Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients

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.

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data

Background

Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes.

Objectives

The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased.

Derivation Cohort

All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699).

Validation Cohort

External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389).

Prediction Model

All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score.

Results

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives.

Conclusions

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.

Implementation of an Electronic National Early Warning System to Decrease Clinical Deterioration in Hospitalized Patients at a Tertiary Medical Center

The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Real-time prediction of COVID-19 related mortality using electronic health records

Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.

Semiautomated Glasgow-Blatchford Bleeding Score helps direct bed placement for patients with upper gastrointestinal bleeding

OBJECTIVE

The Glasgow-Blatchford Bleeding Score (GBS) was designed to identify patients with upper gastrointestinal bleeding (UGIB) who do not require hospitalisation. It may also help stratify patients unlikely to benefit from intensive care.

DESIGN

We reviewed patients assigned a GBS in the emergency room (ER) via a semiautomated calculator. Patients with a score ≤7 (low risk) were directed to an unmonitored bed (UMB), while those with a score of ≥8 (high risk) were considered for MB placement. Conformity with guidelines and subsequent transfers to MB were reviewed, along with transfusion requirement, rebleeding, length of stay, need for intervention and death.

RESULTS

Over 34 months, 1037 patients received a GBS in the ER. 745 had an UGIB. 235 (32%) of these patients had a GBS ≤7. 29 (12%) low-risk patients were admitted to MBs. Four low-risk patients admitted to UMB required transfer to MB within the first 48 hours. Low-risk patients admitted to UMBs were no more likely to die, rebleed, need transfusion or require more endoscopic, radiographic or surgical procedures than those admitted to MBs. No low-risk patient died from GIB. Patients with GBS ≥8 were more likely to rebleed, require transfusion and interventions to control bleeding but not to die.

CONCLUSION

A semiautomated GBS calculator can be incorporated into an ER workflow. Patients with a GBS ≤7 are unlikely to need MB care for UGIB. Further studies are warranted to determine an ideal scoring system for MB admission.

Patterns of behaviour in nursing staff actioning the afferent limb of the rapid response system (RRS): A focused ethnography

AIM

To improve understanding of afferent limb behaviour in acute hospital ward settings, to define and specify who needs to do what differently and to report what afferent limb behaviours should be targeted in a subsequent multi-phase, theory-based, intervention development process.

DESIGN

Focused ethnography was used including direct observation of nursing staff enacting afferent limb behaviours and review of vital signs charts.

METHODS

An observation guide focused observation on "key moments" of the afferent limb. Descriptions of observations from between 7 January 2019-18 December 2019 were recorded in a field journal alongside reflexive notes. Vital signs and early warning scores from charts were reviewed and recorded. Field notes were analysed using structured content analysis. Observed behaviour was compared with expected (policy-specified) behaviour.

RESULTS

Observation was conducted for 300 hr. Four hundred and ninety-nine items of data (e.g., an episode of observation or a set of vital signs) were collected. Two hundred and eighty-nine (58%) items of data were associated with expected (i.e. policy-specified) afferent limb behaviour; 210 (42%) items of data were associated with unexpected afferent limb behaviour (i.e. alternative behaviour or no behaviour). Ten specific behaviours were identified where the behaviour observed deviated (negatively) from policy or where no action was taken when it should have been. One further behaviour was seen to expedite the assessment of a deteriorating patient by an appropriate responder and was therefore considered a positive deviance.

CONCLUSION

Afferent limb failure has been described as a problem of inconsistent staff behaviour. Eleven potential target behaviours for change are reported and specified using a published framework.

IMPACT

Clear specification of target behaviour will allow further enquiry into the determinants of these behaviours and the development of a theory-based intervention that is more likely to result in behaviour change and can be tested empirically in future research.

Translation of evidence into policy to improve clinical practice: the development of an emergency department rapid response system

BACKGROUND

Undetected clinical deterioration is a major cause of high mortality events in Emergency Department (ED) patients. Yet, there is no known model to guide the recognition and response to clinical deterioration in the ED, integrating internal and external resources.

METHODS

An integrative review was firstly conducted to identify the critical components of recognising and responding to clinical deterioration in the ED. Components identified from the review were analysed by clinical experts and informed the development of an ED Clinical Emergency Response System (EDCERS).

