A comparison of the ability of the National Early Warning Score and the National Early Warning Score 2 to identify patients at risk of in-hospital mortality: A multi-centre database study

The impact of mandated use early warning system tools on the development of nurses' higher-order thinking: A systematic review

AIM

Ascertain the impact of mandated use of early warning systems (EWSs) on the development of registered nurses' higher-order thinking.

DESIGN

A systematic literature review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist (Page et al., 2021).

DATA SOURCES

CINAHL, Medline, Embase, PyscInfo.

REVIEW METHODS

Eligible articles were quality appraised using the MMAT tool. Data extraction was conducted independently by four reviewers. Three investigators thematically analysed the data.

RESULTS

Our review found that EWSs can support or suppress the development of nurses' higher-order thinking. EWS supports the development of higher-order thinking in two ways; by confirming nurses' subjective clinical assessment of patients and/or by providing a rationale for the escalation of care. Of note, more experienced nurses expressed their view that junior nurses are inhibited from developing effective higher-order thinking due to reliance on the tool.

CONCLUSION

EWSs facilitate early identification of clinical deterioration in hospitalised patients. The impact of EWSs on the development of nurses' higher-order thinking is under-explored. We found that EWSs can support and suppress nurses' higher-order thinking. EWS as a supportive factor reinforces the development of nurses' heuristics, the mental shortcuts experienced clinicians call on when interpreting their subjective clinical assessment of patients. Conversely, EWS as a suppressive factor inhibits the development of nurses' higher-order thinking and heuristics, restricting the development of muscle memory regarding similar presentations they may encounter in the future. Clinicians' ability to refine and expand on their catalogue of heuristics is important as it endorses the future provision of safe and effective care for patients who present with similar physiological signs and symptoms.

IMPACT

This research impacts health services and education providers as EWS and nurses' development of higher-order thinking skills are essential aspects of delivering safe, quality care.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a systematic review, and therefore, comprises no contribution from patients or the public.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Mortality risk factors in patients receiving ECPR after cardiac arrest: Development and validation of a clinical prognostic prediction model

BACKGROUND

Previous studies have shown an increasing trend of extracorporeal cardiopulmonary resuscitation (ECPR) use in patients with cardiac arrest (CA). Although ECPR have been found to reduce mortality in patients with CA compared with conventional cardiopulmonary resuscitation (CCPR), the mortality remains high. This study was designed to identify the potential mortality risk factors for ECPR patients for further optimization of patient management and treatment selection.

METHODS

We conducted a prospective, multicentre study collecting 990 CA patients undergoing ECPR in 61 hospitals in China from January 2017 to May 2022 in CSECLS registry database. A clinical prediction model was developed using cox regression and validated with external data.

RESULTS

The data of 351 patients meeting the inclusion criteria before October 2021 was used to develop a prediction model and that of 68 patients after October 2021 for validation. Of the 351 patients with CA treated with ECPR, 227 (64.8%) patients died before hospital discharge. Multivariate analysis suggested that a medical history of cerebrovascular diseases, pulseless electrical activity (PEA)/asystole and higher Lactate (Lac) were risk factors for mortality while aged 45-60, higher pH and intra-aortic balloon pump (IABP) during ECPR have protective effects. Internal validation by bootstrap resampling was subsequently used to evaluate the stability of the model, showing moderate discrimination, especially in the early stage following ECPR, with a C statistic of 0.70 and adequate calibration with GOF chi-square = 10.4 (p = 0.50) for the entire cohort. Fair discrimination with c statistic of 0.65 and good calibration (GOF chi-square = 6.1, p = 0.809) in the external validation cohort demonstrating the model's ability to predict in-hospital death across a wide range of probabilities.

CONCLUSION

Risk factors have been identified among ECPR patients including a history of cerebrovascular diseases, higher Lac and presence of PEA or asystole. While factor such as age 45-60, higher pH and use of IABP have been found protective against in-hospital mortality. These factors can be used for risk prediction, thereby improving the management and treatment selection of patients for this resource-intensive therapy.

Predictive value of the National Early Warning Score 2 for hospitalised patients with viral respiratory illness is improved by the addition of inspired oxygen fraction as a weighted variable

OBJECTIVES

The National Early Warning Score 2 (NEWS2) is validated for predicting acute deterioration, however, the binary grading of inspired oxygen fraction (FiO2) may limit performance. We evaluated the incorporation of FiO2 as a weighted categorical variable on NEWS2 prediction of patient deterioration.

SETTING

Two hospitals at a single medical centre, Guy's and St Thomas' NHS Foundation Trust.

DESIGN

Retrospective cohort of all ward admissions, with a viral respiratory infection (SARS-CoV-2/influenza).

PARTICIPANTS

3704 adult ward admissions were analysed between 01 January 2017 and 31 December 2021.

METHODS

The NEWS-FiO2 score transformed FiO2 into a weighted categorical variable, from 0 to 3 points, substituting the original 0/2 points. The primary outcome was a composite of cardiac arrest, unplanned critical care admission or death within 24 hours of the observation. Sensitivity, positive predictive value (PPV), number needed to evaluate (NNE) and area under the receiver operating characteristic curve (AUROC) were calculated. Failure analysis for the time from trigger to outcome was compared by log-rank test.

RESULTS

The mean age was 60.4±19.4 years, 52.6% were men, with a median Charlson Comorbidity of 0 (IQR 3). The primary outcome occurred in 493 (13.3%) patients, and the weighted FiO2 score was strongly associated with the outcome (p=<0.001). In patients receiving supplemental oxygen, 78.5% of scores were reclassified correctly and the AUROC was 0.81 (95% CI 0.81 to 0.81) for NEWS-FiO2 versus 0.77 (95% CI 0.77 to 0.77) for NEWS2. This improvement persisted in the whole cohort with a significantly higher failure rate for NEWS-FiO2 (p=<0.001). At the 5-point threshold, the PPV increased by 22.0% (NNE 6.7) for only a 3.9% decrease in sensitivity.

