 191) and a consultant-led Acute Care at Home Service (n = 314) between April 2018-March 2019. Data were analysed using descriptive and inferential data analysis methods including frequencies, independent t tests and chi-square analysis. Outcome measurements included length of stay, 30-day, 3- and 6-month readmission and mortality rates, functional ability and residence on discharge. STROBE checklist was used in reporting this study.

RESULTS

Acute Care at Home services are associated with higher readmission and mortality rates at 30 days, 3 and 6 months. Fewer patients die while under Acute Care at Home care. Patients admitted to the Acute Care at Home services experience a reduced length of stay and decreased escalation in domiciliary care packages and are less likely to require subacute rehabilitation on discharge. There is no difference in gender, age and early warnings score between the two cohorts.

CONCLUSION

The Acute Care at Home service is a viable alternative to hospital for older patients. It prevents functional decline and the need for domiciliary care or nursing home placement. It is likely that the Acute Care at Home service has higher mortality and readmissions rates due to treating a higher proportion of dependent, frail older adults.

RELEVANCE TO CLINICAL PRACTICE

Acute Care at Home services continue to evolve worldwide. This service evaluation has confirmed that Acute Care at Home services are safe and cost-effective alternatives to traditional older people hospital services. Such services offer patient choice, reduce length of stay and costs and prevent functional decline of older adults. This study accentuates the need to expand Acute Care at Home provision and capacity throughout Northern Ireland.

Time for a prehospital-modified sequential organ failure assessment score: An ambulance-Based cohort study

BACKGROUND

To adapt the Sequential Organ Failure Assessment (SOFA) score to fit the prehospital care needs; to do that, the SOFA was modified by replacing platelets and bilirubin, by lactate, and tested this modified SOFA (mSOFA) score in its prognostic capacity to assess the mortality-risk at 2 days since the first Emergency Medical Service (EMS) contact.

METHODS

Prospective, multicentric, EMS-delivery, ambulance-based, pragmatic cohort study of adults with acute diseases, referred to two tertiary care hospitals (Spain), between January 1st and December 31st, 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation cohort.

RESULTS

A total of 1114 participants comprised two separated cohorts recruited from 15 ambulance stations. The 2-day mortality rate (from any cause) was 5.9% (66 cases). The predictive validity of the mSOFA score was assessed by the calculation of the AUC of ROC in the validation cohort, resulting in an AUC of 0.946 (95% CI, 0.913-0.978, p < .001), with a positive likelihood ratio was 23.3 (95% CI, 0.32-46.2).

CONCLUSIONS

Scoring systems are now a reality in prehospital care, and the mSOFA score assesses multiorgan dysfunction in a simple and agile manner either bedside or en route. Patients with acute disease and an mSOFA score greater than 6 points transferred with high priority by EMS represent a high early mortality group.

TRIAL REGISTRATION

ISRCTN48326533, Registered Octuber 312,019, Prospectively registered (doi:https://doi.org/10.1186/ISRCTN48326533).

New Early Warning Score: EMS Off-Label Use in Out-of-Hospital Patients

Background: The National Early Warning Score (NEWS) is an assessment scale of in-hospital patients’ conditions. The purpose of this study was to assess the appropriateness of a potential off-label use of NEWS by the emergency medical system (EMS) to facilitate the identification of critical patients and to trigger appropriate care in the pre-hospital setting. Methods: A single centre, longitudinal, prospective study was carried out between July and August 2020 in the EMS service of Bologna. Home patients with age ≥ 18 years old were included in the study. The exclusion criterion was the impossibility to collect all the parameters needed to measure NEWS. Results: A total of 654 patients were enrolled in the study. The recorded NEWS values increased along with the severity of dispatch priority code, the EMS return code, the emergency department triage code, and with patients’ age (r = 0.135; p = 0.001). The aggregated value of NEWS was associated with an increased risk of hospitalization (OR = 1.30 (1.17; 1.34); p < 0.0001). Conclusion: This study showed that the use of NEWS in the urgent and emergency care services can help patient assessment while not affecting EMS crew operation and might assist decision making in terms of severity-code assignment and resources utilization.

National Early Warning Score for predicting intensive care unit admission among elderly patients with influenza infections in the emergency department: an effective disposition tool during the influenza season

OBJECTIVE

During the influenza epidemic season, the fragile elderlies are not only susceptible to influenza infections, but are also more likely to develop severe symptoms and syndromes. Such circumstances may pose a significant burden to the medical resources especially in the emergency department (ED). Disposition of the elderly patients with influenza infections to either the ward or intensive care unit (ICU) accurately is therefore a crucial issue.