RESULTS

Twenty four eligible studies were included in the review. Eight core components were identified: 1) vital sign monitoring; 2) track and trigger system; 3) communication plan; 4) response time; 5) emergency nurse response; 6) emergency physician response; 7) critical care team response; and 8) specialty team response. These components informed the development of the EDCERS protocol, integrating responses from staff internal and external to the ED.

CONCLUSIONS

EDCERS was based on the best available evidence and considered the cultural context of care. Future research is needed to determine the useability and impact of EDCERS on patient and health outcomes.

The prevalence and management of deteriorating patients in an Australian emergency department

BACKGROUND

Complex human and system factors impact the effectiveness of Rapid Response Systems (RRS). Emergency Department (ED) specific RRS are relatively new and the factors associated with their effectiveness are largely unknown. This study describes the period prevalence of deterioration and characteristics of care for deteriorating patients in an Australia ED and examine relationships between system factors and escalation of care.

METHODS

A retrospective medical record audit of all patients presenting to an Australian ED in two weeks.

RESULTS

Period prevalence of deterioration was 10.08% (n=269). Failure to escalate care occurred in nearly half (n=52, 47.3%) of the patients requiring a response (n=110). Appropriate escalation practices were associated with where the patient was being cared for (p=0.01), and the competence level of the person documenting deterioration (p=0.005). Intermediate competence level nurses were nine times more likely to escalate care than novices and experts (p=0.005). While there was variance in escalation practice related to system factors, these associations were not statistically significant.

CONCLUSION

The safety of deteriorating ED patients may be improved by informing care based on the escalation practices of staff with intermediate ED experience and competence levels.

Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n = 133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.

Predicting clinical deterioration with Q-ADDS compared to NEWS, Between the Flags, and eCART track and trigger tools

BACKGROUND

Early warning tools have been widely implemented without evidence to guide (a) recognition and (b) response team expertise optimisation. With growing databases from MET-calls and digital hospitals, we now have access to guiding information. The Queensland Adult-Deterioration-Detection-System (Q-ADDS) is widely used and requires validation.

AIM

Compare the accuracy of Q-ADDS to National Early Warning Score (NEWS), Between-the-Flags (BTF) and the electronic Cardiac Arrest Risk Triage Score (eCART)).

METHODS

Data from the Chicago University hospital database were used. Clinical deterioration was defined as unplanned admission to ICU or death. Currently used NEWS, BTF and eCART trigger thresholds were compared with a clinically endorsed Q-ADDS variant.

RESULTS

Of 224,912 admissions, 11,706 (5%) experienced clinical deterioration. Q-ADDS (AUC 0.71) and NEWS (AUC 0.72) had similar predictive accuracy, BTF (AUC 0.64) had the lowest, and eCART (AUC 0.76) the highest. Early warning alert (advising ward MO review) had similar NPV (99.2-99.3%), for all the four tools however sensitivity varied (%: Q-ADDS = 47/NEWS = 49/BTF = 66/eCART = 40), as did alerting rate (% vitals sets: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). MET alert (advising MET/critical-care review) had similar NPV for all the four tools (99.1-99.2%), however sensitivity varied (%: Q-ADDS = 14/NEWS = 24/BTF = 19/eCART = 29), as did MET alerting rate (%: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). High-severity alert (advising advanced ward review, Q-ADDS only): NPV = 99.1%, sensitivity = 26%, alerting rate = 3.5%.

CONCLUSION

The accuracy of Q-ADDS is comparable to NEWS, and higher than BTF, with eCART being the most accurate. Q-ADDS provides an additional high-severity ward alert, and generated significantly fewer MET alerts. Impacts of increased ward awareness and fewer MET alerts on actual MET call numbers and patient outcomes requires further evaluation.

A proposal for selective resuscitation of adult cardiac arrest patients in a pandemic

Allocation of limited resources in pandemics begs for ethical guidance. The issue of ventilator allocation in pandemics has been reviewed by many medical ethicists, but as localities activate crisis standards of care, and health care workers are infected from patient exposure, the decision to pursue cardiopulmonary resuscitation (CPR) must also be examined to better balance the increased risks to healthcare personnel with the very low resuscitation rates of patients infected with coronavirus disease 2019 (COVID-19). A crisis standard of care that is equitable, transparent, and mindful of both human and physical resources will lessen the impact on society in this era of COVID-19. This paper builds on previous work of ventilator allocation in pandemic crises to propose a literature-based, justice-informed ethical framework for selecting treatment options for CPR. The pandemic affects regions differently over time, so these suggested guidelines may require adaptation to local practice variations.

