Combination of the National Early Warning Score (NEWS) and inflammatory biomarkers for early risk stratification in emergency department patients: results of a multinational, observational study

The Value of Emergency Care Data Set (ECDS) Presentation Codes for Predicting Mortality and Inpatient Admission

BACKGROUND

 Early identification of patients at higher risk of death and hospital admission is an important problem in Emergency Departments (ED). Most triage scales were developed before current electronic healthcare records were developed. The implementation of a national Emergency Care Data Set (ECDS) allows for the standardised recording of presenting complaints and the use of Electronic Patient Records (EPR) offers the potential for automated triage. The mortality risk and need for hospital admission associated with the different presenting complaints in a standardised national data set has not been previously reported. This study aimed to quantify the risks of death and hospitalisation from presenting complaints. This would be valuable in developing automated triage tools and decision support software.

METHODS

We conducted an observational retrospective cohort study on patients who visited a single ED in 2021. The presenting complaints related to subsequent attendances were excluded. This patient list was then manually matched with a routinely collected list of deaths. All deaths that occurred within 30 days of attendance were included.

RESULTS

Data was collected from 84,999 patients, of which 1,159 people died within 30 days of attendance. The mortality rate was the highest in cardiac arrest [32 (78.1%)], cardiac arrest due to trauma [2(50%)] and respiratory arrest [3(50%)]. Drowsy [17(12%)], hypothermia [3(13%)] and cyanosis [1(10%)] were also high-risk categories. Chest pain [34(0.6%)] was not a high-risk presenting complaint.

CONCLUSION

The initial presenting complaint in ECDS may be useful to identify people at higher and lower risk of death. This information is useful for building automated triage models.

Identification of Clinically Significant Cytokine Signature Clusters in Patients With Septic Shock

OBJECTIVES

To identify cytokine signature clusters in patients with septic shock.

DESIGN

Prospective observational cohort study.

SETTING

Single academic center in the United States.

PATIENTS

Adult (≥ 18 yr old) patients admitted to the medical ICU with septic shock requiring vasoactive medication support.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred fourteen patients with septic shock completed cytokine measurement at time of enrollment (t 1 ) and 24 hours later (t 2 ). Unsupervised random forest analysis of the change in cytokines over time, defined as delta (t 2 -t 1 ), identified three clusters with distinct cytokine profiles. Patients in cluster 1 had the lowest initial levels of circulating cytokines that decreased over time. Patients in cluster 2 and cluster 3 had higher initial levels that decreased over time in cluster 2 and increased in cluster 3. Patients in clusters 2 and 3 had higher mortality compared with cluster 1 (clusters 1-3: 11% vs 31%; odds ratio [OR], 3.56 [1.10-14.23] vs 54% OR, 9.23 [2.89-37.22]). Cluster 3 was independently associated with in-hospital mortality (hazard ratio, 5.24; p = 0.005) in multivariable analysis. There were no significant differences in initial clinical severity scoring or steroid use between the clusters. Analysis of either t 1 or t 2 cytokine measurements alone or in combination did not reveal clusters with clear clinical significance.

CONCLUSIONS

Longitudinal measurement of cytokine profiles at initiation of vasoactive medications and 24 hours later revealed three distinct cytokine signature clusters that correlated with clinical outcomes.

Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.

Modified Rapid Emergency Medicine Score-Lactate (mREMS-L) performance to screen non-anticipated 30-day-related-mortality in emergency department

BACKGROUND

The aim of this study was to compare the ability to predict 30-day in-hospital mortality of lactate versus the modified Rapid Emergency Medicine Score (mREMS) versus the arithmetic sum of the mREMS plus the numerical value of lactate (mREMS-L).

METHODS

A prospective, multicentric, emergency department delivery, pragmatic study was conducted. To determine the predictive capacity of the scales, lactate was measured and the mREMS and mREMS-L were calculated in adult patients (aged>18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon between 1 January 2020 and 31 December 2021. The area under the receiver operating characteristic (ROC) curve of each of the scales was calculated in terms of mortality for 30 days.

RESULTS

A total of 5371 participants were included, and the in-hospital mortality rate at 30 days was of 11.4% (615 cases). The best cut-off point determined in the mREMS was 7.0 points (sensitivity of 67% and specificity of 84%), and for lactate, the cut-off point was 1.4 mmol/L (sensitivity of 88% and specificity of 67%). Finally, the combined mREMS-L showed a cut-off point of 7.9 (sensitivity of 83% and a specificity of 83%). The area under the ROC curve of the mREMS, lactate and mREMS-L for 30-day mortality was 0.851, 0.853, and 0.903, respectively (p < 0.001 in all cases).

