Emergency Department Triage Early Warning Score (TREWS) predicts in-hospital mortality in the emergency department

Comparison of Performance Characteristics in Early Warning Scoring Tools for Diagnosis of Intubation and Mortality Among COVID-19 Patients

INTRODUCTION

Early warning scores serve as valuable tools for predicting adverse events in patients. This study aimed to compare the diagnostic performance of National Early Warning Score, Hamilton Early Warning Score, Standardized Early Warning Score, and Triage Early Warning Score in forecasting intubation and mortality among patients with coronavirus disease 2019.

METHODS

This predictive correlation study included 370 patients admitted to the emergency department of 22 Bahman Hospital in Neyshabur, Iran, from December 2021 to March 2022. The aforementioned scores were assessed daily upon patient admission and throughout a 1-month hospitalization period, alongside intubation and mortality occurrences. Data analysis used SPSS 26 and MEDCALC 20.0.13 software. We adhered to the Standards for Reporting of Diagnostic Accuracy Studies guidelines to ensure the accurate reporting of our study.

RESULTS

The patients' mean age was 65.03 ± 18.47 years, with 209 (56.5%) being male. Both Standardized Early Warning Score and Hamilton Early Warning Score demonstrated high diagnostic performance, with area under the curve values of 0.92 and 0.95, respectively. For Standardized Early Warning Score, the positive likelihood ratio was 10.81 for intubation and 17.90 for mortality, whereas for Hamilton Early Warning Score, the positive likelihood ratio was 7.88 for intubation and 10.40 for mortality. The negative likelihood ratio values were 0.23 and 0.17 for Standardized Early Warning Score and 0.21 and 0.18 for Hamilton Early Warning Score, respectively, for the 24-hour period preceding intubation events and mortality.

DISCUSSION

Findings suggest that Standardized Early Warning Score, followed by Hamilton Early Warning Score, has superior diagnostic performance in predicting intubation and mortality in patients with coronavirus disease 2019 within 24 hours before these outcomes. Therefore, serial assessments of Hamilton Early Warning Score or Standardized Early Warning Score may be valuable tools for health care providers in identifying high-risk patients with coronavirus disease 2019 who require intubation or are at increased risk of mortality.

Addition of the clinical frailty scale to triage tools and early warning scores improves mortality prognostication at 30 days: A prospective observational multicenter study

Objectives

Frailty, assessed with clinical frailty scale (CFS), alone or in combination with aggregated vital signs, has been proposed as a measure to better predict mortality of older patients in the emergency department (ED), but the added predictive value to conventional triage is unclear.

Methods

This was a secondary analysis of a prospective observational study in three EDs in Sweden that evaluated the prognostic performance of the CFS alone or in combination with the national early warning score (NEWS), triage early warning score (TEWS) or the rapid emergency triage and treatment system (RETTS) triage tool using logistic regression. The primary outcome was 30-day mortality with 7- and 90-day mortality and admission as secondary outcomes reported as area under the receiver operating curve (AuROC) scores with 95% confidence intervals (CIs). The sensitivity, specificity, accuracy, predictive values, and likelihood ratios are reported for all models.

Results

A total of 1832 patients were included with 17 (0.9%), 57 (3.1%), and 121 (6.6%) patients dying within 7, 30, and 90 days, respectively. The admission rate was 43% (795/1832). Frailty (CFS > 4) was significantly associated with 30-day mortality (odds ratio 6, 95% CI 3‒12, p < 0.01). Prognostication of 30-day mortality was similar for all CFS-based models and better compared with models without CFS. The AuROC (95% CI) improved for RETTS from 0.67 (0.61‒0.74) to 0.83 (0.79‒0.88) (p = 0.008), for NEWS from 0.53 (0.45‒0.61) to 0.82 (0.77‒0.87) (p < 0.001), and for TEWS from 0.63 (0.55‒0.71) to 0.82 (0.77‒0.87) (p = 0.002).

Conclusion

Frailty measured with the CFS in combination with RETTS or structured vital sign assessment using NEWS or TEWS was better at prognosticating 30-day mortality compared to RETTS or early warnings score alone. Improved prognostication provides more realistic expectations and allows for informed discussions with patients and initiation of individualized treatment plans early in the ED process.

Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models

OBJECTIVES

Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle symptoms in critical patients, thus leading to delayed treatment. Unlike previous rivalry-focused approaches, our study aimed to establish a collaborative machine learning (ML) model that renders risk scores for severe illness, which may assist the triage staff to provide a better patient stratification for timely critical cares.

METHODS

This retrospective study was conducted at a tertiary teaching hospital. Data were collected from January 2015 to October 2022. Demographic and clinical information were collected at triage. The study focused on severe illness as the outcome. We developed artificial neural network (ANN) models, with or without utilizing the Taiwan Triage and Acuity Scale (TTAS) score as one of the predictors. The model using the TTAS score is termed a machine-human collaborative model (ANN-MH), while the model without it is referred to as a machine-only model (ANN-MO). The predictive power of these models was assessed using the area under the receiver-operating-characteristic (AUROC) and the precision-recall curves (AUPRC); their sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score were compared.

RESULTS

The study analyzed 668,602 ED visits from 2015 to 2022. Among them, 278,724 visits from 2015 to 2018 were used for model training and validation, while 320,201 visits from 2019 to 2022 were for testing model performance. Approximately 2.6% of visits were by severely ill patients, whose TTAS scores ranged from 1 to 5. The ANN-MH model achieved a testing AUROC of 0.918 and AUPRC of 0.369, while for the ANN-MO model the AUROC and AUPRC were 0.909 and 0.339, respectively. Based on these metrics, the ANN-MH model outperformed the ANN-MO model, and both surpassed human triage classification. Subgroup analyses further highlighted the models' capability to identify higher-risk patients within the same triage level.

CONCLUSIONS

The traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk stratification, thereby promoting patient safety.

Triage body temperature and its influence on patients with acute myocardial infarction

BACKGROUND

Fever can occur after acute myocardial infarction (MI). The influence of body temperature (BT) after hospital arrival on patients with acute MI has rarely been investigated.

METHODS

Patients who were diagnosed with acute MI in the emergency department (ED) of a tertiary teaching hospital between 1 January 2020 and 31 December 2020 were enrolled. Based on the tympanic temperature obtained at the ED triage, patients were categorized into normothermic (35.5°C-37.5°C), hypothermic (< 35.5°C), or hyperthermic (> 37.5°C) groups. The primary outcome was in-hospital cardiac arrest (IHCA), while the secondary outcomes were adverse events. Statistical significance was set at p < 0.05.

RESULTS

There were 440 enrollees; significant differences were found among the normothermic (n = 369, 83.9%), hypothermic (n = 27, 6.1%), and hyperthermic (n = 44, 10.0%) groups in the triage respiratory rate (median [IQR]) (20.0 [4.0] cycles/min versus 20.0 [4.0] versus 20.0 [7.5], p = 0.009), triage heart rate (88.0 [29.0] beats/min versus 82.0 [28.0] versus 102.5 [30.5], p < 0.001), presence of ST-elevation MI (42.0% versus 66.7% versus 31.8%, p = 0.014), need for cardiac catheterization (87.3% versus 85.2% versus 72.7%, p = 0.034), initial troponin T level (165.9 [565.2] ng/L versus 49.1 [202.0] versus 318.8 [2002.0], p = 0.002), peak troponin T level (343.8 [1405.9] ng/L versus 218.7 [2318.2] versus 832.0 [2640.8], p = 0.003), length of ICU stay (2.0 [3.0] days versus 3.0 [8.0] versus 3.0 [9.5], p = 0.006), length of hospital stay (4.0 [4.5] days versus 6.0 [15.0] versus 10.5 [10.8], p < 0.001), and infection during hospitalization (19.8% versus 29.6% versus 63.6%, p < 0.001) but not in IHCA (7.6% versus 14.8% versus 11.4%, p = 0.323) or any adverse events (50.9% versus 48.1% versus 63.6%, p = 0.258). Multivariable analysis showed no significant association of triage BT with IHCA or any major complication.

CONCLUSION

Triage BT did not show a significant association with IHCA or adverse events in patients with acute MI. However, triage BT could be associated with different clinical presentations and should warrant further investigation.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.

