NEWS 2 - too little evidence to implement?

Prognostic value of early warning scores in patients presenting to the emergency department with exacerbation of COPD

OBJECTIVE

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a condition that frequently presents to the emergency department (ED) and its prognosis is not very well understood. Risk tools that can be used rapidly in the ED are needed to predict the prognosis of these patients.

METHODS

This study comprised a retrospective cohort of AECOPD patients presenting to a single center between 2015 and 2022. The prognostic accuracy of several clinical early warning scoring systems, Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), NEWS‑2, Systemic Inflammatory Response Syndrome (SIRS) and the quick Sepsis-related Organ Failure Assessment (qSOFA), were compared. The outcome variable was determined as one-month mortality.

RESULTS

Of the 598 patients, 63 (10.5%) had died within 1 month after presenting to the ED. Patients who died had more often congestive heart failure, altered mental status, and admission to intensive care, and they were older. Although the MEWS, NEWS, NEWS‑2, and qSOFA scores of those who died were higher than those who survived, there was no difference between the SIRS scores of these two groups. The score with the highest positive likelihood ratio for mortality estimation was qSOFA (8.5, 95% confidence interval [CI] 3.7-19.6). The negative likelihood ratios of the scores were similar, the NEWS score had a negative likelihood ratio of 0.4 (95% CI 0.2-0.8) with the highest negative predictive value of 96.0%.

CONCLUSION

In AECOPD patients, most of the early warning scores that are frequently used in the ED were found to have a moderate ability to exclude mortality and a low ability to predict mortality.

Evaluation of prognostic scoring systems in patients hospitalized from the emergency department in a low-income region: northern Syria after internal turmoil as a different universe

BACKGROUND

In low-income or underdeveloped countries with conflict and internal unrest, healthcare facilities and staff are limited. For these reasons, it is necessary to use the most straightforward scoring systems to ensure that health facilities and staff are used effectively and to expedite processes through early and effective interventions for patients. In this study, we evaluate and compare the scoring systems used to predict patient prognosis for Emergency Department (ED) patients in northern Syria, which is an area marred by conflict and internal unrest.

METHODS

In this study, patients hospitalized in the Afrin, Azez Vatan, Jarablus, Tel Abyad, Rasulayn, El Bab, and Çobanbey hospitals in northern Syria were investigated. Only patients that were hospitalized in the emergency departments of these hospitals, including wards and intensive care units, were included in the study. Patients that were hospitalized from 03/01/2021 to 08/31/2021, the study period, were prospectively analyzed. Vital signs, medical histories and demographic data of the patients were recorded by calculating National Early Warning Score 2 (NEWS2), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and HOTEL Score (hypotension, oxygen saturation, low temperature, electrocardiogram, loss of independence). Acceptance parameters and scores were analyzed using statistical methods and by comparing groups.

RESULTS

: All four scoring systems were found to be effective in predicting mortality regarding ROC curve analysis. However, the statistical significance of the RAPS was slightly stronger than that of the other scores and REMS had the highest sensitivity and specificity amongst the four systems, at 86.2% and 84.1%, respectively. Regarding the risk of hospitalization in the ICU (p < 0.05), the sensitivity values of the cut-off values offered by the scoring systems remained below 0.70 regarding ROC curve analysis. RAPS had the highest sensitivity (65.2%) of the four systems with a cut-off value of 1.5.

DISCUSSION

This study in northern Syria has shown that although RAPS had stronger statistical power, REMS had better sensitivity and specificity for the prediction of mortality. Additionally, RAPS had better sensitivity for ICU risk. This study will contribute to the evaluation of healthcare in similar regions and to cost-effective healthcare delivery by using scoring systems for ED patients' admission.

NEWS2, patient safety and hypercapnic respiratory failure

The National Early Warning Score (NEWS) has been widely adopted for use in clinical practice in the UK since its introduction in 2012. It is designed to improve patient safety. The original score was adapted in 2017 to improve patient safety further by introducing a separate score for oxygen saturation to be used in selected patients with respiratory diseases. In this article, evidence for the effectiveness of the improved score is reviewed.

Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies

BACKGROUND

Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2).

METHODS

RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people.

FINDINGS

Data were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76-0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1-99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2 at rest (as a percentage); the area under the curve was 0·84 (0·78-0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9-99·7; 1176 of 1183).

INTERPRETATION

Both RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2 is available, RECAP-O2 is useful to assess the need for treatment escalation.

FUNDING

Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK.

Ability of the National Early Warning Score and its respiratory and haemodynamic subcomponents to predict short-term mortality on general wards: a prospective three-centre observational study in Finland

OBJECTIVES

To validate the ability of the National Early Warning Score (NEWS) to predict short-term mortality on hospital wards, with a special reference to the NEWS's respiratory and haemodynamic subcomponents.