CONCLUSION

Transforming FiO2 into a weighted categorical variable improved NEWS2 prediction for patient deterioration, significantly improving the PPV. Prospective external validation is required before institutional implementation.

Variability in Provider Assessment of Sepsis and Potential of Host Response Technology to Address this Dilemma-Results of an Online Delphi Study

Potentially septic patients have a huge clinical and economic impact on hospitals and often present to the emergency department (ED) with undifferentiated symptoms. The triage of these patients is complex and has historically relied heavily upon provider judgment. This study aims to evaluate the consistency of provider judgment and the potential of a new host response sepsis test to aid in the triage process. A modified Delphi study involving 26 participants from multiple specialties was conducted to evaluate provider agreement about sepsis risk and to test proposed actions based on the results of a sepsis test. The participants considered case vignettes of potentially septic patients designed to represent diagnostic dilemmas. Provider assessment of sepsis risk in these cases ranged from 10% to 90% and agreement was poor. Agreement about clinical actions to take in response to testing improved when participants considered their own hypothetical borderline cases. New host response testing for sepsis may have the potential to improve sepsis diagnosis and care and should be applied in a protocolized fashion to ensure consistency of results.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Predicting ICU admission and death in the Emergency Department: A comparison of six early warning scores

AIM

To compare the ability of the most used Early Warning Scores (EWS) to identify adult patients at risk of poor outcomes in the emergency department (ED).

METHODS

Single-center, retrospective observational study. We evaluated the digital records of consecutive ED admissions in patients ≥ 18 years from 2010 to 2019 and calculated NEWS, NEWS2, MEWS, RAPS, REMS, and SEWS based on parameters measured on ED arrival. We assessed the discrimination and calibration performance of each EWS in predicting death/ICU admission within 24 hours using ROC analysis and visual calibration. We also measured the relative weight of clinical and physiological derangements that identified patients missed by EWS risk stratification using neural network analysis.

RESULTS

Among 225,369 patients assessed in the ED during the study period, 1941 (0.9%) were admitted to ICU or died within 24 hours. NEWS was the most accurate predictor (area under the receiver operating characteristic [AUROC] curve 0.904 [95% CI 0.805-0.913]), followed by NEWS2 (AUROC 0.901). NEWS was also well calibrated. In patients judged at low risk (NEWS < 2), 359 events occurred (18.5% of the total). Neural network analysis revealed that age, systolic BP, and temperature had the highest relative weight for these NEWS-unpredicted events.

CONCLUSIONS

NEWS is the most accurate EWS for predicting the risk of death/ICU admission within 24 h from ED arrival. The score also had a fair calibration with few events occurring in patients classified at low risk. Neural network analysis suggests the need for further improvements by focusing on the prompt diagnosis of sepsis and the development of practical tools for the measurement of the respiratory rate.

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.

Machine-learning models for prediction of sepsis patients mortality

OBJECTIVES

Sepsis is an infection-caused syndrome, that leads to life-threatening organ damage. We aim to develop machine learning models with large-scale data to predict sepsis patients' mortality.

DESIGN

we extracted sepsis patients from two databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) as a train set and Philips eICU Collaborative Research Database as a test set.

SETTING

ICUs in multicenter hospitals in the USA during 2012-2019.

PATIENTS OR PARTICIPANTS

A total of 21,680 sepsis-3 patients are included in the study, in which, 3771 patients were dead and 17,909 survived during hospitalization, respectively.

INTERVENTIONS

No interventions.

MAIN VARIABLES OF INTEREST

Basic information, examination items during hospitalization and some medication and treatment information are incorporated into analyzed. Seven different models were built with a Support vector machine, Decision Tree Classifier, Random Forest, Gradients Boosting, Multiple Layer Perception, Xgboost, light Gradients Boosting to predict dead or live during hospitalization.

RESULTS

Algorithms with an AUC value in the test set of the top three: light GBM, GBM, Xgboost. Considering the performance of the training set and the test set, the light GBM model performs best, and then the parameters of the model were adjusted, after that the AUC value was 0.99 in the train set, 0.96 in the test set, respectively.

CONCLUSIONS

Models built with light GBM algorithm from real-world sepsis patients from electronic health records accurately predict whether sepsis patients are dead and can be incorporated into clinical decision tools to enhance the prognosis of the patient and prevent adverse outcomes.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

BACKGROUND

Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear.

METHODS

The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram.

RESULTS

Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram.

CONCLUSIONS

The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Extremes of baseline cognitive function determine the severity of delirium: a population study