CONCLUSIONS

The new score generated, mREMS-L, obtained better statistical results than its components (mREMS and lactate) separately.

Development and External Validation of the International Early Warning Score for Improved Age- and Sex-Adjusted In-Hospital Mortality Prediction in the Emergency Department

OBJECTIVES

Early Warning Scores (EWSs) have a great potential to assist clinical decision-making in the emergency department (ED). However, many EWS contain methodological weaknesses in development and validation and have poor predictive performance in older patients. The aim of this study was to develop and externally validate an International Early Warning Score (IEWS) based on a recalibrated National Early warning Score (NEWS) model including age and sex and evaluate its performance independently at arrival to the ED in three age categories (18-65, 66-80, > 80 yr).

DESIGN

International multicenter cohort study.

SETTING

Data was used from three Dutch EDs. External validation was performed in two EDs in Denmark.

PATIENTS

All consecutive ED patients greater than or equal to 18 years in the Netherlands Emergency department Evaluation Database (NEED) with at least two registered vital signs were included, resulting in 95,553 patients. For external validation, 14,809 patients were included from a Danish Multicenter Cohort (DMC).

MEASUREMENTS AND MAIN RESULTS

Model performance to predict in-hospital mortality was evaluated by discrimination, calibration curves and summary statistics, reclassification, and clinical usefulness by decision curve analysis. In-hospital mortality rate was 2.4% ( n = 2,314) in the NEED and 2.5% ( n = 365) in the DMC. Overall, the IEWS performed significantly better than NEWS with an area under the receiving operating characteristic of 0.89 (95% CIs, 0.89-0.90) versus 0.82 (0.82-0.83) in the NEED and 0.87 (0.85-0.88) versus 0.82 (0.80-0.84) at external validation. Calibration for NEWS predictions underestimated risk in older patients and overestimated risk in the youngest, while calibration improved for IEWS with a substantial reclassification of patients from low to high risk and a standardized net benefit of 5-15% in the relevant risk range for all age categories.

CONCLUSIONS

The IEWS substantially improves in-hospital mortality prediction for all ED patients greater than or equal to18 years.

The Use of the Initial National Early Warning Score 2 at the Emergency Department as a Predictive Tool of In-Hospital Mortality in Hemodialysis Patients

Background The diagnosis of sepsis in the emergency department (ED) is difficult due to the ambiguous nature of its expression and its non-specific symptoms. Multiple scoring tools have been utilized to detect the severity and prognosis of sepsis. This study aimed to evaluate the use of the initial National Early Warning Score 2 (NEWS-2) at the ED as a predictive tool of in-hospital mortality in hemodialysis patients. Methodology We performed a retrospective, observational study to review the records of hemodialysis patients admitted to King Abdulaziz Medical City in Riyadh with suspected sepsis from the 1st of January to the 31st of December 2019 using a convenient sampling technique. Results The results showed that NEWS-2 had a higher sensitivity in predicting sepsis compared to the Quick Sequential Organ Failure Assessment (qSOFA) (16.28% vs. 11.54%). However, qSOFA had a higher specificity in predicting sepsis compared to the NEWS-2 scoring system (81.16% vs. 74.14%). It was found that the NEWS-2 scoring system was more sensitive in predicting mortality compared to qSOFA (26% vs. 20%). However, qSOFA was more specific in predicting mortality compared to NEWS-2 (88.50% vs. 82.98%). Conclusions Our findings demonstrated that the initial NEWS-2 is a subpar screening tool for sepsis and in-hospital mortality in hemodialysis patients. The use of qSOFA at the time of ED presentation was found to have a relatively higher specificity in predicting sepsis and mortality when compared to NEWS-2. To assess the application of the initial NEWS-2 in the ED setting, additional research should be conducted.