The prediction of 24-h mortality by the respiratory rate and oxygenation index compared with National Early Warning Score in emergency department patients: an observational study

BACKGROUND

The ROX index combines respiratory rate and oxygenation to predict the response to oxygen therapy in pneumonia. It is calculated by dividing the patient's oxygen saturation, by the inspired oxygen concentration, and then by the respiratory rate (e.g. 95%/0.21/16 = 28). Since this index includes the most essential physiological variables to detect deterioration, it may be a helpful risk tool in the emergency department (ED). Although small studies suggest it can predict early mortality, no large study has compared it with the National Early Warning Score (NEWS), the most widely validated risk score for death within 24 h.

AIM

The aim of this study was to compare the ability of the ROX index with the NEWS to predict mortality within 24 h of arrival at the hospital.

METHODS

This was a retrospective observational multicentre analysis of data in the Netherlands Emergency Department Evaluation Database (NEED) on 270 665 patients attending four participating Dutch EDs. The ROX index and NEWS were determined on ED arrival and prior to ED treatment.

RESULTS

The risk of death within 24 h increased with falling ROX and rising NEWS values. The area under the receiving operating characteristic curves for 24-h mortality of NEWS was significantly higher than for the ROX index [0.92; 95% confidence interval (CI), 0.91-0.92 versus 0.87; 95% CI, 0.86-0.88; P  < 0.01]. However, the observed and predicted mortality by the ROX index was identical to mortality of 5%, after which mortality was underestimated. In contrast, up to a predicted 24-h mortality of 3% NEWS slightly underestimates mortality, and above this level over-estimates it. The standardized net benefit of ROX is slightly higher than NEWS up to a predicted 24-h mortality of 3%.

CONCLUSION

The prediction of 24-h mortality by the ROX index is more accurate than NEWS for most patients likely to be encountered in the ED. ROX may be used as a first screening tool in the ED.

Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor

Background: Currently-used tools for early recognition of clinical deterioration have high sensitivity, but with low specificity and are based on infrequent measurements. We aimed to develop a pre-symptomatic and real-time detection and warning tool for potential patients’ deterioration based on multi-parameter real-time warning score (MPRT-WS).

Methods: A total of more than 2 million measurements were collected, pooled, and analyzed from 521 participants, of which 361 were patients in general wards defined at high-risk for deterioration and 160 were healthy participants allocation as controls. The risk score stratification was based on cutoffs of multiple physiological parameters predefined by a panel of specialists, and included heart rate, blood oxygen saturation (SpO2), respiratory rate, cuffless systolic and diastolic blood pressure (SBP and DBP), body temperature, stroke volume (SV), cardiac output, and systemic vascular resistance (SVR), recorded every 5 min for a period of up to 72 h. The data was used to define the various risk levels of a real-time detection and warning tool, comparing it with the clinically-used National Early Warning Score (NEWS).

Results: When comparing risk levels among patients using both tools, 92.6%, 6.1%, and 1.3% of the readings were defined as “Low”, “Medium”, and “High” risk with NEWS, and 92.9%, 6.4%, and 0.7%, respectively, with MPRT-WS (p = 0.863 between tools). Among the 39 patients that deteriorated, 30 patients received ‘High’ or ‘Urgent’ using the MPRT-WS (42.7 ± 49.1 h before they deteriorated), and only 6 received ‘High’ score using the NEWS. The main abnormal vitals for the MPRT-WS were SpO2, SBP, and SV for the “Urgent” risk level, DBP, SVR, and SBP for the “High” risk level, and DBP, SpO2, and SVR for the “Medium” risk level.

Conclusion: As the new detection and warning tool is based on highly-frequent monitoring capabilities, it provides medical teams with timely alerts of pre-symptomatic and real-time deterioration.

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.