STUDY DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

Elderly patients (≥65 years) with influenza visiting the ED of a medical centre between 1 January 2010 and 31 December 2015.

PRIMARY OUTCOME MEASURES

Demographic data, vital signs, medical history, subtype of influenza, national early warning score (NEWS) and outcomes (mortality) were analysed. We investigated the ability of NEWS to predict ICU admission via logistic regression and the receiver operating characteristic (ROC) analysis.

RESULTS

We included 409 geriatric patients in the ED with a mean age of 79.5 years and approximately equal sex ratio. The mean NEWS ±SD was 3.4±2.9, and NEWS ≥8 was reported in 11.0% of the total patients. Logistic regression revealed that NEWS ≥8 predicted ICU admission with an OR of 5.37 (95% CI 2.61 to 11.04). The Hosmer-Lemeshow goodness-of-fit test was calculated as 0.95, and the adjusted area under the ROC was 0.72. An NEWS ≥8 is associated with ICU-admission and may help to triage elderly patients with influenza infections during the influenza epidemic season.

CONCLUSION

The high specificity of NEWS ≥8 to predict ICU admission in elderly patients with influenza infection during the epidemic season may avoid unnecessary ICU admissions and ensure proper medical resource allocation.

Association of out of hospital paediatric early warning score with need for hospital admission in a Scottish emergency ambulance population

OBJECTIVE

Physiological derangement, as measured by paediatric early warning score (PEWS) is used to identify children with critical illness at an early point to identify and intervene in children at risk. PEWS has shown some utility as a track and trigger system in hospital and also as a predictor of adverse outcome both in and out of hospital. This study examines the relationship between prehospital observations, aggregated into an eight-point PEWS (Scotland), and hospital admission.

METHODS

A retrospective analysis of all patients aged less than 16 transported to hospital by the Scottish Ambulance Service between 2011 and 2015. Data were matched to outcome data regarding hospital admission or discharge and length of stay.

RESULTS

Full data were available for 21 202 paediatric patients, of whom 6340 (29.9%) were admitted to hospital. Prehospital PEWS Scotland was associated with an odds ratio for admission of 1.189 [95% confidence interval (CI): 1.176-1.202; P < 0.001]. The area under receiver operating curve of 0.617 (95% CI: 0.608-0.625; P < 0.001) suggests poorly predictive ability for hospital admission. There was no association between prehospital PEWS Scotland and length of hospital stay.

CONCLUSION

These data show that a single prehospital PEWS Scotland was a poor predictor of hospital admission for unselected patients in a prehospital population. The decision to admit a child to hospital is not solely based on the physiological derangement of vital signs, and hence physiological-based scoring systems such as PEWS Scotland cannot be used as the sole criteria for hospital admission, from an undifferentiated prehospital population.

Ability of prehospital NEWS to predict 1-day and 7-day mortality is reduced in the older adult patients

BACKGROUND

National Early Warning Score (NEWS) does not include age as a parameter despite age is a significant independent risk factor of death. The aim of this study was to examine whether age has an effect on predictive performance of short-term mortality of NEWS in a prehospital setting. We also evaluated whether adding age as an additional parameter to NEWS improved its short-term mortality prediction.

METHODS

We calculated NEWS scores from retrospective prehospital electronic patient record data for patients 18 years or older with sufficient prehospital data to calculate NEWS. We used area under receiver operating characteristic (AUROC) to analyse the predictive performance of NEWS for 1 and 7 day mortalities with increasing age in three different age groups: <65 years, 65-79 years and ≥80 years. We also explored the ORs for mortality of different NEWS parameters in these age groups. We added age to NEWS as an additional parameter and evaluated its effect on predictive performance.

RESULTS

We analysed data from 35 800 ambulance calls. Predictive performance for 7-day mortality of NEWS decreased with increasing age: AUROC (95% CI) for 1-day mortality was 0.876 (0.848 to 0.904), 0.824 (0.794 to 0.854) and 0.820 (0.788 to 0.852) for first, second and third age groups, respectively. AUROC for 7-day mortality had a similar trend. Addition of age as an additional parameter to NEWS improved its ability to predict short-term mortality when assessed with continuous Net Reclassification Improvement.