Although delirium is a significant clinical and public health problem, little is understood about how specific vulnerabilities underlie the severity of its presentation. Our objective was to quantify the relationship between baseline cognition and subsequent delirium severity. We prospectively investigated a population-representative sample of 1510 individuals aged ≥70 years, of whom 209 (13.6%) were hospitalized across 371 episodes (1999 person-days assessment). Baseline cognitive function was assessed using the modified Telephone Interview for Cognitive Status, supplemented by verbal fluency measures. We estimated the relationship between baseline cognition and delirium severity [Memorial Delirium Assessment Scale (MDAS)] and abnormal arousal (Observational Scale of Level of Arousal), adjusted by age, sex, frailty and illness severity. We conducted further analyses examining presentations to specific hospital settings and common precipitating aetiologies. The median time from baseline cognitive assessment to admission was 289 days (interquartile range 130 to 47 days). In admitted patients, delirium was present on at least 1 day in 45% of admission episodes. The average number of days with delirium (consecutively positive assessments) was 3.9 days. Elective admissions accounted for 88 bed days (4.4%). In emergency (but not elective) admissions, we found a non-linear U-shaped relationship between baseline global cognition and delirium severity using restricted cubic splines. Participants with baseline cognition 2 standard deviations below average (z-score = -2) had a mean MDAS score of 14 points (95% CI 10 to 19). Similarly, those with baseline cognition z-score = + 2 had a mean MDAS score of 7.9 points (95% CI 4.9 to 11). Individuals with average baseline cognition had the lowest MDAS scores. The association between baseline cognition and abnormal arousal followed a comparable pattern. C-reactive protein ≥20 mg/l and serum sodium <125 mM/l were associated with more severe delirium. Baseline cognition is a critical determinant of the severity of delirium and associated changes in arousal. Emergency admissions with lowest and highest baseline cognition who develop delirium should receive enhanced clinical attention.

The United Kingdom's National Early Warning Score: should everyone use it? A narrative review

This review critiques the benefits and drawbacks of the United Kingdom's National Early Warning Score (NEWS). Potential developments for the future are considered, as well as the role for NEWS in an emergency department (ED). The ability of NEWS to predict death within 24 h has been well validated in multiple clinical settings. It provides a common language for the assessment of clinical severity and can be used to trigger clinical interventions. However, it should not be used as the only metric for risk stratification as its ability to predict mortality beyond 24 h is not reliable and greatly influenced by other factors. The main drawbacks of NEWS are that measuring it requires trained professionals, it is time consuming and prone to calculation error. NEWS is recommended for use in acute UK hospitals, where it is linked to an escalation policy that reflects postgraduate experience; patients with lower NEWS are first assessed by a junior clinician and those with higher scores by more senior staff. This policy was based on expert opinion that did not consider workload implications. Nevertheless, its implementation has been shown to improve the efficient recording of vital signs. How and who should respond to different NEWS levels is uncertain and may vary according to the clinical setting and resources available. In the ED, simple triage scores which are quicker and easier to use may be more appropriate determinants of acuity. However, any alternative to NEWS should be easier and cheaper to use and provide evidence of outcome improvement.

Using Vital Signs to Place Acutely Ill Patients Quickly and Easily into Clinically Helpful Pathophysiologic Categories: Derivation and Validation of Eight Pathophysiologic Categories in Two Distinct Patient Populations of Acutely Ill Patients

BACKGROUND

Early warning scores reliably identify patients at risk of imminent death, but do not provide insight into what may be wrong with the patient or what to do about it.

OBJECTIVE

Our aim was to explore whether the Shock Index (SI), pulse pressure (PP), and ROX Index can place acutely ill medical patients in pathophysiologic categories that could indicate the interventions required.

METHODS

A retrospective post-hoc analysis of previously obtained and reported clinical data for 45,784 acutely ill medical patients admitted to a major regional referral Canadian hospital between 2005 and 2010 and validated on 107,546 emergency admissions to four Dutch hospitals between 2017 and 2022.

RESULTS

SI, PP, and ROX values divided patients into eight mutually exclusive physiologic categories. Mortality was highest in patient categories that included ROX Index value < 22, and a ROX Index value < 22 multiplied the risk of any other abnormality. Patients with a ROX Index value < 22, PP < 42 mm Hg, and SI > 0.7 had the highest mortality and accounted for 40% of deaths within 24 h of admission, whereas patients with a PP ≥ 42 mm Hg, SI ≤ 0.7, and ROX Index value ≥ 22 had the lowest risk of death. These results were the same in both the Canadian and Dutch patient cohorts.

CONCLUSIONS

SI, PP, and ROX Index values can place acutely ill medical patients into eight mutually exclusive pathophysiologic categories with different mortality rates. Future studies will assess the interventions needed by these categories and their value in guiding treatment and disposition decisions.

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

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

NEWS2, patient safety and hypercapnic respiratory failure

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

Prediction of prognosis and outcome of patients with pulmonary embolism in the emergency department using early warning scores and qSOFA score

Objective

To determine the prediction ability of the National Early Warning Score (NEWS), National Early Warning Score 2 (NEWS2), and quick Sequential Organ Failure Assessment (qSOFA) score for the prognosis of pulmonary embolism (PE) in the emergency department.

Methods

This retrospective study involved 245 patients with PE. The NEWS, NEWS2, and qSOFA scores were compared according to the hospitalization clinic (ward vs. intensive care unit), hospitalization length (<10 vs. >10 days), severity of embolism (massive vs. submassive), and outcome (discharged vs. died).

Results

The areas under the curve of the NEWS, NEWS2, and qSOFA score for 1-week mortality were 0.854 (sensitivity, 78%; specificity, 73%; cutoff, 7.5; confidence interval, 0.807–0.902), 0.870 (sensitivity, 83%; specificity, 73%; cutoff, 5.5; confidence interval, 0.825–0.915), and 0.789 (sensitivity, 83%; specificity, 51%; cutoff, 0.5; confidence interval, 0.720–0.858), respectively.

Conclusion

The NEWS2 more accurately predicts 1-week mortality than do the NEWS and qSOFA score in patients with PE.

Predicting outcome in acute respiratory admissions using patterns of National Early Warning Scores

AIMS

Accurately predicting risk of patient deterioration is vital. Altered physiology in chronic disease affects the prognostic ability of vital signs based early warning score systems. We aimed to assess the potential of early warning score patterns to improve outcome prediction in patients with respiratory disease.