External validation of a triage tool for predicting cardiac arrest in the emergency department

Early recognition and prevention comprise the first ring of the Chain of Survival for in-hospital cardiac arrest (IHCA). We previously developed and internally validated an emergency department (ED) triage tool, Emergency Department In-hospital Cardiac Arrest Score (EDICAS), for predicting ED-based IHCA. We aimed to externally validate this novel tool in another ED population. This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center with approximately 130,000 ED visits per year. We retrieved data from 268,208 ED visits over a 2-year period. We selected one ED visit per person and excluded out-of-hospital cardiac arrest or children. Patient demographics and computerized triage information were retrieved, and the EDICAS was calculated to predict the ED-based IHCA. A total of 145,557 adult ED patients were included. Of them, 240 (0.16%) developed IHCA. The EDICAS showed excellent discrimination with an area under the receiver operating characteristic (AUROC) of 0.88. The AUROC of the EDICAS outperformed those of other early warning scores (0.80 for Modified Early Warning Score [MEWS] and 0.83 for Rapid Emergency Medicine Score [REMS]) in the same ED population. An EDICAS of 6 or above (i.e., high-risk patients) corresponded to a sensitivity of 33%, a specificity of 97%, and a positive likelihood ratio of 12.2. In conclusion, we externally validated a tool for predicting imminent IHCA in the ED and demonstrated its superior performance over other early warning scores. The real-world impact of the EDICAS warning system with appropriate interventions would require a future prospective study.

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.

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.

Comparing the prehospital NEWS with in-hospital ESI in predicting 30-day severe outcomes in emergency patients

BACKGROUND

In Iran, the emergency departments (EDs) have largely adopted the emergency severity index (ESI) to prioritize the emergency patients, however emergency medical services (EMS) mainly triage the patients based on the paramedics' gestalt. The National Early Warning Score (NEWS) is a recommended prehospital triage in the UK. We aimed to compare prehospital NEWS and ED ESI for predicting severe outcomes in emergency patients.

METHODS

An observational study was conducted in a university-affiliated ED between January and April 2021. Adult patients who arrived in the ED by EMS were included. EMS providers calculated the patients' NEWS upon arriving on the scene using an Android NEWS application. In the ED, triage nurses utilized the ESI algorithm to prioritize patients with higher clinical risk. Then, Research nurses recorded patients' 30-day severe outcomes (death or ICU admission). Finally, The prognostic properties of ESI and NEWS were evaluated.

RESULTS

One thousand forty-eight cases were included in the final analysis, of which 29 (2.7%) patients experienced severe outcomes. The difference between the prehospital NEWS and ED ESI in predicting severe outcomes was not statistically significant (AUC = 0.825, 95% CI: 0.74-0.91 and 0.897, 95% CI, 0.83-0.95, for prehospital NEWS and ESI, respectively).

CONCLUSION

Our findings indicated that prehospital NEWS compares favorably with ED ESI in predicting 30-day severe outcomes in emergency patients.

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.

Trajectories of Vital Signs and Risk of In-Hospital Cardiac Arrest

Background: Little is known about the trajectories of vital signs prior to in-hospital cardiac arrest (IHCA), which could explain the heterogeneous processes preceding this event. We aimed to identify clinically relevant subphenotypes at high risk of IHCA in the emergency department (ED).

Methods: This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center. We retrieved data from 733,398 ED visits over a 7-year period. We selected one ED visit per person and retrieved patient demographics, triage data, vital signs (systolic blood pressure [SBP], heart rate [HR], body temperature, respiratory rate, oxygen saturation), selected laboratory markers, and IHCA status. Group-based trajectory modeling was performed.

Results: There were 37,697 adult ED patients with a total of 1,507,121 data points across all vital-sign categories. Three to four trajectory groups per vital-sign category were identified, and the following five trajectory groups were associated with a higher rate of IHCA: low and fluctuating SBP, high and fluctuating HR, persistent hypothermia, recurring tachypnea, and low and fluctuating oxygen saturation. The IHCA-prone trajectory group was associated with a higher triage level and a higher mortality rate, compared to other trajectory groups. Except for the persistent hypothermia group, the other four trajectory groups were more likely to have higher levels of C-reactive protein, lactic acid, cardiac troponin I, and D-dimer. Multivariable analysis revealed that hypothermia (adjusted odds ratio [aOR], 2.20; 95% confidence interval [95%CI], 1.35–3.57) and recurring tachypnea (aOR 2.44; 95%CI, 1.24–4.79) were independently associated with IHCA.