Predicting patient decompensation from continuous physiologic monitoring in the emergency department

Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional signal processing and transformer-based deep learning on ECG and PPG waveforms. We study 19,847 adult ED visits, divided into training (75%), validation (12.5%), and a chronologically sequential held-out test set (12.5%). The best-performing models use a combination of engineered and transformer-derived features, predicting in a 90-minute window new tachycardia with AUROC of 0.836 (95% CI, 0.800-0.870), new hypotension with AUROC 0.802 (95% CI, 0.747-0.856), and new hypoxia with AUROC 0.713 (95% CI, 0.680-0.745), in all cases significantly outperforming models using only standard triage data. Salient features include vital sign trends, PPG perfusion index, and ECG waveforms. This approach could improve the triage of apparently stable patients and be applied continuously for the prediction of near-term clinical deterioration.

Clinical Outcome and Prognosis of a Nosocomial Outbreak of COVID-19

Nosocomial coronavirus disease 2019 (COVID-19) outbreaks have been reported despite widespread quarantine methods to prevent COVID-19 in society and hospitals. Our study was performed to investigate the clinical outcome and prognosis of a nosocomial outbreak of COVID-19. We retrospectively analyzed the medical records of patients diagnosed with nosocomial COVID-19 of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) at a university teaching hospital between 1 November 2021 and 31 April 2022. Nosocomial COVID-19 was defined as a positive SARS-CoV-2 polymerase chain reaction (PCR) test result 4 or more days after admission in asymptomatic patients who had a negative SARS-CoV-2 PCR test on admission. In this study, 167 patients were diagnosed with nosocomial COVID-19 (1.14%) among a total of 14,667 patients admitted to hospital during the study period. A total of 153 patients (91.6%) survived, but 14 patients (8.4%) died. The median time between admission and COVID-19 diagnosis was 11 days, and the median duration of hospital stay was 24 days. After adjusting for other factors, no vaccination (adjusted HR = 5.944, 95% CI = 1.626–21.733, p = 0.007) and chronic kidney disease (adjusted HR = 6.963, 95% CI = 1.182–41.014, p = 0.032) were found to increase mortality risk. Despite strict quarantine, a significant number of nosocomial COVID-19 cases with a relatively high mortality rate were reported. As unvaccinated status or chronic kidney disease were associated with poor outcomes of nosocomial COVID-19, more active preventive strategies and treatments for patients with these risk factors are needed.

Performance of digital early warning score (NEWS2) in a cardiac specialist setting: retrospective cohort study

INTRODUCTION

Patients with cardiovascular diseases (CVD) are at significant risk of developing critical events. Early warning scores (EWS) are recommended for early recognition of deteriorating patients, yet their performance has been poorly studied in cardiac care settings. Standardisation and integrated National Early Warning Score 2 (NEWS2) in electronic health records (EHRs) are recommended yet have not been evaluated in specialist settings.

OBJECTIVE

To investigate the performance of digital NEWS2 in predicting critical events: death, intensive care unit (ICU) admission, cardiac arrest and medical emergencies.

METHODS

Retrospective cohort analysis.

STUDY COHORT

Individuals admitted with CVD diagnoses in 2020; including patients with COVID-19 due to conducting the study during the COVID-19 pandemic.

MEASURES

We tested the ability of NEWS2 in predicting the three critical outcomes from admission and within 24 hours before the event. NEWS2 was supplemented with age and cardiac rhythm and investigated. We used logistic regression analysis with the area under the receiver operating characteristic curve (AUC) to measure discrimination.

RESULTS

In 6143 patients admitted under cardiac specialties, NEWS2 showed moderate to low predictive accuracy of traditionally examined outcomes: death, ICU admission, cardiac arrest and medical emergency (AUC: 0.63, 0.56, 0.70 and 0.63, respectively). Supplemented NEWS2 with age showed no improvement while age and cardiac rhythm improved discrimination (AUC: 0.75, 0.84, 0.95 and 0.94, respectively). Improved performance was found of NEWS2 with age for COVID-19 cases (AUC: 0.96, 0.70, 0.87 and 0.88, respectively).

CONCLUSION

The performance of NEWS2 in patients with CVD is suboptimal, and fair for patients with CVD with COVID-19 to predict deterioration. Adjustment with variables that strongly correlate with critical cardiovascular outcomes, that is, cardiac rhythm, can improve the model. There is a need to define critical endpoints, engagement with clinical experts in development and further validation and implementation studies of EHR-integrated EWS in cardiac specialist settings.

Biologically Active Adrenomedullin (bio-ADM) is of Potential Value in Identifying Congestion and Selecting Patients for Neurohormonal Blockade in Acute Dyspnea

PURPOSE

This study was designed to evaluate the role of biologically active adrenomedullin (bio-ADM) in congestion assessment and risk stratification in acute dyspnea.