METHODS

Patients admitted under respiratory medicine between April 2015 and March 2017 had their National Early Warning Score 2 (NEWS2) calculated retrospectively from vital sign observations. Prediction models (including temporal patterns) were constructed and assessed for ability to predict death within 24 hours using all observations collected not meeting exclusion criteria. The best performing model was tested on a validation cohort of admissions from April 2017 to March 2019.

RESULTS

The derivation cohort comprised 7,487 admissions and the validation cohort included 8,739 admissions. Adding the maximum score in the preceding 24 hours to the most recently recorded NEWS2 improved area under the receiver operating characteristic curve for death in 24 hours from 0.888 (95% confidence interval (CI) 0.881-0.895) to 0.902 (95% CI 0.895-0.909) in the overall respiratory population.

CONCLUSION

Combining the most recently recorded score and the maximum NEWS2 score from the preceding 24 hours demonstrated greater accuracy than using snapshot NEWS2. This simple inclusion of a scoring pattern should be considered in future iterations of early warning scoring systems.

Organizational Justice and Health: A Survey in Hospital Workers

In complex systems such as hospitals, work organization can influence the level of occupational stress and, consequently, the physical and mental health of workers. Hospital healthcare workers were asked to complete a questionnaire during their regular occupational health examination, in order to assess the perceived level of organizational justice, and to verify whether it was associated with occupational stress, mental health, and absenteeism. The questionnaire included the Colquitt Organizational Justice (OJ) Scale, the Karasek/Theorell demand-control-support (DCS) questionnaire for occupational stress, and the General Health Questionnaire (GHQ12) for mental health. Workers were also required to indicate whether they had been absent because of back pain in the past year. Organizational justice was a significant predictor of occupational stress. Stress was a mediator in the relationship between justice and mental health. Occupational stress was more closely related to perceptions of lack of distributive justice than to perceptions of procedural, informational, and interpersonal justice. Physicians perceived significantly less distributive justice than other workers. In adjusted univariate logistic regression models, the perceptions of organizational justice were associated with a significant reduction in the risk of sick leave for back pain (OR 0.96; CI95% 0.94−0.99; p < 0.001), whereas occupational stress was associated with an increased risk of sick leave (OR 6.73; CI95% 2.02−22.40; p < 0.002). Work organization is a strong predictor of occupational stress and of mental and physical health among hospital employees.

Predictive value of the New Zealand Early Warning Score for early mortality in low-acuity patients discharged at scene by paramedics: an observational study

OBJECTIVES

The utility of New Zealand Early Warning Score (NZEWS) for prediction of adversity in low-acuity patients discharged at scene by paramedics has not been investigated. The objective of this study was to evaluate the association between the NZEWS risk-assessment tool and adverse outcomes of early mortality or ambulance reattendance within 48 hours in low-acuity, prehospital patients not transported by ambulance.

DESIGN

A retrospective cohort study.

SETTING

Prehospital emergency medical service provided by St John New Zealand over a 2-year period (1 July 2016 through 30 June 2018).

PARTICIPANTS

83 171 low-acuity, adult patients who were attended by an ambulance and discharged at scene. Of these, 41 406 had sufficient recorded data to calculate an NZEWS.

PRIMARY AND SECONDARY OUTCOMES AND MEASURES

Binary logistic regression modelling was used to investigate the association between the NZEWS and adverse outcomes of reattendance within 48 hours, mortality within 2 days, mortality within 7 days and mortality within 30 days.

RESULTS

An NZEWS greater than 0 was significantly associated with all adverse outcomes studied (p<0.01), compared with the reference group (NZEWS=0). There was a startling correlation between 2-day, 7-day and 30-day mortality and higher early warning scores; the odds of 2-day mortality in patients with an early warning score>10 was 70 times that of those scoring 0 (adjusted OR 70.64, 95% CI: 30.73 to 162.36). The best predictability for adverse outcome was observed for 2-day and 7-day mortality, with moderate area under the receiver operating characteristic curve scores of 0.78 (95% CI: 0.73 to 0.82) and 0.74 (95% CI: 0.71 to 0.77), respectively.

CONCLUSIONS

Adverse outcomes in low-acuity non-transported patients show a significant association with risk prediction by the NZEWS. There was a very high association between large early warning scores and 2-day mortality in this patient group. These findings suggest that NZEWS has significant utility for decision support and improving safety when determining the appropriateness of discharging low-acuity patients at the scene.

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19.

Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences.

Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 > 2 led to a "number needed to (NNT) diagnose" that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model.

Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making.

Low body temperature and mortality in older patients with frailty in the emergency department

Purpose

The aim of this study was to assess the association between low body temperature and mortality in frail older adults in the emergency department (ED).

Methods

Inclusion criteria were: ≥ 75 years of age, Clinical Frailty Scale (CFS) score of 4–8, and temperature documented at ED admission. Patients were allocated to three groups by body temperature: low ≤ 36.0 °C, normal 36.1–38.0 and high ≥ 38.1. Odds ratios (OR) for 30-day and 90-day mortality were analysed.

Results

1577 patients, 61.2% female, were included. Overall mortalities were 85/1577 (5.4%) and 144/1557 (9.2%) in the 30-day and 90-day follow-ups, respectively. The ORs for low body temperature were 3.03 (1.72–5.35; P < 0.001) and 2.71 (1.68–4.38; P < 0.001) for 30-day and 90-day mortality, respectively. This association remained when adjusted for age, CFS score and gender. Mortality of the high-temperature group did not differ significantly when compared to the normal-temperature group.

Conclusions

Low body temperature in frail older ED patients was associated with significantly higher 30- and 90-day mortality.

Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease

An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients’ treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusters with highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decision-making process.

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

OBJECTIVES

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

DESIGN

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

SETTING

One university hospital and two regional hospitals in Finland.

PARTICIPANTS

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

PRIMARY AND SECONDARY OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION NUMBER

NCT04055350.