CONCLUSIONS

Age should be considered as an additional parameter to NEWS, as it improved its performance in predicting short-term mortality in this prehospital cohort.

Using National Early Warning Score (NEWS) 2 to help manage medical emergencies in the dental practice

If a medical emergency occurs in the dental practice, members of the dental team must be able to respond promptly, effectively and safely. Fundamental to this response is knowing when it is necessary to call 999 for an ambulance and communicating effectively with the ambulance service to ensure the timely arrival of the emergency services and timely transfer to hospital. This can be helped by using the Royal College of Physicians' (RCP's) National Early Warning Score (NEWS) 2, widely used by the ambulance service and in hospitals - it reliably detects deterioration in adults, triggering review, treatment and escalation of care. Although NEWS2 hasn't yet been validated for use in primary care, NHS England is encouraging its widespread use in this sector. Using NEWS2 in the dental practice will help the dental team to effectively, confidently and safely manage medical emergencies, including sepsis, should they arise. This will facilitate effective teamwork and help to ensure enhanced patient outcomes. This article provides an overview of NEWS2, including benefits for using it in the dental practice and guidance on how to implement it.

Do medical students recognise the deteriorating patient by analysing their vital signs? A monocentric observational study based on the National Early Warning Score 2

OBJECTIVE

Assessment of the expertise of medical students in evaluating vital signs and their implications for the current risk of a patient, an appropriate monitoring frequency, and a proper clinical response.

METHODS

251 second-year and 267 fifth-year medical students in a curriculum consisting of 6 years of medical school at Ulm University, Germany, were interviewed in a paper-based questionnaire. The students were asked to rate their proficiency in interpreting vital signs and to give pathological thresholds of vital signs. Based on the National Early Warning Score 2 (NEWS2), nine vital signs of fictional patients were created and students were asked to comment on their clinical risk, to set an appropriate monitoring frequency as well as a clinical response.

RESULTS

Interviewing medical students regarding each vital sign individually, the students indicated a pathological threshold in accordance with the NEWS2 for respiratory rate, temperature, and heart rate. By contrast, inappropriate pathological limits were given regarding oxygen saturation and systolic blood pressure. Translating the vital signs into nine fictional patients, fifth-year medical students overall chose an appropriate response in 78% (67%-78%, median±IQR). In detail, fifth-year students successfully identified patients at very high or low risk and allocated them accordingly. However, cases on the edge were often stratified inappropriately. For example, a fictional case with vital signs indicating a surging sepsis was frequently underappreciated (48.5%) and allocated to an insufficient clinical response by fifth-year students.

CONCLUSIONS

Recognising the healthy as well as the deteriorating patient is a key ability for future physicians. NEWS2-based education might be a valuable tool to assess and give feedback on student's knowledge in this vital professional activity.

A pre-hospital risk score predicts critical illness in non-trauma patients transported by ambulance to a Dutch tertiary referral hospital

Background

Early pre-hospital identification of critically ill patients reduces morbidity and mortality. To identify critically ill non-traumatic and non-cardiac arrest patients, a pre-hospital risk stratification tool was previously developed in the United States. The aim of this study was to investigate the accuracy of this tool in a Dutch Emergency Department.

Methods

This retrospective study included all patients of 18 years and older transported by ambulance to the Emergency Department of a tertiary referral hospital between January 1st 2017 and December 31st 2017. Documentation of pre-hospital vital parameters had to be available. The tool included a full set of vital parameters, which were categorized by predetermined thresholds.

Study outcome was the accuracy of the tool in predicting critical illness, defined as admittance to the Intensive Care Unit for delivery of vital organ support or death within 28 days. Accuracy of the risk stratification tool was measured with the Area Under the Receiver Operating Characteristics (AUROC) curve.

Results

Nearly 3000 patients were included in the study, of whom 356 patients (12.2%) developed critical illness. We observed moderate discrimination of the pre-hospital risk score with an AUROC of 0.74 (95%-CI 0.71–0.77). Using a threshold of 3 to identify critical illness, we observed a sensitivity of 45.0% (95%-CI 44.8–45.2) and a specificity of 86.0% (95%-CI 85.9–86.0).

Conclusion

These data show that this pre-hospital risk stratification tool is a moderately effective tool to predict which patients are likely to become critically ill in a Dutch non-trauma and non-cardiac arrest population.

SafeNET: Initial development and validation of a real-time tool for predicting mortality risk at the time of hospital transfer to a higher level of care

Background

Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer.