METHODS

This is a sub-analysis of the Lithuanian Echocardiography Study of Dyspnea in Acute Settings. Congestion was assessed by means of clinical (peripheral edema, rales) and sonographic (estimated right atrial pressure) parameters. Ninety-day mortality was chosen for outcome analysis.

RESULTS

There were 1188 patients included. Bio-ADM concentration was higher in patients with peripheral edema at admission (48.2 [28.2-92.6] vs 35.4 [20.9-59.2] ng/L, P < .001). There was a stepwise increase in bio-ADM concentration with increasing prevalence of rales: 29.8 [18.8-51.1], 38.5 [27.5-67.1], and 51.1 [33.1-103.2] ng/L in patients with no rales, rales covering less than one-half, and greater than or equal to one-half of the pulmonary area, respectively (P < 0.001). Bio-ADM concentration demonstrated gradual elevation in patients with normal, moderately, and severely increased estimated right atrial pressure: 25.1 [17.6-42.4] ng/L, 36.1 [23.1-50.2], and 47.1 [30.7-86.7] ng/L, respectively (P < .05). Patients with bio-ADM concentration >35.5 ng/L were at more than twofold increased risk of dying (P < .001). Survival in those with high bio-ADM was significantly modified by neurohormonal blockade at admission (P < .05), especially if NT-proBNP levels were lower than the median (P = .002 for interaction).

CONCLUSION

Bio-ADM reflects the presence and the degree of pulmonary, peripheral, and intravascular volume overload and is strongly related to 90-day mortality in acute dyspnea. Patients with high bio-ADM levels demonstrated survival benefit from neurohormonal blockade.

Prognostic value of cardiac biomarkers and National Early Warning Score 2 in acute dyspnoea

Objective

Patients hospitalised with acute dyspnoea due to acute heart failure (AHF) have a grave prognosis, but the European Society of Cardiology guidelines recommend no system to risk stratify these patients. The prognostic value of combining National Early Warning Score (NEWS) 2 and established cardiac biomarkers is not known.

Methods

We measured high-sensitivity cardiac troponin T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) and calculated NEWS2 in 314 patients with acute dyspnoea within 24 hours of hospitalisation. Their prognostic merits were assessed in the total cohort and for the subgroup with AHF separately.

Results

The median age was 73 (quartile (Q) 1–3, 63–81) years, 48% were women and 143 patients (46%) were hospitalised with AHF. The 114 patients (36%) who died during follow-up (median 823 days, Q1–3, 471–998) had higher concentrations of hs-cTnT (62 vs 33 ng/L, p<0.001) and NT-proBNP (6995 vs 2605 ng/L, p<0.001), and higher NEWS2 (6.1 vs 4.5 points, p<0.001), compared with survivors. Patients with increased vs low NEWS2 clinical risk had higher mortality rates in adjusted analyses in the total cohort (HR 2.11, 95% CI 1.28 to 3.48) and in patients with AHF (HR 2.00, 95% CI 1.54 to 2.60). NEWS2 provided incremental prognostic information compared with biomarkers alone for the total cohort: area under the curve 0.72 vs 0.70, p=0.042, and for the subpopulation with AHF: 0.70 vs 0.67, p=0.014.

Conclusion

NEWS2 predicts long-term mortality in patients hospitalised due to acute dyspnoea and the subgroup with AHF and provide incremental prognostic information to hs-cTnT and NT-proBNP.

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.

Admission of patients with chest pain and/or breathlessness from the emergency department in relation to risk assessment and copeptin levels - an observational study

BACKGROUND

One of the most critical decisions that emergency department (ED) physicians make is the discharge versus admission of patients. We aimed to study the association of the decision in the ED to admit patients with chest pain and/or breathlessness to a ward with risk assessment using the Rapid Emergency Triage and Treatment System (RETTS), the National Early Warning Score (NEWS), and plasma levels of the biomarkers copeptin, midregional proadrenomedulin (MR-proADM), and midregional proatrial natriuretic peptide (MR-proANP).

METHODS

Patients presenting at the ED with chest pain and/or breathlessness with less than one week onset were enrolled. Patients were triaged according to RETTS. NEWS was calculated from the vital signs retrospectively.