DESIGN

A large, 1-year, prospective, observational three-centre study. First measured vital sign datasets on general wards were prospectively collected using a mobile solution system during routine patient care. Area under receiver operator characteristic curves were constructed, and comparisons between ROC curves were conducted with Delong's test for two correlated ROC curves.

SETTING

One university hospital and two regional hospitals in Finland.

PARTICIPANTS

All 19 001 adult patients admitted to 45 general wards in the three hospitals over the 1-year study period. After excluding 102/19 001 patients (0.53%) with data on some vital signs missing, the final cohort consisted of 18 889 patients with full datasets.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome measure was 1-day mortality and secondary outcomes were 2-day and 30-day mortality rates.

RESULTS

Patients' median age was 70 years, 51% were male and 31% had a surgical reason for admission. The 1-day mortality was 0.36% and the 30-day mortality was 3.9%. The NEWS discriminated 1-day non-survivors with excellent accuracy (AUROC 0.91, 95% CI 0.87 to 0.95) and 30-day mortality with acceptable accuracy (0.75, 95% CI 0.73 to 0.77). The NEWS's respiratory rate component discriminated 1-day non-survivors better (0.78, 95% CI 0.72 to 0.84) as compared with the oxygen saturation (0.66, 95% CI 0.59 to 0.73), systolic blood pressure (0.65, 95% CI 0.59 to 0.72) and heart rate (0.67, 95% CI 0.61 to 0.74) subcomponents (p<0.01 in all ROC comparisons). As with the total NEWS, the discriminative performance of the individual score components decreased substantially for the 30-day mortality.

CONCLUSIONS

NEWS discriminated general ward patients at risk for acute death with excellent statistical accuracy. The respiratory rate component is especially strongly associated with short-term mortality.

TRIAL REGISTRATION NUMBER

NCT04055350.

“NEWS2” as an Objective Assessment of Hospitalised COPD Exacerbation Severity

Introduction

There is currently no accepted way to risk-stratify hospitalised exacerbations of chronic obstructive pulmonary disease (COPD). We hypothesised that the revised UK National Early Warning Score (NEWS2) calculated at admission would predict inpatient mortality, need for non-invasive ventilation (NIV) and length-of-stay.

Methods

We included data from 52,284 admissions for exacerbation of COPD. Data were divided into development and validation cohorts. Logistic regression was used to examine relationships between admission NEWS2 and outcome measures. Predictive ability of NEWS2 was assessed using area under receiver operating characteristic curves (AUC). We assessed the benefit of including other baseline data in the prediction models and assessed whether these variables themselves predicted admission NEWS2.

Results

53% of admissions had low risk, 24% medium risk and 23% a high risk NEWS2 in the development cohort. The proportions dying as an inpatient were 2.2%, 3.6% and 6.5% by NEWS2 risk category, respectively. The proportions needing NIV were 4.4%, 9.2% and 18.0%, respectively. NEWS2 was poorly predictive of length-of-stay (AUC: 0.59[0.57–0.61]). In the external validation cohort, the AUC (95% CI) for NEWS2 to predict inpatient death and need for NIV were 0.72 (0.68–0.77) and 0.70 (0.67–0.73). Inclusion of patient demographic factors, co-morbidity and COPD severity improved model performance. However, only 1.34% of the variation in admission NEWS2 was explained by these baseline variables.

Conclusion

The generic NEWS2 risk assessment tool, readily calculated from simple physiological data, predicts inpatient mortality and need for NIV (but not length-of-stay) at exacerbations of COPD. NEWS2 therefore provides a classification of hospitalised COPD exacerbation severity.

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.

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data

The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission and readmission from time-series of vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked ensemble platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction by incorporating static features. The model is used to assess a patient's risk of adversity and provides visual justifications of its prediction. Results of three case studies show that the model outperforms existing platforms in ICU and general ward settings, achieving average Precision-Recall Areas Under the Curve (PR-AUCs) of 0.891 (95% CI: 0.878-0.939) for mortality and 0.908 (95% CI: 0.870-0.935) in predicting ICU admission and readmission.

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.

Can the prehospital National Early Warning Score 2 identify patients at risk of in-hospital early mortality? A prospective, multicenter cohort study

BACKGROUND

The National Early Warning Score 2 (NEWS2) scores can help identify clinical deterioration.

OBJECTIVE

To assess the predictive capacity of the NEWS2 at prehospital level for the detection of early mortality in the hospital.

METHODS

Prospective multicenter cohort study, in which we compiled a database of observed vital signs between March 1, 2018 and May 30, 2019. We collected demographic data, vital signs (respiration rate, oxygen saturation, supplemental oxygen, temperature, systolic blood pressure, heart rate and level of consciousness), prehospital diagnosis and hospital mortality data. We calculated the AUROC of the NEWS2 for early mortality.

