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.

Recognition of Critically Ill Patients by Acute Healthcare Providers: A Multicenter Observational Study.

OBJECTIVES: Although the Modified Early Warning Score (MEWS) is increasingly being used in the acute care chain to recognize disease severity, its superiority compared with clinical gestalt remains unproven. Therefore, the aim of this study was to compare the accuracy of medical caregivers and MEWS in predicting the development of critical illness. DESIGN: This was a multicenter observational prospective study. SETTING: It was performed in a level-1 trauma center with two different sites and emergency departments (EDs) with a combined capacity of about 50.000 patients annually. PATIENTS: It included all adult patients presented to the ED by Emergency Medical Services (EMS). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: For all patients, the acute caregivers were asked several standardized questions regarding clinical predicted outcome (clinical gestalt), and the MEWS was calculated. The primary outcome was the occurrence of critical illness, defined as ICU admission, serious adverse events, and mortality within 72 hours. The sensitivity, specificity, and discriminative power of both clinical gestalt and MEWS for the occurrence of critical illness were calculated as the area under the receiver operating characteristic curve (AUROC). Among the total of 800 included patients, 113 patients (14.1%) suffered from critical illness. The specificity for predicting three-day critical illness for all caregivers (for EMS nurses, ED nurses, and physicians) was 93.2%; 97.3%, and 96.8%, respectively, and was significantly ( p < 0.01) better than an MEWS score of 3 or higher (70.4%). The sensitivity was significantly lower for EMS and ED nurses, but not significantly different for physicians compared with MEWS. The AUROCs for prediction of 3-day critical illness by both the ED nurses (AUROC = 0.809) and the physicians (AUROC = 0.848) were significantly higher ( p = 0.032 and p = 0.010, respectively) compared with MEWS (AUROC = 0.731). CONCLUSIONS: For patients admitted to the ED by EMS, medical professionals can predict the development of critical illness within 3 days significantly better than the MEWS. Although MEWS is able to correctly predict those patients that become critically ill, its use leads to overestimation due to a substantial number of false positives.

Comparing complaint-based triage scales and early warning scores for emergency department triage.

BACKGROUND: Emergency triage systems are used globally to prioritise care based on patients' needs. These systems are commonly based on patient complaints, while the need for timely interventions on regular hospital wards is usually assessed with early warning scores (EWS). We aim to directly compare the ability of currently used triage scales and EWS scores to recognise patients in need of urgent care in the ED. METHODS: We performed a retrospective, single-centre study on all patients who presented to the ED of a Dutch Level 1 trauma centre, between 1 September 2018 and 24 June 2020 and for whom a Netherlands Triage System (NTS) score as well as a Modified Early Warning Score (MEWS) was recorded. The performance of these scores was assessed using surrogate markers for true urgency and presented using bar charts, cross tables and a paired area under the curve (AUC). RESULTS: We identified 12 317 unique patient visits where NTS and MEWS scores were documented during triage. A paired comparison of the AUC of these scores showed that the MEWS score had a significantly better AUC than the NTS for predicting the need for hospital admission (0.65 vs 0.60; p<0.001) or 30-day all-cause mortality (0.70 vs 0.60; p<0.001). Furthermore, when non-urgent MEWS scores co-occur with urgent NTS scores, the MEWS score seems to more accurately capture the urgency level that is warranted. CONCLUSIONS: The results of this study suggest that EWSs could potentially be used to replace the current emergency triage systems.

Performance of early warning and risk stratification scores versus clinical judgement in the acute setting: a systematic review.

OBJECTIVE: Risk stratification is increasingly based on Early Warning Score (EWS)-based models, instead of clinical judgement. However, it is unknown how risk-stratification models and EWS perform as compared with the clinical judgement of treating acute healthcare providers. Therefore, we performed a systematic review of all available literature evaluating clinical judgement of healthcare providers to the use of risk-stratification models in predicting patients' clinical outcome. METHODS: Studies comparing clinical judgement and risk-stratification models in predicting outcomes in adult patients presenting at the ED were eligible for inclusion. Outcomes included the need for intensive care unit (ICU) admission; severe adverse events; clinical deterioration and mortality. Risk of bias among the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. RESULTS: Six studies (6419 participants) were included of which 4 studies were judged to be at high risk of bias. Only descriptive analysis was performed as a meta-analysis was not possible due to few included studies and high clinical heterogeneity. The performance of clinical judgement and risk-stratification models were both moderate in predicting mortality, deterioration and need for ICU admission with area under the curves between 0.70 and 0.89. The performance of clinical judgement did not significantly differ from risk-stratification models in predicting mortality (n=2 studies) or deterioration (n=1 study). However, clinical judgement of healthcare providers was significantly better in predicting the need for ICU admission (n=2) and severe adverse events (n=1 study) as compared with risk-stratification models. CONCLUSION: Based on limited existing data, clinical judgement has greater accuracy in predicting the need for ICU admission and the occurrence of severe adverse events compared with risk-stratification models in ED patients. However, performance is similar in predicting mortality and deterioration. PROSPERO REGISTRATION NUMBER: CRD42020218893.