RESULTS

Three hundred and thirty-four patients (167 males), mean age 63.8 ± 16.8 years, were included. Of which, 210 (62.8%) patients complained of chest pain, 65 (19.5%) of breathlessness, and 59 (17.7%) of both. Of these, 176 (52.7%) patients were admitted to a ward, and 158 (47.3%) patients were discharged from the ED. In binary logistic models, age, gender, vital signs (O₂ saturation and heart rate), NEWS class, and copeptin were associated with admission to a ward from the ED. In receiver-operating-characteristics (ROC) analysis, copeptin had an incremental predictive value compared to NEWS alone (P = 0.002).

CONCLUSIONS

Emergency physicians' decisions to admit patients with chest pain and/or breathlessness from the ED to a ward are related to age, O₂ saturation, heart rate, NEWS category, and copeptin. As an independent predictive marker for admission, early analysis of copeptin might be beneficial when improving patient pathways at the ED.

Lactate improves the predictive ability of the National Early Warning Score 2 in the emergency department

AIMS

The aim of this study was to compare the ability to predict 2-, 7-, 14-, and 30-day in-hospital mortality of lactate vs the National Early Warning Score 2 (NEWS2) vs the arithmetic sum of the NEWS2 plus the numerical value of lactate (NEWS2-L).

METHODS

This was a prospective, multicentric, emergency department delivery, pragmatic cohort study. To determine the predictive capacity of lactate, we calculated the NEWS2 and NEWS2-L in adult patients (aged >18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon (Spain) between November 1, 2019, and September 30, 2020. The area under the receiver operating characteristic curve of each of the scales was calculated in terms of mortality for every time frame (2, 7, 14, and 30 days). We determined the cut-off point of each scale that offered highest sensitivity and specificity using the Youden index.

RESULTS

A total of 1716 participants were included, and the in-hospital mortality rates at 2, 7, 14, and 30 days were of 7.8% (134 cases), 11.6% (200 cases), 14.2% (243 cases), and 17.2% (295 cases), respectively. The best cut-off point determined in the NEWS2 was 6.5 points (sensitivity of 97% and specificity of 59%), and for lactate, the cut-off point was 3.3 mmol/L (sensitivity of 79% and specificity of 72%). Finally, the combined NEWS2-L showed a cut-off point of 11.7 (sensitivity of 86% and a specificity of 85%). The area under the receiver operating characteristic curve of the NEWS2, lactate, and NEWS2-L in the validation cohort for 2-day mortality was 0.889, 0.856, and 0.923, respectively (p<0.001 in all cases).

CONCLUSIONS

The new score generated, NEWS2-L, obtained better statistical results than its components (NEWS2 and lactate) separately.

Application of the national early warning score (NEWS) in patients with acute aortic dissection: A case-control study

BACKGROUND

The vital-sign monitoring strategy of patients with acute aortic dissection in the emergency department is mainly based on traditional experience. This study attempts to explore the significance of the national early warning score (NEWS) in monitoring the condition of patients with acute aortic dissection during emergency observation and to provide evidence for emergency nurses in optimal and scientific monitoring of patients.

METHODS

The case-control method was used to continuously enrol patients with acute aortic dissection who had been in the emergency department; the STROBE checklist was used in this process. Based on patients' clinical deterioration, they were divided into two groups: clinical deterioration and non-clinical deterioration. The NEWS at each time point was compared by independent-samples t-test, and the predictive power of NEWS was evaluated according to the area under the receiver operating characteristic curve.

RESULTS

A total of 290 patients with acute aortic dissection were included: 46 patients showed clinical deterioration and 244 did not. There were significant differences in the NEW scores of the two groups at admission time and at 12, 8, 4 and 0.5 h before clinical deterioration. The NEW scores of the clinical deterioration group showed an upward trend, while the non-clinical deterioration group showed a relatively stable trend. The NEWS can be used to predict the occurrence of clinical deterioration earlier at 4 h before clinical deterioration. Simultaneously, the patient's respiration rate and SpO₂ had better predictive performance than other vital signs.

CONCLUSION

The NEWS can be used to triage patients with acute aortic dissection admitted to the emergency department. Continuous use of the NEWS for monitoring can play a vital role in early warning of clinical deterioration in patients with acute aortic dissection. In clinical care, attention should also be paid when patients with acute aortic dissection have abnormal respiration rate and SpO₂ .