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.

Thoracic Society of Australia and New Zealand Position Statement on Acute Oxygen Use in Adults: 'Swimming between the flags'

Oxygen is a life-saving therapy but, when given inappropriately, may also be hazardous. Therefore, in the acute medical setting, oxygen should only be given as treatment for hypoxaemia and requires appropriate prescription, monitoring and review. This update to the Thoracic Society of Australia and New Zealand (TSANZ) guidance on acute oxygen therapy is a brief and practical resource for all healthcare workers involved with administering oxygen therapy to adults in the acute medical setting. It does not apply to intubated or paediatric patients. Recommendations are made in the following six clinical areas: assessment of hypoxaemia (including use of arterial blood gases); prescription of oxygen; peripheral oxygen saturation targets; delivery, including non-invasive ventilation and humidified high-flow nasal cannulae; the significance of high oxygen requirements; and acute hypercapnic respiratory failure. There are three sections which provide (1) a brief summary, (2) recommendations in detail with practice points and (3) a detailed explanation of the reasoning and evidence behind the recommendations. It is anticipated that these recommendations will be disseminated widely in structured programmes across Australia and New Zealand.

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.

Sepsis Prediction for the General Ward Setting

Objective

To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design.

Design

Retrospective analysis of data extracted from electronic health records (EHR).

Setting

Single, tertiary-care academic medical center in St. Louis, MO, USA.

Patients

Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019.

Interventions

None.

Measurements and Main Results

Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (AUPRC) of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 h of crossing the alert threshold, 20.9% had a sepsis-related event occur.

Conclusions

A machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h.

Evaluation of NEWS2 response thresholds in a retrospective observational study from a UK acute hospital

OBJECTIVE

Use of National Early Warning Score 2 (NEWS2) has been mandated in adults admitted to acute hospitals in England. Urgent clinical review is recommended at NEWS2 ≥5. This policy is recognised as requiring ongoing evaluation. We assessed NEWS2 acquisition, alerting at key thresholds and patient outcomes, to understand how response recommendations would affect clinical resource allocation.

SETTING

Adult acute hospital in England.

DESIGN

Retrospective observational cohort study.

PARTICIPANTS

100 362 consecutive admissions between November 2018 and July 2019.

OUTCOME

Death or admission to intensive care unit within 24 hours of a score.

METHODS

NEWS2 were assembled as single scores from consecutive 24-hour time frames, (the first NEWS2 termed 'Index-NEWS2'), or as all scores from the admission (termed All-NEWS2). Scores were excluded when a patient was in intensive care, in the presence of a decision not to attempt cardiopulmonary resuscitation, or on day 1 of elective admission.

RESULTS

A mean of 4.5 NEWS2 were acquired per patient per day. The outcome rate following an Index-NEWS2 was 0.22/100 patient-days. The sensitivity of outcome prediction at Index-NEWS2 ≥5=0.46, and number needed to evaluate (NNE)=52. At this threshold, a mean of 37.6 alerts/100 patient-days would be generated, occurring in 12.3% of patients on any single day. Threshold changes to increase sensitivity by 0.1, would result in a twofold increase in alert rate and 1.5-fold increase in NNE. Overall, NEWS2 classification performance was significantly worse on Index-scores than All-scores (c-statistic=0.78 vs 0.85; p<0.001).

CONCLUSIONS

The combination of low event-rate, high alert-rate and low sensitivity, in patients for cardiopulmonary resuscitation, means that at current NEWS2 thresholds, resource demand would be sufficient to meaningfully compete with other pathways to clinical evaluation. In analyses that epitomise in-patient screening, NEWS2 performance suggests a need for re-evaluation of current response recommendations in this population.

The predictive power of the National Early Warning Score (NEWS) 2, as compared to NEWS, among patients assessed by a Rapid response team: A prospective multi-centre trial

Aim

Early identification of patients at risk of serious adverse events (SAEs) is of vital importance, yet it remains a challenging task. We investigated the predictive power of National Early Warning Score (NEWS) 2, as compared to NEWS, among patients assessed by a Rapid response team (RRT).

Methods

Prospective, observational cohort study on 898 consecutive patients assessed by the RRTs in 26 Swedish hospitals. For each patient, NEWS and NEWS 2 scores were uniformly calculated by the study team. The associations of NEWS and NEWS 2 scores with unanticipated admissions to Intensive care unit (ICU), mortality and in-hospital cardiac arrests (IHCA) within 24 h, and the composite of these three events were investigated using logistic regression. The predictive power of NEWS and NEWS 2 was assessed using the area under the receiver operating characteristic (AUROC) curves.

Results

The prognostic accuracy of NEWS/NEWS 2 in predicting mortality was acceptable (AUROC 0.69/0.67). In discriminating the composite outcome and unanticipated ICU admission, both NEWS and NEWS 2 were relatively weak (AUROC 0.62/0.62 and AUROC 0.59/0.60 respectively); for IHCA the performance was poor. There were no differences between NEWS and NEWS 2 as to the predictive power.

Conclusion

The prognostic accuracy of NEWS 2 to predict mortality within 24 h was acceptable. However, the prognostic accuracy of NEWS 2 to predict IHCA was poor. NEWS and NEWS 2 performed similar in predicting the risk of SAEs but their performances were not sufficient for use as a risk stratification tool in patients assessed by a RRT.

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.

Comorbidity-adjusted NEWS predicts mortality in suspected patients with COVID-19 from nursing homes: Multicentre retrospective cohort study

Aims

To assess the prognostic accuracy of comorbidity‐adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department.

Design

Multicentre retrospective cohort study.

Methods

Patients transferred by high‐priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2‐day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort.

