Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study.

Background: The frequency at which patients should have their vital signs (e.g. blood pressure, pulse, oxygen saturation) measured on hospital wards is currently unknown. Current National Health Service monitoring protocols are based on expert opinion but supported by little empirical evidence. The challenge is finding the balance between insufficient monitoring (risking missing early signs of deterioration and delays in treatment) and over-observation of stable patients (wasting resources needed in other aspects of care). Objective: Provide an evidence-based approach to creating monitoring protocols based on a patient's risk of deterioration and link these to nursing workload and economic impact. Design: Our study consisted of two parts: (1) an observational study of nursing staff to ascertain the time to perform vital sign observations; and (2) a retrospective study of historic data on patient admissions exploring the relationships between National Early Warning Score and risk of outcome over time. These were underpinned by opinions and experiences from stakeholders. Setting and participants: Observational study: observed nursing staff on 16 randomly selected adult general wards at four acute National Health Service hospitals. Retrospective study: extracted, linked and analysed routinely collected data from two large National Health Service acute trusts; data from over 400,000 patient admissions and 9,000,000 vital sign observations. Results: Observational study found a variety of practices, with two hospitals having registered nurses take the majority of vital sign observations and two favouring healthcare assistants or student nurses. However, whoever took the observations spent roughly the same length of time. The average was 5:01 minutes per observation over a 'round', including time to locate and prepare the equipment and travel to the patient area. Retrospective study created survival models predicting the risk of outcomes over time since the patient was last observed. For low-risk patients, there was little difference in risk between 4 hours and 24 hours post observation. Conclusions: We explored several different scenarios with our stakeholders (clinicians and patients), based on how 'risk' could be managed in different ways. Vital sign observations are often done more frequently than necessary from a bald assessment of the patient's risk, and we show that a maximum threshold of risk could theoretically be achieved with less resource. Existing resources could therefore be redeployed within a changed protocol to achieve better outcomes for some patients without compromising the safety of the rest. Our work supports the approach of the current monitoring protocol, whereby patients' National Early Warning Score 2 guides observation frequency. Existing practice is to observe higher-risk patients more frequently and our findings have shown that this is objectively justified. It is worth noting that important nurse-patient interactions take place during vital sign monitoring and should not be eliminated under new monitoring processes. Our study contributes to the existing evidence on how vital sign observations should be scheduled. However, ultimately, it is for the relevant professionals to decide how our work should be used. Study registration: This study is registered as ISRCTN10863045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/05/03) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 6. See the NIHR Funding and Awards website for further award information.

Patient recovery in hospital is tracked by measuring heart rate, blood pressure and other 'vital signs' and converting them into a score. These are 'observed' regularly by nursing staff so that deterioration can be spotted early. However, taking observations can disturb patients, and taking them too often causes extra work for staff. More frequent monitoring is recommended for higher scores, but evidence is lacking. To work out how often patients should be monitored, we needed to know how likely it is for patients to become more unwell between observations. We analysed over 400,000 patient records from two hospitals to understand how scores change with time. We looked at three of the most serious risks for patients in hospital. These risks are dying, needing intensive care or having a cardiac arrest. We also looked at the risk that a patient's condition would deteriorate significantly before their measurements were taken again. We identified early signs of deterioration and how changes in vital signs affected the risk of a patient's condition becoming worse. From this we calculated a maximum risk of deterioration. We then calculated different monitoring schedules that keep individual patients below this risk level. Some of those would consume less staff time than current National Health Service guidelines suggest. We also watched staff record patients' vital signs. We learnt it takes about 5 minutes to take these measurements from each patient. This information helped us calculate how costs would change if patients' vital signs were taken more or less often. We found that patients with a low overall score could have their vital signs monitored less often without being in danger of serious harm. This frees up nursing time so that patients with a higher score can be monitored more often. Importantly, this can be achieved without employing more staff.

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.

Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust.

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.

P-POSSUM and the NELA Score Overpredict Mortality for Laparoscopic Emergency Bowel Surgery: An Analysis of the NELA Database.