Manchester Triage System: assessment in an emergency hospital service

OBJECTIVES

to analyze demographic data, clinical profile and outcomes of patients in emergency services according to Manchester Triage System's priority level.

METHODS

a cross-sectional, analytical study, carried out with 3,624 medical records. For statistical analysis, the Chi-Square Test was used.

RESULTS

white individuals were more advanced in age. In the red and white categories, there was a higher percentage of men when compared to women (p=0.0018) and higher prevalence of personal history. Yellow priority patients had higher percentage of pain (p<0.0001). Those in red category had a higher frequency of altered vital signs, external causes, and death outcome. There was a higher percentage of exams performed and hospitalization in the orange category. Blue priority patients had a higher percentage of non-specific complaints and dismissal after risk stratification.

CONCLUSIONS

a higher percentage of altered vital signs, number of tests performed, hospitalization and death were evidenced in Manchester protocol's high priority categories.

Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments

Background

In-hospital mortality and short-term mortality are indicators that are commonly used to evaluate the outcome of emergency department (ED) treatment. Although several scoring systems and machine learning-based approaches have been suggested to grade the severity of the condition of ED patients, methods for comparing severity-adjusted mortality in general ED patients between different systems have yet to be developed. The aim of the present study was to develop a scoring system to predict mortality in ED patients using data collected at the initial evaluation and to validate the usefulness of the scoring system for comparing severity-adjusted mortality between institutions with different severity distributions.

Methods

The study was based on the registry of the National Emergency Department Information System, which is maintained by the National Emergency Medical Center of the Republic of Korea. Data from 2016 were used to construct the prediction model, and data from 2017 were used for validation. Logistic regression was used to build the mortality prediction model. Receiver operating characteristic curves were used to evaluate the performance of the prediction model. We calculated the standardized W statistic and its 95% confidence intervals using the newly developed mortality prediction model.

Results

The area under the receiver operating characteristic curve of the developed scoring system for the prediction of mortality was 0.883 (95% confidence interval [CI]: 0.882–0.884). The Ws score calculated from the 2016 dataset was 0.000 (95% CI: − 0.021 – 0.021). The Ws score calculated from the 2017 dataset was 0.049 (95% CI: 0.030–0.069).

Conclusions

The scoring system developed in the present study utilizing the parameters gathered in initial ED evaluations has acceptable performance for the prediction of in-hospital mortality. Standardized W statistics based on this scoring system can be used to compare the performance of an ED with the reference data or with the performance of other institutions.

Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review

OBJECTIVE

To assess predictive performance of universal early warning scores (EWS) in disease subgroups and clinical settings.

DESIGN

Systematic review.

DATA SOURCES

Medline, CINAHL, Embase and Cochrane database of systematic reviews from 1997 to 2019.

INCLUSION CRITERIA

Randomised trials and observational studies of internal or external validation of EWS to predict deterioration (mortality, intensive care unit (ICU) transfer and cardiac arrest) in disease subgroups or clinical settings.

RESULTS

We identified 770 studies, of which 103 were included. Study designs and methods were inconsistent, with significant risk of bias (high: n=16 and unclear: n=64 and low risk: n=28). There were only two randomised trials. There was a high degree of heterogeneity in all subgroups and in national early warning score (I2=72%-99%). Predictive accuracy (mean area under the curve; 95% CI) was highest in medical (0.74; 0.74 to 0.75) and surgical (0.77; 0.75 to 0.80) settings and respiratory diseases (0.77; 0.75 to 0.80). Few studies evaluated EWS in specific diseases, for example, cardiology (n=1) and respiratory (n=7). Mortality and ICU transfer were most frequently studied outcomes, and cardiac arrest was least examined (n=8). Integration with electronic health records was uncommon (n=9).

CONCLUSION

Methodology and quality of validation studies of EWS are insufficient to recommend their use in all diseases and all clinical settings despite good performance of EWS in some subgroups. There is urgent need for consistency in methods and study design, following consensus guidelines for predictive risk scores. Further research should consider specific diseases and settings, using electronic health record data, prior to large-scale implementation.

PROSPERO REGISTRATION NUMBER

PROSPERO CRD42019143141.

Epidemiology and clinical features of common community human coronavirus disease

Background

We would evaluate the epidemiology, clinical aspects, and prognostic factors of patients of all ages admitted with human corona virus (HCoV).