Modified Early Warning System: Quality Improvement with the Help of Healthcare Failure Modes and Effect Analysis

Introduction: Hospitals struggle to implement MEWS. This study aims to improve MEWS implementation in the studied hospital.Objective: Improve the implementation of MEWS with the help of HFMEA.Materials: HFMEA together with training is used to improve the implementation.Results: The pre-intervention RPN got reduced from 1558 to 516 in the post-implementation phase.Application: This demonstrates improvement in the implementation of MEWS with the help of HFMEA, this study design can be widely used.Conclusion: The HFMEA is an effective tool to use for the improvement of MEWS implementation by the hospital nurses.

Evaluating a Widely Implemented Proprietary Deterioration Index Model Among Hospitalized COVID-19 Patients

INTRODUCTION

The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the COVID-19 pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations.

METHODS

We studied adult patients admitted with COVID-19 to non-ICU care at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of ICU-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization.

RESULTS

Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. Median age of the cohort was 64 (IQR 53-75) with 168 (43%) African Americans and 169 (43%) women. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.79 (95% CI 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically-relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%.

CONCLUSION

We found the EDI identifies small subsets of high- and low-risk COVID-19 patients with fair discrimination. We did not find evidence of bias by race or sex. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among COVID-19 patients.

Capillary refill time as part of an early warning score for rapid response team activation is an independent predictor of outcomes

BACKGROUND

Capillary refill time (CRT) is easy, quick to perform and when prolonged in critical illness, correlates with progression of organ failure and mortality. It is utilized in our hospital's early warning score (EWS) as one of 11 parameters. We sought to define CRT's value in predicting patient outcomes, compared to the remaining EWS elements.

METHODS

Five-year prospective observational study of 6480 consecutive Rapid Response Team (RRT) patients. CRT measured at the index finger was considered prolonged if time to previous-color return was >3 s. We analyzed the odds ratio of normal vs prolonged-CRT, compared to the other EWS variables, to individual and combined outcomes of mortality, cardiac arrest and higher-level of care transfer.

RESULTS

Twenty-percent (N = 1329) of RRT-patients had prolonged-CRT (vs normal-CRT), were twice as likely to die (36% vs 17.8%, p < .001), more likely to experience the combined outcome (72.1% vs 54.2%, p < .001) and had longer hospital length of stays, 15.3 (SD 0.3) vs 13.5 days (SD 0.5) (p < .001). Multivariable logistic regression for mortality ranked CRT second to hypoxia among all 11 variables evaluated (p < 001).

CONCLUSIONS

This is the first time CRT has been evaluated in RRT patients. Its measurement is easy to perform and proves useful as an assessment of adult patients at-risk for clinical decline. Its prolongation in our population was an independent predictor of mortality and the combined outcome. This study and others suggest that CRT should be considered further as a fundamental assessment of patients at-risk for clinical decline.

Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology

OBJECTIVE

To provide an overview and critical appraisal of early warning scores for adult hospital patients.

DESIGN

Systematic review.

DATA SOURCES

Medline, CINAHL, PsycInfo, and Embase until June 2019.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Studies describing the development or external validation of an early warning score for adult hospital inpatients.

RESULTS

13 171 references were screened and 95 articles were included in the review. 11 studies were development only, 23 were development and external validation, and 61 were external validation only. Most early warning scores were developed for use in the United States (n=13/34, 38%) and the United Kingdom (n=10/34, 29%). Death was the most frequent prediction outcome for development studies (n=10/23, 44%) and validation studies (n=66/84, 79%), with different time horizons (the most frequent was 24 hours). The most common predictors were respiratory rate (n=30/34, 88%), heart rate (n=28/34, 83%), oxygen saturation, temperature, and systolic blood pressure (all n=24/34, 71%). Age (n=13/34, 38%) and sex (n=3/34, 9%) were less frequently included. Key details of the analysis populations were often not reported in development studies (n=12/29, 41%) or validation studies (n=33/84, 39%). Small sample sizes and insufficient numbers of event patients were common in model development and external validation studies. Missing data were often discarded, with just one study using multiple imputation. Only nine of the early warning scores that were developed were presented in sufficient detail to allow individualised risk prediction. Internal validation was carried out in 19 studies, but recommended approaches such as bootstrapping or cross validation were rarely used (n=4/19, 22%). Model performance was frequently assessed using discrimination (development n=18/22, 82%; validation n=69/84, 82%), while calibration was seldom assessed (validation n=13/84, 15%). All included studies were rated at high risk of bias.