RESULTS

We included a total of 2335 participants. Median age was 69 years (IQR 54-81 years). The AUC for mortality within one day was 0.862 (95%CI:0.78-0.93), within two days 0.885 (95%CI:0.84-0.92) and within seven days 0.835 (95%CI:0.79-0.87) (in all cases, p<0.001).

CONCLUSIONS

The NEWS2 performed at prehospital level is a bedside tool for predicting early hospital mortality.

Development of the National Early Warning Score-Calcium Model for Predicting Adverse Outcomes in Patients With Acute Pancreatitis

INTRODUCTION

This study aimed to develop a new model on the basis of the National Early Warning Score to predict intensive care unit admission and the mortality of patients with acute pancreatitis.

METHODS

Patients diagnosed with acute pancreatitis in the emergency department were enrolled. The values of the National Early Warning Score, Modified Early Warning Score, and Bedside Index of Severity in Acute Pancreatitis in predicting intensive care unit admission and mortality of patients with acute pancreatitis were evaluated.

RESULTS

A total of 379 patients with acute pancreatitis were enrolled; 77 patients (20.3%) were admitted to the intensive care unit and 14 (3.7%) died. The National Early Warning Score and calcium level were identified as independent risk factors of intensive care unit admission. Serum calcium exhibited a moderate correlation with National Early Warning Score (r = -0.46; P < 0.001), Modified Early Warning Score (r = -0.37; P < 0.001), and Bedside Index of Severity in Acute Pancreatitis (r = -0.39; P < 0.001). A new model called National Early Warning Score-calcium was developed by combining National Early Warning Score and calcium blood test result, which had larger areas under the curve for predicting intensive care unit admission and mortality than the other 3 scoring systems.

DISCUSSION

A new model developed by combining National Early Warning Score and calcium exhibited better value in predicting the prognosis of acute pancreatitis than the models involving National Early Warning Score, Modified Early Warning Score, and Bedside Index of Severity in Acute Pancreatitis alone.

Gram-negative bloodstream infections and sepsis: risk factors, screening tools and surveillance

INTRODUCTION AND BACKGROUND

Incidence of gram-negative bloodstream infections (GNBSIs) and sepsis are rising in the UK. Healthcare-associated risk factors have been identified that increase the risk of infection and associated mortality. Current research is focused on identifying high-risk patients and improving the methods used for surveillance.

SOURCES OF DATA

Comprehensive literature search of the topic area using PubMed (Medline). Government, professional and societal publications were also reviewed.

AREAS OF AGREEMENT

A range of healthcare-associated risk factors independently associate with the risk of GNBSIs and sepsis.

AREAS OF CONTROVERSY

There are calls to move away from using simple comorbidity scores to predict the risk of sepsis-associated mortality, instead more advanced multimorbidity models should be considered.

GROWING POINTS AND AREAS FOR DEVELOPING RESEARCH

Advanced risk models should be created and evaluated for their ability to predict sepsis-associated mortality. Investigations into the accuracy of NEWS2 to predict sepsis-associated mortality are required.

Impact of using data from electronic protocols in nursing performance management: A qualitative interview study

Aim

To explore the impact of using electronic data in performance management to improve nursing compliance with a protocol.

Background

Electronic data are increasingly used to monitor protocol compliance but little is known about the impact on nurses’ practice in hospital wards.

Method

Seventeen acute hospital nursing staff participated in semi‐structured interviews about compliance with an early warning score (EWS) protocol delivered by a bedside electronic handheld device.

Results

Before electronic EWS data was used to monitor compliance, staff combined protocol‐led actions with clinical judgement. However, some observations were missed to reduce noise and disruption at night. After compliance monitoring was introduced, observations were sometimes covertly omitted using a loophole. Interviewees described a loss of autonomy but acknowledged the EWS system sometimes flagged unexpected patient deterioration.

Conclusions

Introducing automated electronic systems to support nursing tasks can decrease nursing burden but remove the ability to record legitimate reasons for missing observations. This can result in covert resistance that could reduce patient safety.

Implications for nursing management

Providing the ability to log legitimate reasons for missing observations would allow nurses to balance professional judgement with the use of electronic data in performance management of protocol compliance.

Comparison of early warning scores in patients with COPD exacerbation: DECAF and NEWS score

Background

The National Early Warning Score 2 (NEWS2) includes two oxygen saturation scales; the second adjusts target saturations to 88%–92% for those with hypercapnic respiratory failure. Using this second scale in all patients with COPD exacerbation (‘NEWS2_{All COPD}’) would simplify practice, but the impact on alert frequency and prognostic performance is unknown. Admission NEWS2 score has not been compared with DECAF (dyspnoea, eosinopenia, consolidation, acidaemia, atrial fibrillation) for inpatient mortality prediction.