Methods

This study was retrospectively performed at five university teaching hospitals between 1st January 2018 and 31th March 2020. Routine molecular testing using for multiplex real-time reverse transcription-polymerase chain reaction (RT-PCR) methods was conducted on the respiratory viruses. We assessed the demographics, laboratory findings, and treatment of patients infected with coronavirus.

Results

There were 807 coronavirus-infected patients from 24,311 patients with respiratory virus PCR test admitted to five hospitals over 27 months. All-cause mortality rates of patients admitted for seasonal HCoV disease were 3.1% in all patients and 10.8% in patients aged ≥18 years. The Cox proportional hazard regression analysis was performed in patients aged ≥18 years. After adjusting for other clinical variables, general weakness symptoms [hazard ratio (HR), 2.651; 95% confidence interval (CI), 1.147-6.125, P=0.023], National Early Warning Score (NEWS) ≥2 (HR, 5.485; 95% CI, 1.261-23.858, P=0.023), and coronavirus subtype OC43 (HR, 2.500; 95% CI, 1.060-5.897, P=0.036) were significantly associated with death from coronavirus.

Conclusions

Coronavirus infection can reveal a higher mortality rate in patients of ≥18 than those of <18 years, thus, adult patients require more careful treatment. Furthermore, in adult patients, the factors associated with death from coronavirus include general weakness symptoms, NEWS higher than 2, and OC43 subtype.

Identifying a Clinical Risk Triage Score for Adult Emergency Department

Emergency triage is crucial for the treatment and prognosis of emergency patients, but its validity needs further improvement. The purpose of this study was to identify a risk score for adult triage. We conducted a regression analysis of physiological and biochemical data from 1,522 adult patients. A 60-point triage scoring model included temperature, pulse, systolic blood pressure, oxygen saturation, consciousness, dyspnea, admission mode, syncope history, chest pain or chest tightness, complexion, hematochezia or hematemesis, hemoptysis, white blood count, creatinine, bicarbonate, platelets, and creatine kinase. The area under curve in predicting ICU admission was 0.929 (95% CI [0.913-0.944]) for the derivation cohort and 0.911 (95% CI [0.884-0.938]) for the validation cohort. Four categories: critical level (≥13 points), severe level (6-12 points), urgency level (1-5 points), and sub-acute level (0 points) were divided, which significantly distinguished the severity of emergency patients.

Retrospective study on the possible existence of a treatment paradox in sepsis scores in the emergency department

OBJECTIVE

The quick Sequential Organ Failure Assessment (qSOFA) is developed as a tool to identify patients with infection with increased risk of dying from sepsis in non-intensive care unit settings, like the emergency department (ED). An abnormal score may trigger the initiation of appropriate therapy to reduce that risk. This study assesses the risk of a treatment paradox: the effect of a strong predictor for mortality will be reduced if that predictor also acts as a trigger for initiating treatment to prevent mortality.

DESIGN

Retrospective analysis on data from a large observational cohort.

SETTING

ED of a tertiary medical centre in the Netherlands.

PARTICIPANTS

3178 consecutive patients with suspected infection.

PRIMARY OUTCOME

To evaluate the existence of a treatment paradox by determining the influence of baseline qSOFA on treatment decisions within the first 24 hours after admission.

RESULTS

226 (7.1%) had a qSOFA ≥2, of which 51 (22.6%) died within 30 days. Area under receiver operating characteristics of qSOFA for 30-day mortality was 0.68 (95% CI 0.61 to 0.75). Patients with a qSOFA ≥2 had higher odds of receiving any form of intensive therapy (OR 11.4 (95% CI 7.5 to 17.1)), such as aggressive fluid resuscitation (OR 8.8 95% CI 6.6 to 11.8), fast antibiotic administration (OR 8.5, 95% CI 5.7 to 12.3) or vasopressic therapy (OR 17.3, 95% CI 11.2 to 26.8), compared with patients with qSOFA <2.

CONCLUSION

In ED patients with suspected infection, a qSOFA ≥2 was associated with more intensive treatment. This could lead to inadequate prediction of 30-day mortality due to the presence of a treatment paradox.

TRIAL REGISTRATION NUMBER

6916.

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.