CONCLUSIONS

Early warning scores are widely used prediction models that are often mandated in daily clinical practice to identify early clinical deterioration in hospital patients. However, many early warning scores in clinical use were found to have methodological weaknesses. Early warning scores might not perform as well as expected and therefore they could have a detrimental effect on patient care. Future work should focus on following recommended approaches for developing and evaluating early warning scores, and investigating the impact and safety of using these scores in clinical practice.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017053324.

Early Detection of In-Patient Deterioration: One Prediction Model Does Not Fit All

Supplemental Digital Content is available in the text.

Objectives:

Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models.

Design:

Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; and logistic regression modeling for the outcome of ICU transfer for a specific clinical reason.

Setting:

Cardiac medical-surgical ward; tertiary care academic hospital.

Patients:

Eight-thousand one-hundred eleven adult patients, 457 of whom were transferred to an ICU for clinical deterioration.

Interventions:

None.

Measurements and Main Results:

We calculated the contributing relative risks of individual vital signs, laboratory tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate receiver operating characteristic areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons—respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy—had distinct signatures of illness. Statistical models trained to target-specific reasons for ICU transfer performed better than one model targeting combined events.

Conclusions:

A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.

Comparison of Early Warning Scoring Systems for Hospitalized Patients With and Without Infection at Risk for In-Hospital Mortality and Transfer to the Intensive Care Unit

IMPORTANCE

Risk scores used in early warning systems exist for general inpatients and patients with suspected infection outside the intensive care unit (ICU), but their relative performance is incompletely characterized.

OBJECTIVE

To compare the performance of tools used to determine points-based risk scores among all hospitalized patients, including those with and without suspected infection, for identifying those at risk for death and/or ICU transfer.

DESIGN, SETTING, AND PARTICIPANTS

In a cohort design, a retrospective analysis of prospectively collected data was conducted in 21 California and 7 Illinois hospitals between 2006 and 2018 among adult inpatients outside the ICU using points-based scores from 5 commonly used tools: National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Between the Flags (BTF), Quick Sequential Sepsis-Related Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS). Data analysis was conducted from February 2019 to January 2020.

MAIN OUTCOMES AND MEASURES

Risk model discrimination was assessed in each state for predicting in-hospital mortality and the combined outcome of ICU transfer or mortality with area under the receiver operating characteristic curves (AUCs). Stratified analyses were also conducted based on suspected infection.

RESULTS

The study included 773 477 hospitalized patients in California (mean [SD] age, 65.1 [17.6] years; 416 605 women [53.9%]) and 713 786 hospitalized patients in Illinois (mean [SD] age, 61.3 [19.9] years; 384 830 women [53.9%]). The NEWS exhibited the highest discrimination for mortality (AUC, 0.87; 95% CI, 0.87-0.87 in California vs AUC, 0.86; 95% CI, 0.85-0.86 in Illinois), followed by the MEWS (AUC, 0.83; 95% CI, 0.83-0.84 in California vs AUC, 0.84; 95% CI, 0.84-0.85 in Illinois), qSOFA (AUC, 0.78; 95% CI, 0.78-0.79 in California vs AUC, 0.78; 95% CI, 0.77-0.78 in Illinois), SIRS (AUC, 0.76; 95% CI, 0.76-0.76 in California vs AUC, 0.76; 95% CI, 0.75-0.76 in Illinois), and BTF (AUC, 0.73; 95% CI, 0.73-0.73 in California vs AUC, 0.74; 95% CI, 0.73-0.74 in Illinois). At specific decision thresholds, the NEWS outperformed the SIRS and qSOFA at all 28 hospitals either by reducing the percentage of at-risk patients who need to be screened by 5% to 20% or increasing the percentage of adverse outcomes identified by 3% to 25%.