Conclusions: We identified five novel vital-sign sub-phenotypes associated with a higher likelihood of IHCA, with distinct patterns in clinical course and laboratory markers. A better understanding of the pre-IHCA vital-sign trajectories may help with the early identification of deteriorating patients.

Early Warning Scores and Their Application in the Inpatient Oncology Settings

Early Warning Score (EWS) systems are tools that use alterations in vital signs to rapidly identify clinically deteriorating patients and escalate care accordingly. Since its conception in 1997, EWSs have been used in several settings, including the general inpatient ward, intensive care units, and the emergency department. Several iterations of EWSs have been developed with varying levels of sensitivity and specificity for use in different populations. There are multiple strengths of these tools, including their simplicity and their ability to standardize communication and to reduce inappropriate or delayed referrals to the intensive care unit. Although early identification of deteriorating patients in the oncology population is vital to reduce morbidity and mortality and to improve long-term prognosis, the application in the oncology setting has been limited. Patients with an oncological diagnosis are usually older, medically complex, and can have increased susceptibility to infections, end-organ damage, and death. A search using PubMed and Scopus was conducted for articles published between January 1997 and November 2020 pertaining to EWSs in the oncology setting. Seven relevant studies were identified and analyzed. The most commonly used EWS in this setting was the Modified Early Warning Score. Of the seven studies, only two included prospective validation of the EWS in the oncology population and the other five only included a retrospective assessment of the data. The majority of studies were limited by their small sample size, single-institution analysis, and retrospective nature. Future studies should assess dynamic changes in scores over time and evaluate balance measures to identify use of health care resources.

Evaluating the burden of head injuries on a rural emergency department in South Africa

BACKGROUND

Head injuries place a significant burden on the emergency department (ED) workload. This is prominent in low-middle income countries (LMICs), which have low resourced health systems and a skewed burden compared to global data. A large paucity of data exists among LMICs, therefore limiting comparisons on a global perspective. This study aimed to evaluate the ED burden of head injuries in a rural setting, within a LMIC.

METHODS

A retrospective chart review of all ED patients presenting with head injuries was conducted over a 3-month period. Relevant data was extracted using a data collection tool, followed by descriptive statistical analysis.

RESULTS

A total of 263 patients were identified, with a median age of 27 years and male predominance (78.7%). Interpersonal violence (IPV) was the mechanism of injury in 59.7% (n = 157) of cases, followed by road traffic injuries (23.2%) and non-intentional trauma (17.1%). Most injuries were because of blunt trauma (71.1%) and common types were soft tissue (46.2%) and scalp injuries (35.0%). In the paediatric subgroup, the most common mechanism of injury was falls, accounting for 52.0% of all falls in the study. The majority (71.5%) of patients were discharged, while 22.8% were admitted and 2.67% demised in the ED.

CONCLUSION

At this rural centre, there is a high ED burden of minor head injuries because of IPV, with a strong male predominance. This study serves to add to limited reported data from a LMIC setting, which appears to have a skewed burden compared to the global data.

The Value of Physiological Scoring Criteria in Predicting the In-Hospital Mortality of Acute Patients; a Systematic Review and Meta-Analysis

INTRODUCTION

There is no comprehensive meta-analysis on the value of physiological scoring systems in predicting the mortality of critically ill patients. Therefore, the present study intended to conduct a systematic review and meta-analysis to collect the available clinical evidence on the value of physiological scoring systems in predicting the in-hospital mortality of acute patients.

METHOD

An extensive search was performed on Medline, Embase, Scopus, and Web of Science databases until the end of year 2020. Physiological models included Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), modified REMS (mREMS), and Worthing Physiological Score (WPS). Finally, the data were summarized and the findings were presented as summary receiver operating characteristics (SROC), sensitivity, specificity and diagnostic odds ratio (DOR).