Integrating infection and sepsis management through holistic early warning systems and heuristic approaches: a concept proposal

This is a first attempt to integrate the three pillars of infection management: the infection prevention and control (IPC), and surveillance (IPCS), antimicrobial stewardship (AMS), and rapid identification and management of sepsis (RIMS). The new 'Sepsis-3' definition extrapolates the diagnosis of sepsis from our previously slightly naïve concept of a stepwise evolving pattern. In doing so, however, we have placed the transition from infection toward sepsis in the domain of uncertainty and time-dependency. This now demands that clinical judgment be used in the risk stratification of patients with infection, and that pragmatic local solutions be used to prompt clinicians to evaluate formally for sepsis. We feel it is necessary to stimulate the development of a new generation of concepts and models aiming at embracing uncertainty. We see the opportunity for a heuristic approach focusing on the relevant clinical predictors at hand allowing to navigate the uncertainty of infection diagnosis under time constraints. The diverse and situated clinical approaches eventually emerging need to focus on the understanding of infection as the unbalanced interactions of host, pathogen, and environment. In order extend such approach throughout the patient journey we propose a holistic early warning system underpinned by the risk-based categories of hazards and vulnerabilities iteratively fostered by the information gathered by the infection prevention control and surveillance, clinical microbiology, and clinical chemistry services.

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.

Diagnostic and prognostic value of the D-dimer test in emergency department patients: secondary analysis of an observational study

Background D-dimer measurement improves the rule-out of thromboembolic disease. However, little is known about the risk of false positive results for the diagnosis of thromboembolic disease and its prognostic value. Herein, we investigated factors influencing the accuracy of D-dimer and its prognostic value in a large cohort of emergency department (ED) patients. Methods This is a secondary analysis of a prospective observational single center, cohort study. Consecutive patients, for whom a D-dimer test was requested by the treating physician, were included. Associations of clinical parameters on admission with false positive D-dimer results for the diagnosis of thromboembolic disease were investigated with logistic regression analysis. Results A total of 3301 patients were included, of which 203 (6.1%) had confirmed thromboembolic disease. The negative and positive predictive values of the D-dimer test at the 0.5 mg/L cut-off were 99.9% and 11.4%, respectively. Several factors were associated with positive D-dimer results potentially falsely indicating thromboembolic disease in multivariate analysis including advanced age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.04-1.05, p < 0.001), congestive heart failure (CHF) (OR 2.79, 95% CI 1.77-4.4, p < 0.01), renal failure (OR 2.00, 95% CI 1.23-3.24, p = 0.005), history of malignancy (OR 2.6, 95% CI 1.57-4.31, p < 0.001), C-reactive protein (CRP) (OR 1.02, 95% CI 1.01-1.02, p < 0.001) and glomerular filtration rate (GFR) (OR 0.99, 95% CI 0.99-1.00, p = 0.003). Regarding its prognostic value, D-dimer was associated with a 30-day mortality (adjusted OR 1.05, 95% CI 1.02-1.09, p = 0.003) with an area under the curve (AUC) of 0.79. Conclusions While D-dimer allows an accurate rule-out of thromboembolic disease, its positive predictive value in routine ED patients is limited and largely influenced by age, comorbidities and acute disease factors. The strong prognostic value of D-dimer in this population warrants further investigation.

Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department

Objective

The National Early Warning Score (NEWS), based on the patients’ vital signs, detects clinical deterioration in critically ill patients and is used to reduce the incidence of in-hospital cardiac arrest. However, although mortality prediction based on vital signs may be difficult in older patients, the effectiveness of the NEWS has not yet been evaluated in this population. This study aimed to test the hypothesis that an elevated NEWS at admission increases the mortality risk in older patients admitted to the emergency department (ED).

Methods

We conducted a single-center retrospective study, including patients admitted to the ED between November 2016 and February 2017. We included patients aged >65 years who were admitted to the ED for any medical problem. The NEWS was calculated at the time of ED admission. The primary outcome was in-hospital mortality.

Results

In total, 3,169 patients were included in this study. Median age was 75 years (interquartile range [IQR], 70 to 80 years), and 1,557 (49.1%) patients were male. The in-hospital mortality rate was 5.1% (161 patients). Median NEWS was higher in non-survivors than in survivors (5 [IQR, 3–8] vs. 1 [IQR, 0–3], P<0.001). Multivariate logistic analysis showed that the NEWS was associated with in-hospital mortality, after adjusting for other confounders. The area under the curve of the NEWS for predicting in-hospital mortality was 0.820 (95% confidence interval, 0.806 to 0.833).

Conclusion

Our results show that the NEWS at admission is associated with in-hospital mortality among patients aged >65 years.