Methods and findings

All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance.

The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively).

Conclusions

The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland

Aim

To show whether adding blood glucose to the National Early Warning Score (NEWS) parameters in a machine learning model predicts 30-day mortality more precisely than the standard NEWS in a prehospital setting.

Methods

In this study, vital sign data prospectively collected from 3632 unselected prehospital patients in June 2015 were used to compare the standard NEWS to random forest models for predicting 30-day mortality. The NEWS parameters and blood glucose levels were used to develop the random forest models. Predictive performance on an unknown patient population was estimated with a ten-fold stratified cross-validation method.

Results

All NEWS parameters and blood glucose levels were reported in 2853 (79%) eligible patients. Within 30 days after contact with ambulance staff, 97 (3.4%) of the analysed patients had died. The area under the receiver operating characteristic curve for the 30-day mortality of the evaluated models was 0.682 (95% confidence interval [CI], 0.619-0.744) for the standard NEWS, 0.735 (95% CI, 0.679-0.787) for the random forest-trained NEWS parameters only and 0.758 (95% CI, 0.705-0.807) for the random forest-trained NEWS parameters and blood glucose. The models predicted secondary outcomes similarly, but adding blood glucose into the random forest model slightly improved its performance in predicting short-term mortality.

Conclusions

Among unselected prehospital patients, a machine learning model including blood glucose and NEWS parameters had a fair performance in predicting 30-day mortality.

National Early Warning Score 2 (NEWS2) to identify inpatient COVID-19 deterioration: a retrospective analysis

INTRODUCTION

We sought to provide the first report of the use of NEWS2 monitoring to pre-emptively identify clinical deterioration within hospitalised COVID-19 patients.

METHODS

Consecutive adult admissions with PCR-confirmed COVID-19 were included in this single-centre retrospective UK cohort study. We analysed all electronic clinical observations recorded within 28 days of admission until discharge or occurrence of a serious event, defined as any of the following: initiation of respiratory support, admission to intensive care, initiation of end of life care, or in-hospital death.

RESULTS

133/296 (44.9%) patients experienced at least one serious event. NEWS2 ≥ 5 heralded the first occurrence of a serious event with sensitivity 0.98 (95% CI 0.96-1.00), specificity 0.28 (0.21-0.35), positive predictive value (PPV) 0.53 (0.47-0.59), and negative predictive value (NPV) 0.96 (0.90-1.00). The NPV (but not PPV) of NEWS2 monitoring exceeded that of other early warning scores including the Modified Early Warning Score (MEWS) (0.59 [0.52-0.66], p<0.001) and quick Sepsis Related Organ Failure Assessment (qSOFA) score (0.58 [0.51-0.65], p<0.001).

CONCLUSION

Our results support the use of NEWS2 monitoring as a sensitive method to identify deterioration of hospitalised COVID-19 patients, albeit at the expense of a relatively high false-trigger rate.

Consensus on predictors of clinical deterioration in emergency departments: A Delphi process study

AIM

The study aim was to determine relevance and applicability of generic predictors of clinical deterioration in emergency departments based on consensus among clinicians.

METHODS

Thirty-three predictors of clinical deterioration identified from literature were assessed in a modified two-stage Delphi-process. Sixty-eight clinicians (physicians and nurses) participated in the first round and 48 in the second round; all treating hospitalized patients in Danish emergency departments, some with pre-hospital experience. The panel rated the predictors for relevance (relevant marker of clinical deterioration) and applicability (change in clinical presentation over time, generic in nature and possible to detect bedside). They rated their level of agreement on a 9-point Likert scale and were also invited to propose additional generic predictors between the rounds. New predictors suggested by more than one clinician were included in the second round along with non-consensus predictors from the first round. Final decisions of non-consensus predictors after second round were made by a research group and an impartial physician.

RESULTS

The Delphi-process resulted in 19 clinically relevant and applicable predictors based on vital signs and parameters (respiratory rate, saturation, dyspnoea, systolic blood pressure, pulse rate, abnormal electrocardiogram, altered mental state and temperature), biochemical tests (serum c-reactive protein, serum bicarbonate, serum lactate, serum pH, serum potassium, glucose, leucocyte counts and serum haemoglobin), objective clinical observations (skin conditions) and subjective clinical observations (pain reported as new or escalating, and relatives' concerns).

CONCLUSION

The Delphi-process led to consensus of 19 potential predictors of clinical deterioration widely accepted as relevant and applicable in emergency departments.