Results

A total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two‐day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low‐, medium‐ and high‐level groups of comorbidities.

Conclusion

The comorbidity‐adjusted National Early Warning Score provides a good short‐term prognostic criterion, information that can help in the decision‐making process to guide the best strategy for each older adult, under the current pandemic.

Impact

What problem did the study address?

Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging.

What were the main findings?

Comorbidity‐adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately.

Where and on whom will the research have impact?

A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.

Predicting severe outcomes using national early warning score (NEWS) in patients identified by a rapid response system: a retrospective cohort study

There are insufficient data in managing patients at high risk of deterioration. We aimed to investigate that national early warning score (NEWS) could predict severe outcomes in patients identified by a rapid response system (RRS), focusing on the patient’s age. We conducted a retrospective cohort study from June 2019 to December 2020. Outcomes were unplanned intensive care unit (ICU) admission, ICU mortality, and in-hospital mortality. We analyzed the predictive ability of NEWS using receiver operating characteristics (ROC) curve and the effect of NEWS parameters using multivariable logistic regression. A total of 2,814 RRS activations were obtained. The predictive ability of NEWS for unplanned ICU admission and in-hospital mortality was fair but was poor for ICU mortality. The predictive ability of NEWS showed no differences between patients aged 80 years or older and under 80 years. However, body temperature affected in-hospital mortality for patients aged 80 years or older, and the inverse effect on unplanned ICU admission was observed. The NEWS showed fair predictive ability for unplanned ICU admission and in-hospital mortality among patients identified by the RRS. The different presentations of patients 80 years or older should be considered in implementing the RRS.

Validation of the Emergency Surgery Score (ESS) in a Greek patient population: a prospective bi-institutional cohort study

Purpose

The Emergency Surgery Score (ESS) is a reliable point-based score that predicts mortality and morbidity in emergency surgery patients. However, it has been validated only in the U.S. patients. We aimed to prospectively validate ESS in a Greek patient population.

Methods

All patients who underwent an emergent laparotomy were prospectively included over a 15-month period. A systematic chart review was performed to collect relevant preoperative, intraoperative, and postoperative variables based on which the ESS was calculated for each patient. The relationship between ESS and 30-day mortality, morbidity (i.e., the occurrence of at least one complication), and the need for intensive care unit (ICU) admission was evaluated and compared between the Greek and U.S. patients using the c-statistics methodology. The study was registered on "Research Registry" with the unique identifying number 5901.

Results

A total of 214 patients (102 Greek) were included. The mean age was 64 years, 44% were female, and the median ESS was 7. The most common indication for surgery was hollow viscus perforation (25%). The ESS reliably and incrementally predicted mortality (c-statistics = 0.79 [95% CI 0.67–0.90] and 0.83 [95% CI 0.74–0.92]), morbidity (c-statistics = 0.83 [95% CI 0.76–0.91] and 0.79 [95% CI 0.69–0.88]), and ICU admission (c-statistics = 0.88 [95% CI 0.81–0.96] and 0.84 [95% CI 0.77–0.91]) in both Greek and U.S. patients.

Conclusion

The correlation between the ESS and the surgical outcomes was statistically significant in both Greek and U.S. patients undergoing emergency laparotomy. ESS could prove globally useful for preoperative patient counseling and quality-of-care benchmarking.

Systematic geriatric assessment for older patients with frailty in the emergency department: a randomised controlled trial

BACKGROUND

Comprehensive geriatric assessment provided in hospital wards in frail patients admitted to hospital has been shown to reduce mortality and increase the likelihood of living at home later. Systematic geriatric assessment provided in emergency departments (ED) may be effective for reducing days in hospital and unnecessary hospital admissions, but this has not yet been proven in randomised trials.

METHODS

We conducted a single-centre, randomised controlled trial with a parallel-group, superiority design in an academic hospital ED. ED patients aged ≥ 75 years who were frail, or at risk of frailty, as defined by the Clinical Frailty Scale, were included in the trial. Patients were recruited during the period between December 11, 2018 and June 7, 2019, and followed up for 365 days. For the intervention group, systematic geriatric assessment was added to their standard care in the ED, whereas the control group received standard care only. The primary outcome was cumulative hospital stay during 365-day follow-up. The secondary outcomes included: admission rate from the index visit, total hospital admissions, ED-readmissions, proportion of patients living at home at 365 days, 365-day mortality, and fall-related ED-visits.

RESULTS

A total of 432 patients, 63 % female, with median age of 85 years, formed the analytic sample of 213 patients in the intervention group and 219 patients in the control group. Cumulative hospital stay during one-year follow-up as rate per 100 person-years for the intervention and control groups were: 3470 and 3149 days, respectively, with rate ratio of 1.10 (95 % confidence interval, 0.55-2.19, P = .78). Admission rates to hospital wards from the index ED visit for the intervention and control groups were: 62 and 70 %, respectively (P = .10). No significant differences were observed between the groups for any outcomes.

CONCLUSION

Systematic geriatric assessment for older adults with frailty in the ED did not reduce hospital stay during one-year follow-up. No statistically significant difference was observed for any secondary outcomes. More coordinated, continuous interventions should be tested for potential benefits in long-term outcomes.

TRIAL REGISTRATION

The trial was registered in the ClinicalTrials.gov (registration number and date NCT03751319 23/11/2018).

Performance Analysis of the National Early Warning Score and Modified Early Warning Score in the Adaptive COVID-19 Treatment Trial Cohort

We sought to validate prognostic scores in coronavirus disease 2019 including National Early Warning Score, Modified Early Warning Score, and age-based modifications, and define their performance characteristics.