CONCLUSIONS AND RELEVANCE

In all hospitalized patients evaluated in this study, including those meeting criteria for suspected infection, the NEWS appeared to display the highest discrimination. Our results suggest that, among commonly used points-based scoring systems, determining the NEWS for inpatient risk stratification could identify patients with and without infection at high risk of mortality.

Development and validation of early warning score system: A systematic literature review

OBJECTIVES

This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies.

METHODOLOGY

A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy.

RESULTS

A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database.

CONCLUSION

This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.

Effect of an Electronic Medical Record-Based Screening System on a Rapid Response System: 8-Years' Experience of a Single Center Cohort

An electronic medical record (EMR)-based screening system has been developed as a trigger system for a rapid response team (RRT) that traditionally used direct calling. We compared event characteristics, intensive care unit (ICU) admission, and 28-day mortality following RRT activation of the two trigger systems. A total of 10,026 events were classified into four groups according to the activation time (i.e., daytime or on-call time) and the triggering type (i.e., calling or screening). Among surgical patients, the ICU admission was lowest for the on-call screening group (26.2%). Compared to the on-call screening group, the on-call calling group and daytime calling group showed higher ICU admission with an odds ratio (OR) of 2.07 (95% CI 1.50–2.84, p < 0.001) and OR of 2.68 (95% CI 1.91–3.77, p < 0.001), respectively. The 28-day mortality was lowest for the on-call screening group (8.7%). Compared to the on-call screening group, on-call calling (OR 1.88, 95% CI 1.20–2.95, p = 0.006) and daytime calling (OR 1.89, 95% CI 1.17–3.05, p < 0.001) showed higher 28-day mortality. The EMR-based screening system might be useful in detecting at-risk surgical patients, particularly during on-call time. The clinical usefulness of an EMR-based screening system can vary depending on patients’ characteristics.

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports on the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals.

RESULTS

Each of the 26 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration

Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration. Methods. We abstracted 3 years of data (2014–2016) and identified patients on medical wards that died or were transferred to the intensive care unit. We developed a time-varying risk model and then implemented the model over a 10-week period to assess prospective predictive performance. We compared performance to our currently used tool, National Early Warning Score. In order to aid clinical decision making, we transformed the quantitative score into a three-level clinical decision support tool. Results. The developed risk score had an average area under the curve of 0.814 (95% confidence interval = 0.79–0.83) versus 0.740 (95% confidence interval = 0.72–0.76) for the National Early Warning Score. We found the proposed score was able to respond to acute clinical changes in patients’ clinical status. Upon implementing the score, we were able to achieve the desired positive predictive value but needed to retune the thresholds to get the desired sensitivity. Discussion. This work illustrates the potential for academic medical centers to build, refine, and implement risk models that are targeted to their patient population and work flow.

Adjusting Early Warning Score by clinical assessment: a study protocol for a Danish cluster-randomised, multicentre study of an Individual Early Warning Score (I-EWS)

INTRODUCTION

Track and trigger systems (TTSs) based on vital signs are implemented in hospitals worldwide to identify patients with clinical deterioration. TTSs may provide prognostic information but do not actively include clinical assessment, and their impact on severe adverse events remain uncertain. The demand for prospective, multicentre studies to demonstrate the effectiveness of TTSs has grown the last decade. Individual Early Warning Score (I-EWS) is a newly developed TTS with an aggregated score based on vital signs that can be adjusted according to the clinical assessment of the patient. The objective is to compare I-EWS with the existing National Early Warning Score (NEWS) algorithm regarding clinical outcomes and use of resources.

METHOD AND ANALYSIS

In a prospective, multicentre, cluster-randomised, crossover, non-inferiority study. Eight hospitals are randomised to use either NEWS in combination with the Capital Region of Denmark NEWS Override System (CROS) or implement I-EWS for 6.5 months, followed by a crossover. Based on their clinical assessment, the nursing staff can adjust the aggregated score with a maximum of -4 or +6 points. We expect to include 150 000 unique patients. The primary endpoint is all-cause mortality at 30 days. Coprimary endpoint is the average number of times per day a patient is NEWS/I-EWS-scored, and secondary outcomes are all-cause mortality at 48 hours and at 7 days as well as length of stay.