Initial Prehospital Rapid Emergency Medicine Score (REMS) as a Predictor of Patient Outcomes

Background: A standardized objective measure of prehospital patient risk of hospitalization or death is needed. The Rapid Emergency Medicine Score (REMS), a validated risk-stratification tool, has not been widely tested for prehospital use. This study's objective was to assess predictive characteristics of initial prehospital REMS for ED disposition and overall patient mortality. Methods: This retrospective analysis used linked prehospital and hospital data from the national ESO Data Collaborative. All 911 responses from 1/1/2019-12/31/2019 were included. REMS (0-26) was calculated using age and first prehospital values for: pulse rate, mean arterial pressure, respiratory rate, oxygen saturation, and Glasgow Coma Scale. Non-transports, patients <18 and cardiac arrests prior to EMS arrival were excluded. The primary outcome was ED disposition, dichotomized to discharge versus admission, transfer, or death. The secondary outcome was overall survival to discharge (ED or inpatient). Transfers and records without inpatient disposition were excluded from the secondary analysis. Predictive ability was assessed using area under the receiver operating curve (AUROC). Optimal REMS cut points were determined using test characteristic curves. Univariable logistic regression modeling was used to quantify the association between initial prehospital REMS and each outcome. Results: Of 579,505 eligible records, 94,640 (16%) were excluded due to missing data needed to calculate REMS. Overall, 62% (n = 298,223) of patients were discharged from the ED, 36% (n = 175,212) were admitted, 2% (n = 10,499) were transferred, and 0.2% (n = 931) died in the ED. A REMS of 5 or lower demonstrated optimal statistical prediction for ED discharge versus not discharged (admission/transfer/death) (AUROC: 0.68). Patients with initial prehospital REMS of 5 or lower showed a three-fold increase in odds of ED discharge (OR: 3.28, 95%CI: 3.24-3.32). Of the 457,226 patients included in overall mortality analysis, >98% (n = 450,112) survived. AUROC of initial prehospital REMS for overall mortality was 0.79. A score 7 or lower was statistically optimal for predicting survival. Initial prehospital REMS of 7 or lower was associated with a five-fold increase in odds of overall survival (OR:5.41, 95%CI:5.15-5.69). Conclusion: Initial prehospital REMS was predictive of ED disposition and overall patient mortality, suggesting value as a risk-stratification measure for EMS agencies, systems and researchers.

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.

Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

Aim of the study

The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs.

Methods

In this retrospective study, all electronic ambulance mission reports between 2008 and 2015 in a single EMS system were collected. Adult patients (≥ 18 years) were included in the analysis. Random forest models with and without blood glucose were compared to the traditional NEWS for predicting one-day mortality. A ten-fold cross-validation method was applied to train and validate the random forest models.

Results

A total of 26,458 patients were included in the study of whom 278 (1.0%) died within one day of ambulance mission. The area under the receiver operating characteristic curve for one-day mortality was 0.836 (95% CI, 0.810−0.860) for NEWS, 0.858 (95% CI, 0.832−0.883) for a random forest trained with NEWS variables only and 0.868 (0.843−0.892) for a random forest trained with NEWS variables and blood glucose.

Conclusion

A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance.

Outcome prediction value of National Early Warning Score in septic patients with community-acquired pneumonia in emergency department: A single-center retrospective cohort study

BACKGROUND

To evaluate the accuracy of National Early Warning Score (NEWS) in predicting clinical outcomes (28-day mortality, intensive care unit [ICU] admission, and mechanical ventilation use) for septic patients with community-acquired pneumonia (CAP) compared with other commonly used severity scores (CURB65, Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick SOFA [qSOFA], and Mortality in Emergency Department Sepsis [MEDS]) and admission lactate level.

METHODS

Adult patients diagnosed with CAP admitted between January 2017 and May 2019 with admission SOFA ≥2 from baseline were enrolled. Demographic characteristics were collected. The primary outcome was the 28-day mortality after admission, and the secondary outcome included ICU admission and mechanical ventilation use. Outcome prediction value of parameters above was compared using receiver operating characteristics (ROC) curves. Cox regression analyses were carried out to determine the risk factors for the 28-day mortality. Kaplan-Meier survival curves were plotted and compared using optimal cut-off values of qSOFA and NEWS.