DESIGN

We analyzed prospectively collected data from the Adaptive COVID-19 Treatment Trial. National Early Warning Score was collected daily during the trial, Modified Early Warning Score was calculated, and age applied to both scores. We assessed prognostic value for the end points of recovery, mechanical ventilation, and death for score at enrollment, average, and slope of score over the first 48 hours.

SETTING

A multisite international inpatient trial.

PATIENTS

A total of 1,062 adult nonpregnant inpatients with severe coronavirus disease 2019 pneumonia.

INTERVENTIONS

Adaptive COVID-19 Treatment Trial 1 randomized participants to receive remdesivir or placebo. The prognostic value of predictive scores was evaluated in both groups separately to assess for differential performance in the setting of remdesivir treatment.

MEASUREMENTS AND MAIN RESULTS

For mortality, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.60-0.68), and improved with addition of age (c-index, 0.66-0.74). For recovery, baseline National Early Warning Score and Modified Early Warning Score demonstrated somewhat better prognostic ability (c-index, 0.65-0.69); however, National Early Warning Score+age and Modified Early Warning Score+age further improved performance (c-index, 0.68-0.71). For deterioration, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.59-0.69) and improved with addition of age (c-index, 0.63-0.70). All prognostic performance improvements due to addition of age were significant (p < 0.05).

CONCLUSIONS

In the Adaptive COVID-19 Treatment Trial 1 cohort, National Early Warning Score and Modified Early Warning Score demonstrated moderate prognostic performance in patients with severe coronavirus disease 2019, with improvement in predictive ability for National Early Warning Score+age and Modified Early Warning Score+age. Area under receiver operating curve for National Early Warning Score and Modified Early Warning Score improved in patients receiving remdesivir versus placebo early in the pandemic for recovery and mortality. Although these scores are simple and readily obtainable in myriad settings, in our data set, they were insufficiently predictive to completely replace clinical judgment in coronavirus disease 2019 and may serve best as an adjunct to triage, disposition, and resourcing decisions.

Increased time from physiological derangement to critical care admission associates with mortality

Background

Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using ‘decision to admit’ to critical care, or arrival in the emergency department as ‘time zero’, rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission [‘Score to Door’ (STD) time] impacts on patient outcomes.

Methods

A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models.

Results

Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0–1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0–1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty.

Conclusion

In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the ‘Score to Door’ concept as a clinical indicator within rapid response systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03650-1.

Modified National Early Warning Score as Early Predictor of Outcome in COVID-19 Pandemic

COVID-19 represented an important challenge to the Italian healthcare system (IHCS). Our main aim was to obtain evidence to support the use of modified national early warning score (m-NEWS) as an interdisciplinary, common, and universal scoring scale to quickly recognize patients with a risk of clinical deterioration before admission and during hospitalization. As a secondary goal, we tried to find a score threshold that can trigger patients' immediate medical review as a part of an optimal triaging protocol for an emergency setting where healthcare resources are overloaded. We performed a retrospective observational study. We included in our study all patients treated for COVID-19 infection in surgical departments between 01 March 2020 and 16 April 2020. Patients with negative test results for SARS-COV-2 were excluded. m-NEWS was obtained twice a day. Patients' m-NEWS were analyzed in order to verify the correlation between m-NEWS (at admission and m-NEWS variation 24 h after admission) and outcome (positive outcome-survival, negative outcome-death, or intensive care unit (ICU) transfer). We included a population-based sample of 225 SARS-COV-2-infected patients. Overall, the average age at hospitalization was 71 (ranging from 40 to 95). 144 (64%) patients were males and 81 (36%) females. m-NEWS values lower or equal to 7 were associated with the majority of the "recovered" population (100/132 75.75%) and at the same time with the minority of the "non-recovered" population (25/93 26.88%). For our sample, age is statistically correlated to the outcome but a triage protocol based solely on this variable is less effective than m-NEWS, which showed to be a reliable and easy-to-use score for first patient evaluation. Our observations pave the way towards further studies aiming at optimizing territorial and community healthcare management protocols.

Hydrocortisone, Ascorbic Acid, and Thiamine (HAT) Therapy Decreases Oxidative Stress, Improves Cardiovascular Function, and Improves Survival in Murine Sepsis

INTRODUCTION

A small clinical trial showed HAT therapy improved survival but no studies have been reported in animal models to examine potential mechanisms.

METHODS

Sepsis was induced in female mice using the cecal ligation and puncture (CLP) model. Physiologic parameters including heart rate (HR), pulse distension (PD), and respiratory rate (RR) were measured noninvasively at baseline, 6 and 24 h post CLP. These measurements stratified mice into predicted to live (Live-P) or die (Die-P). Mice were randomized to receive HAT therapy or vehicle. Oxidative stress was measured in peritoneal exudative cells 24 h after CLP.

RESULTS

HR, PD, and RR all declined within the first 6 h of sepsis and were significantly lower in the Die-P mice compared with Live-P. HR 6 h post-CLP best predicted mortality and continued to decline between 6 and 24 h post CLP. Oxidative stress in peritoneal cells harvested 24 h post CLP (determined by 8 isoprostaglandin F2α and protein carbonyl derivatives) was significantly higher in the Die-P mice. HAT therapy was initiated 7 h post-CLP after mortality prediction and stratification. HAT significantly reduced oxidative stress in the Die-P mice without altering these parameters in the Live-P mice. HAT treatment prevented the decline in HR, again only in the Die-P mice. Mice treated with HAT therapy had significantly better survival.

CONCLUSIONS

Physiologic parameters accurately predicted mortality. Die-P mice had significant oxidative stress compared with Live-P. HAT therapy significantly decreased oxidative stress, increased HR, and improved survival in the Die-P mice. These data suggest that HAT exerts a beneficial effect through reducing oxidative stress and improving cardiovascular function.