RESULTS

Data from 25 articles were included. The overall analysis showed that the area under the SROC curve of REMS, RAPS, mREMS, and WPS criteria were 0.83 (95% CI: 0.79-0.86), 0.89 (95% CI: 0.86-0.92), 0.64 (95% CI: 0.60-0.68) and 0.86 (95% CI: 0.83-0.89), respectively. DOR for REMS, RAPS, mREMS and WPS models were 11 (95% CI: 8-16), 13 (95% CI: 4-41), 2 (95% CI: 2-4) and 17 (95% CI: 5-59) respectively. When analyses were limited to trauma patients, the DOR of the REMS and RAPS models were 112 and 431, respectively. Due to the lack of sufficient studies, it was not possible to limit the analyses for mREMS and WPS.

CONCLUSION

The findings of the present study showed that three models of RAPS, REMS and WPS have a high predictive value for in-hospital mortality. In addition, the value of these models in trauma patients is much higher than other patient settings.

The role of emergency department triage early warning score (TREWS) and modified early warning score (MEWS) to predict in-hospital mortality in COVID-19 patients

Background

It is necessary to identify critical patients requiring hospitalization early due to the rapid increase in the number of COVID-19 cases.

Aim

This study aims to evaluate the effectiveness of scoring systems such as emergency department triage early warning score (TREWS) and modified early warning score (MEWS) in predicting mortality in COVID-19 patients.

Methods

In this retrospective cohort study, PCR positive patients evaluated for COVID-19 and decided to be hospitalized were evaluated. During the first evaluation, MEWS and TREWS scores of the patients were calculated. Intensive care needs as well as 24-h and 28-day mortality rates were evaluated.

Results

A total of 339 patients were included in the study. While 30 (8.8%) patients were hospitalized in the intensive care unit, 4 (1.2%) died in the emergency. The number of patients who died within 28 days was found to be 57 (16.8%). In 24-h mortality, the median MEWS value was found to be 7 (IQR 25–75) while the TREWS value was 11.5 (IQR 25–75). In the ROC analysis made for the diagnostic value of 28-day mortality of MEWS and TREWS scores, the area under the curve (AUC) for the MEWS score was found to be 0.833 (95% CI 0.777–0.888, p < 0.001) while it was identified as 0.823 (95% CI 0.764–0.882, p < 0.001) for the TREWS.

Conclusion

MEWS and TREWS calculated at emergency services are effective in predicting 28-day mortality in patients requiring hospitalization due to COVID-19.

Copeptin combined with National Early Warning Score for predicting survival in elderly critical ill patients at emergency department

OBJECTIVE

Copeptin, reflecting vasopressin release, as well as the National Early Warning Score (NEWS), reflecting the severity of critical illness, might qualify for survival prediction in elderly patients with critical illness. This prospective observational study aims at assessing the predictive value of copeptin combined with NEWS on the prognosis of elderly critical ill patients at emergency department (ED).

METHODS

We analyzed serum copeptin levels and the NEWS at admission to the ED in a prospective, single-center, and observational study comprising 205 elderly patients with critical illness. Death within 30 days after admission to the ED was the primary end point.

RESULTS

The serum copeptin levels and the NEWS in the non-survivor patients group were higher than those in the survivor group [30.35 (14.20, 38.91) vs 17.53 (13.01, 25.20), P = 0.001 and 9.0 (7.0-10.0) vs 7.0 (6.0-8.0), P = 0.001]. Multivariate logistic regression analysis showed that copeptin, NEWS and copeptin combined with NEWS were all independent risk factors for 30-day mortality in elderly patients with critical illness. Copeptin, NEWS and copeptin combined with NEWS all performed well in predicting 30-day survival, with area under the ROC curve (AUC) values of 0.766 (95%CI, 0.702-0.822), 0.797 (95%CI, 0.744-0.877) and 0.854 (95%CI, 0.798-0.899) respectively. Using the Z test to compare the areas under the above three curves, copeptin combined with NEWS showed a higher predictive value for 30-day survival (P < 0.05). As we calculated, the optimal cut-off values of copeptin and NEWS using the Youden index were 19.78 pg/mL and 8.5 points, respectively. Risk stratification analysis showed that patients with both copeptin levels higher than 19.78 pg/mL and NEWS points higher than 8.5 points had the highest risk of death.

CONCLUSIONS

Copeptin combined with NEWS have a stronger predictive power on the prognosis of elderly patients with critical illness at ED, comparing to either factor individually.