RESULTS

Among the 340 enrolled patients, 90 patients were dead after a 28-day follow-up, 62 patients were admitted to ICU, and 84 patients underwent mechanical ventilation. Among single predictors, NEWS achieved the largest area under the receiver operating characteristic (AUROC) curve in predicting the 28-day mortality (0.861), ICU admission (0.895), and use of mechanical ventilation (0.873). NEWS+lactate, similar to MEDS+lactate, outperformed other combinations of severity score and admission lactate in predicting the 28-day mortality (AUROC 0.866) and ICU admission (AUROC 0.905), while NEWS+lactate did not outperform other combinations in predicting mechanical ventilation (AUROC 0.886). Admission lactate only improved the predicting performance of CURB65 and qSOFA in predicting the 28-day mortality and ICU admission.

CONCLUSIONS

NEWS could be a valuable predictor in septic patients with CAP in emergency departments. Admission lactate did not predict well the outcomes or improve the severity scores. A qSOFA ≥2 and a NEWS ≥9 were strongly associated with the 28-day mortality, ICU admission, and mechanical ventilation of septic patients with CAP in the emergency departments.

Febrile neutropenia in patients with acute myeloid leukemia: Outcome in relation to qSOFA score, C-reactive protein, and blood culture findings

OBJECTIVES

To evaluate quick Sequential Organ Failure Assessment (qSOFA) score during febrile neutropenia (FN) in adult patients receiving intensive chemotherapy for acute myeloid leukemia (AML).

METHODS

qSOFA score, as well as the association of qSOFA score with ICU admission, infectious mortality, blood culture findings, and C-reactive protein (CRP) measurements during FN were assessed among 125 adult AML patients with 355 FN periods receiving intensive chemotherapy in a tertiary care hospital from November 2006 to December 2018.

RESULTS

The multivariate model for qSOFA score ≥ 2 included CRP ≥ 150 mg/L on d0-2 [OR 2.9 (95% CI 1.1-7.3), P = .026], Gram-negative bacteremia [OR 2.7 (95% CI 1.1-6.9), P = .034], and treatment according to AML-2003 vs more recent protocols [OR 2.7 (95% CI 1.0-7.4), P = .047]. Age or gender did not gain significance in the model. qSOFA score ≥ 2 was associated with ICU treatment and infectious mortality during FN with sensitivity and specificity of 0.700 and 0.979, and 1.000 and 0.971, respectively.

CONCLUSION

qSOFA offers a useful tool to evaluate the risk of serious complications in AML patients during FN.

The Value of Prehospital Early Warning Scores to Predict in - Hospital Clinical Deterioration: A Multicenter, Observational Base-Ambulance Study

OBJECTIVES

Early warning scores are clinical tools capable of identifying prehospital patients with high risk of deterioration. We sought here to contrast the validity of seven early warning scores in the prehospital setting and specifically, to evaluate the predictive value of each score to determine early deterioration-risk during the hospital stay, including mortality at one, two, three and seven- days since the index event. Methods: A prospective multicenter observational based-ambulance study of patients treated by six advanced life support emergency services and transferred to five Spanish hospitals between October 1, 2018 and December 31, 2019. We collected demographic, clinical, and laboratory variables. Seven risk score were constructed based on the analysis of prehospital variables associated with death within one, two, three and seven days since the index event. The area under the receiver operating characteristics was used to determine the discriminant validity of each early warning score. Results: A total of 3,273 participants with acute diseases were accurately linked. The median age was 69 years (IQR, 54-81 years), 1,348 (41.1%) were females. The overall mortality rate for patients in the study cohort ranged from 3.5% for first-day mortality (114 cases), to 7% for seven-day mortality (228 cases). The scores with the best performances for one-day mortality were Vitalpac Early Warning Score with an area under the receiver operating characteristic (AUROC) of 0.873 (95% CI: 0.81-0.9), for two-day mortality, Triage Early Warning Score with an AUROC of 0.868 (95% CI: 0.83-0.9), for three and seven-days mortality the Modified Rapid Emergency Medicine Score with an AUROC of 0.857 (0.82-0.89) and 0.833 (95% CI: 0.8-0.86). In general, there were no significant differences between the scores analyzed. Conclusions: All the analyzed scores have a good predictive capacity for early mortality, and no statistically significant differences between them were found. The National Early Warning Score 2, at the clinical level, has certain advantages. Early warning scores are clinical tools that can help in the complex decision-making processes during critical moments, so their use should be generalized in all emergency medical services.