Methods

NEWS, NEWS2 and NEWS2_{All COPD} and DECAF were calculated at admission in 2645 patients with COPD exacerbation attending consecutively to one of six UK hospitals, all of whom met spirometry criteria for COPD. Alert frequency and appropriateness were assessed for all NEWS iterations. Prognostic performance was compared using the area under the receiver operating characteristic (AUROC) curve. Missing data were imputed using multiple imputation.

Findings

Compared with NEWS, NEWS2 reclassified 3.1% patients as not requiring review by a senior clinician (score≥5). NEWS2_{All COPD} reduced alerts by 12.6%, or 16.1% if scoring for injudicious use of oxygen was exempted. Mortality was low in reclassified patients, with no patients dying the same day as being identified as low risk. NEWS2_{All COPD} was a better prognostic score than NEWS (AUROC 0.72 vs 0.65, p<0.001), with similar performance to NEWS2 (AUROC 0.72 vs 0.70, p=0.090). DECAF was superior to all scores (validation cohort AUROC 0.82) and offered a more clinically useful range of risk stratification (DECAF=1.2%–25.5%; NEWS2=3.5%–15.4%).

Conclusion

NEWS2_{All COPD} safely reduces the alert frequency compared with NEWS2. DECAF offers superior prognostic performance to guide clinical decision-making on admission, but does not replace repeated measures of NEWS2 during hospitalisation to detect the deteriorating patient.

A review of early warning systems for prompt detection of patients at risk for clinical decline

Early Warning Scores (EWS) are a composite evaluation of a patient's basic physiology, changes of which are the first indicators of clinical decline and are used to prompt further patient assessment and when indicated intervention. These are sometimes referred to as "track and triggers systems" with tracking meant to denote periodic observation of physiology and trigger being a predetermined response criteria. This review article examines the most widely used EWS, with special attention paid to those used in military and trauma populations.The earliest EWS is the Modified Early Earning Score (MEWS). In MEWS, points are allocated to vital signs based on their degree of abnormality, and summed to yield an aggregate score. A score above a threshold would elicit a clinical response such as a rapid response team. Modified Early Earning Score was subsequently followed up with the United Kingdom's National Early Warning Score, the electronic cardiac arrest triage score, and the 10 Signs of Vitality score, among others.Severity of illness indicators have been in military and civilian trauma populations, such as the Revised Trauma Score, Injury Severity Score, and Trauma and Injury Severity. The sequential organ failure assessment score and its attenuated version quick sequential organ failure assessment were developed to aggressively identify patients near septic shock.Effective EWS have certain characteristics. First, they should accurately capture vital signs information. Second, almost all data should be derived electronically rather than manually. Third, the measurements should take into consideration multiple organ systems. Finally, information that goes into an EWS must be captured in a timely manner. Future trends include the use of machine learning to detect subtle changes in physiology and the inclusion of data from biomarkers. As EWS improve, they will be more broadly used in both military and civilian environments. LEVEL OF EVIDENCE: Review article, level I.

The inclusion of delirium in version 2 of the National Early Warning Score will substantially increase the alerts for escalating levels of care: findings from a retrospective database study of emergency medical admissions in two hospitals

BACKGROUND

The National Early Warning Score (NEWS) is being replaced with NEWS2 which adds 3 points for new confusion or delirium. We estimated the impact of adding delirium on the number of medium/high level alerts that are triggers to escalate care.

METHODS

Analysis of emergency medical admissions in two acute hospitals (York Hospital (YH) and Northern Lincolnshire and Goole NHS Foundation Trust hospitals (NH)) in England. Twenty per cent were randomly assigned to have delirium.

RESULTS

The number of emergency admissions (YH: 35584; NH: 35795), mortality (YH: 5.7%; NH: 5.5%), index NEWS (YH: 2.5; NH: 2.1) and numbers of NEWS recorded (YH: 879193; NH: 884072) were similar in each hospital. The mean number of patients with medium level alerts per day increased from 55.3 (NEWS) to 69.5 (NEWS2), a 25.7% increase in YH and 64.1 (NEWS) to 77.4 (NEWS2), a 20.7% increase in NH. The mean number of patients with high level alerts per day increased from 27.3 (NEWS) to 34.4 (NEWS2), a 26.0% increase in YH and 29.9 (NEWS) to 37.7 (NEWS2), a 26.1% increase in NH.

CONCLUSIONS

The addition of delirium in NEWS2 will have a substantial increase in medium and high level alerts in hospitalised emergency medical patients. Rigorous evaluation of NEWS2 is required before widespread implementation because the extent to which staff can cope with this increase without adverse consequences remains unknown.