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

OBJECTIVE

There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).

MATERIALS AND METHODS

We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors. Our framework was developed and evaluated based on the Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) predictive model to detect and leverage signals of clinician expertise for prediction of patient trajectories.

RESULTS

Seven themes-identified during development and simulation testing of the CONCERN model-informed framework development. The HPM-ExpertSignals conceptual framework includes a 3-step modeling technique: (1) identify patterns of clinical behaviors from user interaction with CIS; (2) interpret patterns as proxies of an individual's decisions, knowledge, and expertise; and (3) use patterns in predictive models for associations with outcomes. The CONCERN model differentiated at risk patients earlier than other early warning scores, lending confidence to the HPM-ExpertSignals framework.

DISCUSSION

The HPM-ExpertSignals framework moves beyond transactional data analytics to model clinical knowledge, decision making, and CIS interactions, which can support predictive modeling with a focus on the rapid and frequent patient surveillance cycle.

CONCLUSIONS

We propose this framework as an approach to embed clinicians' knowledge-driven behaviors in predictions and inferences to facilitate capture of healthcare processes that are activated independently, and sometimes well before, physiological changes are apparent.

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

OBJECTIVE

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

STUDY DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

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

PRIMARY OUTCOME MEASURES

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

RESULTS

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

CONCLUSION

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

Investigation into the predictive capability for mortality and the trigger points of the National Early Warning Score 2 (NEWS2) in emergency department patients

INTRODUCTION

National Early Warning Score 2 (NEWS2) is widely used to monitor and trigger assessment throughout a patient's hospital journey. Since the development and role out of NEWS2, its ability to predict mortality has been assessed in several settings, although to date not within an undifferentiated ED population.

METHODS

We conducted a retrospective observational study of all adult ED attendees at two EDs in Northern England, between March and November 2019. Multilevel multiple logistic regression analyses were conducted on patient episode data to assess the relationship between mortality at 2, 7 and 30 days from attendances; and maximum NEWS2, adjusting for age, sex, arrival mode and triage priority.

RESULTS

Data were collected from 91 871 valid patient episodes associated with 64 760 patients. NEWS2 was a significant predictor of mortality at 2 days (OR 1.75; 95% CI 1.58 to 1.93); at 7 days (OR 1.69; 95% CI 1.59 to 1.80); at 30 days (OR 1.58; 95% CI 1.52 to 1.64). For the analyses of categorised NEWS2, NEWS2 of 2-20 was significantly associated with mortality at 2, 7 and 30 days compared with none assigned: OR 3.54 (95% CI 2.15 to 5.85) at 2 days; OR 6.05 (95% CI 3.92 to 9.34) at 7 days; OR 12.4 (95% CI 7.91 to 19.3) at 30 days. Increasing age, male sex, arrival by ambulance and higher triage categories were also associated with significantly increased mortality. Area under the receiver operating characteristic curve values of 0.963, 0.946 and 0.915, respectively, were recorded for mortality outcomes, with optimum likelihood ratios associated with a trigger of 4 NEWS2 points.

CONCLUSIONS

NEWS2 is an effective predictor of mortality for patients presenting to the ED. Findings suggest that maximum NEWS2 of 4 and over may be the best trigger point for escalation of treatment. Findings also suggest a NEWS2 of 0-1 can identify a very low-risk group within the ED.

Development and validation of early warning score systems for COVID-19 patients

COVID‐19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. The ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high‐flow nasal oxygen, continuous positive airways pressure, non‐invasive ventilation, intubation) within a prediction window of 24 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

Validation of National Early Warning Score for predicting 30-day mortality after rapid response system activation in Japan

Aim

Although rapid response systems (RRS) are used to prevent adverse events, Japan reportedly has low activation rates and high mortality rates. The National Early Warning Score (NEWS) could provide a solution, but it has not been validated in Japan. We aimed to validate NEWS for Japanese patients.

Methods

This retrospective observational study included data of 2,255 adult patients from 33 facilities registered in the In‐Hospital Emergency Registry in Japan between January 2014 and March 2018. The primary evaluated outcome was mortality rate 30 days after RRS activation. Accuracy of NEWS was analyzed with the correlation coefficient and area under the receiver operating characteristic curve. Prediction weights of NEWS parameters were then analyzed using multiple logistic regression and a machine learning method, classification and regression trees.

Results

The correlation coefficient of NEWS for 30‐day mortality rate was 0.95 (95% confidence interval [CI], 0.88–0.98) and the area under the receiver operating characteristic curve was 0.668 (95% CI, 0.642–0.693). Sensitivity and specificity values with a cut‐off score of 7 were 89.8% and 45.1%, respectively. Regarding prediction values of each parameter, oxygen saturation showed the highest odds ratio of 1.36 (95% CI, 1.25–1.48), followed by altered mental status 1.23 (95% CI, 1.14–1.32), heart rate 1.21 (95% CI, 1.09–1.34), systolic blood pressure 1.12 (95% CI, 1.04–1.22), and respiratory rate 1.03 (95% CI, 1.05–1.26). Body temperature and oxygen supplementation were not significantly associated. Classification and regression trees showed oxygen saturation as the most heavily weighted parameter, followed by altered mental status and respiratory rate.

Conclusions

National Early Warning Score could stratify 30‐day mortality risk following RRS activation in Japanese patients.

National Early Warning Score 2 (NEWS2) better predicts critical Coronavirus Disease 2019 (COVID-19) illness than COVID-GRAM, a multi-centre study

Purpose

Clinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score.

Methods

We retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley–McNeil test.

Results

The NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80–0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68–0.85; p < 0.0001). Hanley–McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03).

Conclusions

The NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-021-01620-x.