ETHICS AND DISSEMINATION

The study was presented for the Regional Ethics committee who decided that no formal approval was needed according to Danish law (J.no. 1701733). The I-EWS study is a large prospective, randomised multicentre study that investigates the effect of integrating a clinical assessment performed by the nursing staff in a TTS, in a head-to-head comparison with the internationally used NEWS with the opportunity to use CROS.

TRIAL REGISTRATION NUMBER

NCT03690128.

Involving patients in recognising clinical deterioration in hospital using the Patient Wellness Questionnaire: A mixed-methods study

Background

Measures exist to improve early recognition of, and response to deteriorating patients in hospital. Despite these, 7% of the deaths reported to the National Reporting and Learning System from acute hospitals in 2015 related to a failure to recognise or respond to deterioration. Interventions have been developed that allow patients and relatives to escalate patient deterioration to a critical care outreach team. However, there is not a strong evidence base for the clinical effectiveness of these interventions, or patients' ability to recognise deterioration.

Aims

The aims of this study were as follows. (a) To identify methods of involving patients in recognising deterioration in hospital, generated by health professionals. (b) To develop and evaluate an identified method of patient involvement in practice, and explore its feasibility and acceptability from the perspectives of patients.

Methods

The study used a mixed-methods design. A measure to capture patient-reported wellness during observation was developed (Patient Wellness Questionnaire) through focus group discussion with health professionals and patients, and piloted on inpatient wards.

Results

There was limited uptake where patients were asked to record ratings of their wellness using the Patient Wellness Questionnaire themselves. However, where the researcher asked patients about their wellness using the Patient Wellness Questionnaire and recorded their responses during observation, this was acceptable to most patients.

Conclusions

This study has developed a measure that can be used to routinely collect patient-reported wellness during observation in hospital and may potentially improve early detection of deterioration.

Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring

Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.

Predicting adverse hemodynamic events in critically ill patients

PURPOSE OF REVIEW

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized.

RECENT FINDINGS

Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains.

SUMMARY

Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

The efficacy of twelve early warning systems for potential use in regional medical facilities in Queensland, Australia

AIM

Early warning system (EWS) validation studies are conducted predominantly in tertiary metropolitan facilities and are not necessarily applicable to regional hospitals. This study evaluates 12 EWSs for use in regional subcritical hospitals.

METHOD

This is a retrospective case-control study of patients who experienced severe adverse events (SAEs) in two regional private hospitals. Vital signs collected over 72 h preceding the SAE were applied to 12 EWSs representing three classes of EWSs. The EWS area under the receiver operator characteristic curve (AUROC), sensitivity, specificity, and number of alerts were calculated.

RESULTS

Data from 159 index and 172 control patients showed no significant differences in demographics, length of stay, and level of comorbidities. Only half of index patients achieved a medical emergency alert threshold score. On average, index patients triggered alerts 20.06 (22.67) hours preceding the SAE and alerted 2.25 (3.87) times over 72 h. The AUROC ranged from 0.628 to 0.747, with a single-parameter EWS having the lowest AUROC and an aggregated weighted EWS, the highest. The sensitivity of the EWS ranges from 0.359 to 0.692. The specificity was greater than 0.9 for all the EWSs tested.

CONCLUSIONS

Based on the EWS sensitivity and AUROC, there is a lack of conclusive evidence of the efficacy of the 12 EWSs tested. However, because the adoption of the EWS in Australian hospitals is mandatory, the implementation of an aggregated weighted EWS, such as Compass, should be considered in subcritical regional private hospitals. Given that only half of SAE achieved an EWS medical alert threshold score, it is important that good clinical judgement be used with EWS.

Statistical Modeling and Aggregate-Weighted Scoring Systems in Prediction of Mortality and ICU Transfer: A Systematic Review

BACKGROUND

The clinical deterioration of patientsin general hospital wards is an important safety issue. Aggregate-weighted early warning systems (EWSs) may not detect risk until patients present with acute decline.

PURPOSE

We aimed to compare the prognostic test accuracy and clinical workloads generated by EWSs using statistical modeling (multivariable regression or machine learning) versus aggregate-weighted tools.

DATA SOURCES

We searched PubMed and CINAHL using terms that described clinical deterioration and use of an advanced EWS.