Effectiveness of the rapid emergency medicine score and the rapid acute physiology score in prognosticating mortality in patients presenting to the emergency department with COVID-19 symptoms

Objective

We investigated the effectiveness of the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score in identifying critical patients among those presenting to the emergency department with COVID-19 symptoms.

Material and methods

This prospective, observational, cohort study included patients with COVID-19 symptoms presenting to the emergency department over a two-month period. Demographics, clinical characteristics, and the data of all-cause mortality within 30 days after admission were noted, and the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score were calculated by the researchers. The receiver operating characteristic curve analysis was performed to determine the discriminative ability of the scores.

Results

A total of 555 patients with a mean of age of 49.4 ± 16.8 years were included in the study. The rate of 30-day mortality was 3.9% for the whole study cohort, 7.2% for the patients with a positive rt-PCR test result for SARS-CoV-2, and 1.2% for those with a negative rt-PCR test result for SARS-CoV-2. In the group of patients with COVID-19 symptoms, according to the best Youden's index, the cut-off value for the Rapid Emergency Medicine Score was determined as 3.5 (sensitivity: 81.82%, specificity: 73.08%), and the area under curve (AUC) value was 0.840 (95% confidence interval 0.768–0.913). In the same group, according to the best Youden's index, the cut-off value for the Rapid Acute Physiology Score was 2.5 (sensitivity: 90.9%, specificity: 97.38%), and the AUC value was 0.519 (95% confidence interval 0.393–0.646).

Conclusion

REMS is able to predict patients with COVID-19-like symptoms without positive rt-PCR for SARS-CoV-2 that are at a high-risk of 30-day mortality. Prospective multicenter cohort studies are needed to provide best scoring system for triage in pandemic clinics.

Clinical Utility of Delta Lactate for Predicting Early In-Hospital Mortality in Adult Patients: A Prospective, Multicentric, Cohort Study

One of the challenges in the emergency department (ED) is the early identification of patients with a higher risk of clinical deterioration. The objective is to evaluate the prognostic capacity of ΔLA (correlation between prehospital lactate (pLA) and hospital lactate (hLA)) with respect to in-hospital two day mortality. We conducted a pragmatic, multicentric, prospective and blinded-endpoint study in adults who consecutively attended and were transported in advanced life support with high priority from the scene to the ED. The corresponding area under the receiver operating characteristics curve (AUROC) was obtained for each of the outcomes. In total, 1341 cases met the inclusion criteria. The median age was 71 years (interquartile range: 54–83 years), with 38.9% (521 cases) females. The total 2 day mortality included 106 patients (7.9%). The prognostic precision for the 2 day mortality of pLA and hLA was good, with an AUROC of 0.800 (95% CI: 0.74–0.85; p < 0.001) and 0.819 (95% CI: 0.76–0.86; p < 0.001), respectively. Of all patients, 31.5% (422 cases) had an ΔLA with a decrease of <10%, of which a total of 66 patients (15.6%) died. A lactate clearance ≥ 10% is associated with a lower risk of death in the ED, and this value could potentially be used as a guide to determine if a severely injured patient is improving in response to the established treatment.

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.

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Traumatic brain injuries are complex situations in which the emergency medical services must quickly determine the risk of deterioration using minimal diagnostic methods. The aim of this study is to analyze whether the use of early warning scores can help with decision-making in these dynamic situations by determining the patients who need the intensive care unit. A prospective, multicentric cohort study without intervention was carried out on traumatic brain injury patients aged over 18 given advanced life support and taken to the hospital. Our study included a total of 209 cases. The total number of intensive-care unit admissions was 50 cases (23.9%). Of the scores analyzed, the National Early Warning Score2 was the best result presented with an area under the curve of 0.888 (0.81–0.94; p < 0.001) and an odds ratio of 25.4 (95% confidence interval (CI):11.2–57.5). The use of early warning scores (and specifically National Early Warning Score2) can help the emergency medical services to differentiate traumatic brain injury patients with a high risk of deterioration. The emergency medical services should use the early warning scores routinely in all cases for the early detection of high-risk situations.