STUDY SELECTION

The outcome was clinical deterioration (intensive care unit transfer or death) of adult patients on general hospital wards. We included studies published from January 1, 2012 to September 15, 2018.

DATA EXTRACTION

Following 2015 PRIMSA systematic review protocol guidelines; 2015 TRIPOD criteria for predictive model evaluation; and the Cochrane Collaboration guidelines, we reported model performance, adjusted positive predictive value (PPV), and conducted simulations of workup-to-detection ratios.

DATA SYNTHESIS

Of 285 articles, six studies reported the model performance of advanced EWSs, and five were of high quality. All EWSs using statistical modeling identified at-risk patients with greater precision than aggregate-weighted EWSs (mean AUC 0.80 vs 0.73). EWSs using statistical modeling generated 4.9 alerts to find one true positive case versus 7.1 alerts in aggregate-weighted EWSs; a nearly 50% relative workload increase for aggregate-weighted EWSs.

CONCLUSIONS

Compared with aggregate-weighted tools, EWSs using statistical modeling consistently demonstrated superior prognostic performance and generated less workload to identify and treat one true positive case. A standardized approach to reporting EWS model performance is needed, including outcome definitions, pretest probability, observed and adjusted PPV, and workup-to-detection ratio.

Use of National Early Warning Score for observation for increased risk for clinical deterioration during post-ICU care at a surgical ward

Purpose

Patients transferred from an intensive care unit (ICU) to a general ward are at risk for clinical deterioration. The aim of the study was to determine if an increase in National Early Warning Score (NEWS) value predicted worse outcomes in surgical ward patients previously treated in the ICU.

Patients and methods

A retrospective observational study was conducted in a cohort of gastrointestinal surgery patients after transfer from an ICU/high dependency unit (HDU). NEWS values were collected throughout the ward admission. Clinical deterioration was defined by ICU readmission or death. The ability of NEWS to predict clinical deterioration was determined using a linear mixed effect model.

Results

We included 124 patients, age 65.9±14.5, 60% males with an ICU Simplified Acute Physiology Score II 33.8±12.7. No patients died unexpectedly at the ward and 20 were readmitted to an ICU/HDU. The NEWS values increased by a mean of 0.15 points per hour (intercept 3.7, P<0.001) before ICU/HDU readmission according to the linear mixed effect model. NEWS at transfer from ICU was the only factor that predicted readmission (OR 1.32; 95% CI 1.01–1.72; P=0.04) at the time of admission to the ward.

Conclusion

Clinical deterioration of surgical patients was preceded by an increase in NEWS.

Validation of deep-learning-based triage and acuity score using a large national dataset

AIM

Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset.

METHODS

We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables).

RESULTS

A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179).

CONCLUSION

Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.

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

Aims

To compare the ability of the National Early Warning Score (NEWS) and the National Early Warning Score 2 (NEWS2) to identify patients at risk of in-hospital mortality and other adverse outcomes.

Methods

We undertook a multi-centre retrospective observational study at five acute hospitals from two UK NHS Trusts. Data were obtained from completed adult admissions who were not fit enough to be discharged alive on the day of admission. Diagnostic coding and oxygen prescriptions were used to identify patients with type II respiratory failure (T2RF). The primary outcome was in-hospital mortality within 24 h of a vital signs observation. Secondary outcomes included unanticipated intensive care unit admission or cardiac arrest within 24 h of a vital signs observation. Discrimination was assessed using the c-statistic.

Results

Among 251,266 adult admissions, 48,898 were identified to be at risk of T2RF by diagnostic coding. In this group, NEWS2 showed statistically significant lower discrimination (c-statistic, 95% CI) for identifying in-hospital mortality within 24 h (0.860, 0.857–0.864) than NEWS (0.881, 0.878-0.884). For 1394 admissions with documented T2RF, discrimination was similar for both systems: NEWS2 (0.841, 0.827-0.855), NEWS (0.862, 0.848–0.875). For all secondary endpoints, NEWS2 showed no improvements in discrimination.

Conclusions

NEWS2 modifications to NEWS do not improve discrimination of adverse outcomes in patients with documented T2RF and decrease discrimination in patients at risk of T2RF. Further evaluation of the relationship between SpO₂ values, oxygen therapy and risk should be investigated further before wide-scale adoption of NEWS2.