BACKGROUND: Risk stratification has become a key part of the care processes for patients having emergency bowel surgery. This study aimed to determine if operative approach influences risk-model performance, and risk-adjusted mortality rates in the United Kingdom. METHODS: A prospectively planned analysis was conducted using National Emergency Laparotomy Audit (NELA) data from December 2013 to November 2018. The risk-models investigated were P-POSSUM and the NELA Score, with model performance assessed in terms of discrimination and calibration. Risk-adjusted mortality was assessed using Standardised Mortality Ratios (SMR). Analysis was performed for the total cohort, and cases performed open, laparoscopically and converted to open. Sub-analysis was performed for cases with ≤ 20% predicted mortality. RESULTS: Data were available for 116 396 patients with P-POSSUM predicted mortality, and 46 935 patients with the NELA score. Both models displayed excellent discrimination with little variation between operative approaches (c-statistic: P-POSSUM 0.801-0.836; NELA Score 0.811-0.862). The NELA score was well calibrated across all deciles of risk, but P-POSSUM over-predicted risk beyond 20% mortality. Calibration plots for operative approach demonstrated that both models increasingly over-predicted mortality for laparoscopy, relative to open and converted to open surgery. SMRs calculated using both models consistently demonstrated that risk-adjusted mortality with laparoscopy was a third lower than open surgery. CONCLUSION: Risk-adjusted mortality for emergency bowel surgery is lower for laparoscopy than open surgery, with P-POSSUM and NELA score both over-predicting mortality for laparoscopy. Operative approach should be considered in the development of future risk-models that rely on operative data.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System.

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

The performance of the National Early Warning Score and National Early Warning Score 2 in hospitalised patients infected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

INTRODUCTION: Since the introduction of the UK's National Early Warning Score (NEWS) and its modification, NEWS2, coronavirus disease 2019 (COVID-19), has caused a worldwide pandemic. NEWS and NEWS2 have good predictive abilities in patients with other infections and sepsis, however there is little evidence of their performance in COVID-19. METHODS: Using receiver-operating characteristics analyses, we used the area under the receiver operating characteristic (AUROC) curve to evaluate the performance of NEWS or NEWS2 to discriminate the combined outcome of either death or intensive care unit (ICU) admission within 24 h of a vital sign set in five cohorts (COVID-19 POSITIVE, n = 405; COVID-19 NOT DETECTED, n = 1716; COVID-19 NOT TESTED, n = 2686; CONTROL 2018, n = 6273; CONTROL 2019, n = 6523). RESULTS: The AUROC values for NEWS or NEWS2 for the combined outcome were: COVID-19 POSITIVE, 0.882 (0.868-0.895); COVID-19 NOT DETECTED, 0.875 (0.861-0.89); COVID-19 NOT TESTED, 0.876 (0.85-0.902); CONTROL 2018, 0.894 (0.884-0.904); CONTROL 2019, 0.842 (0.829-0.855). CONCLUSIONS: The finding that NEWS or NEWS2 performance was good and similar in all five cohorts (range = 0.842-0.894) suggests that amendments to NEWS or NEWS2, such as the addition of new covariates or the need to change the weighting of existing parameters, are unnecessary when evaluating patients with COVID-19. Our results support the national and international recommendations for the use of NEWS or NEWS2 for the assessment of acute-illness severity in patients with COVID-19.

Impact of the coronavirus pandemic on the patterns of vital signs recording and staff compliance with expected monitoring schedules on general wards.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) placed increased burdens on National Health Service hospitals and necessitated significant adjustments to their structures and processes. This research investigated if and how these changes affected the patterns of vital sign recording and staff compliance with expected monitoring schedules on general wards. METHODS: We compared the pattern of vital signs and early warning score (EWS) data collected from admissions to a single hospital during the initial phase of the COVID-19 pandemic with those in three control periods from 2018, 2019 and 2020. Main outcome measures were weekly and monthly hospital admissions; daily and hourly patterns of recorded vital signs and EWS values; time to next observation and; proportions of 'on time', 'late' and 'missed' vital signs observations sets. RESULTS: There were large falls in admissions at the beginning of the COVID-19 era. Admissions were older, more unwell on admission and throughout their stay, more often required supplementary oxygen, spent longer in hospital and had a higher in-hospital mortality compared to one or more of the control periods. More daily observation sets were performed during the COVID-19 era than in the control periods. However, there was no clear evidence that COVID-19 affected the pattern of vital signs collection across the 24-h period or the week. CONCLUSIONS: The increased burdens of the COVID-19 pandemic, and the alterations in healthcare structures and processes necessary to respond to it, did not adversely affect the hospitals' ability to monitor patients under its care and to comply with expected monitoring schedules.

Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation.

OBJECTIVES: Early warning scores (EWS) alerting for in-hospital deterioration are commonly developed using routinely collected vital-sign data from the whole in-hospital population. As these in-hospital populations are dominated by those over the age of 45 years, resultant scores may perform less well in younger age groups. We developed and validated an age-specific early warning score (ASEWS) derived from statistical distributions of vital signs. DESIGN: Observational cohort study. SETTING: Oxford University Hospitals (OUH) July 2013 to March 2018 and Portsmouth Hospitals (PH) NHS Trust January 2010 to March 2017 within the Hospital Alerting Via Electronic Noticeboard database. PARTICIPANTS: Hospitalised patients with electronically documented vital-sign observations OUTCOME: Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest. METHODS AND RESULTS: Statistical distributions of vital signs were used to develop an ASEWS to predict the composite outcome within 24 hours. The OUH development set consisted of 2 538 099 vital-sign observation sets from 142 806 admissions (mean age (SD): 59.8 (20.3)). We compared the performance of ASEWS to the National Early Warning Score (NEWS) and our previous EWS (MCEWS) on an OUH validation set consisting of 581 571 observation sets from 25 407 emergency admissions (mean age (SD): 63.0 (21.4)) and a PH validation set consisting of 5 865 997 observation sets from 233 632 emergency admissions (mean age (SD): 64.3 (21.1)). ASEWS performed better in the 16-45 years age group in the OUH validation set (AUROC 0.820 (95% CI 0.815 to 0.824)) and PH validation set (AUROC 0.840 (95% CI 0.839 to 0.841)) than NEWS (AUROC 0.763 (95% CI 0.758 to 0.768) and AUROC 0.836 (95% CI 0.835 to 0.838) respectively) and MCEWS (AUROC 0.808 (95% CI 0.803 to 0.812) and AUROC 0.833 (95% CI 0.831 to 0.834) respectively). Differences in performance were not consistent in the elder age group. CONCLUSIONS: Accounting for age-related vital sign changes can more accurately detect deterioration in younger patients.

The effect of fractional inspired oxygen concentration on early warning score performance: A database analysis.

OBJECTIVES: To calculate fractional inspired oxygen concentration (FiO2) thresholds in ward patients and add these to the National Early Warning Score (NEWS). To evaluate the performance of NEWS-FiO2 against NEWS when predicting in-hospital death and unplanned intensive care unit (ICU) admission. METHODS: A multi-centre, retrospective, observational cohort study was carried out in five hospitals from two UK NHS Trusts. Adult admissions with at least one complete set of vital sign observations recorded electronically were eligible. The primary outcome measure was an 'adverse event' which comprised either in-hospital death or unplanned ICU admission. Discrimination was assessed using the Area Under the Receiver Operating Characteristic curve (AUROC). RESULTS: A cohort of 83,304 patients from a total of 271,363 adult admissions were prescribed oxygen. In this cohort, NEWS-FiO2 (AUROC 0.823, 95% CI 0.819-0.824) outperformed NEWS (AUORC 0.811, 95% CI 0.809-0.814) when predicting in-hospital death or unplanned ICU admission within 24 h of a complete set of vital sign observations. CONCLUSIONS: NEWS-FiO2 generates a performance gain over NEWS when studied in ward patients requiring oxygen. This warrants further study, particularly in patients with respiratory disorders.

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.

Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model.

AIM: The National Early Warning System (NEWS) is based on vital signs; the Laboratory Decision Tree Early Warning Score (LDT-EWS) on laboratory test results. We aimed to develop and validate a new EWS (the LDTEWS:NEWS risk index) by combining the two and evaluating the discrimination of the primary outcome of unanticipated intensive care unit (ICU) admission or in-hospital mortality, within 24 h. METHODS: We studied emergency medical admissions, aged 16 years or over, admitted to Oxford University Hospitals (OUH) and Portsmouth Hospitals (PH). Each admission had vital signs and laboratory tests measured within their hospital stay. We combined LDT-EWS and NEWS values using a linear time-decay weighting function imposed on the most recent blood tests. The LDTEWS:NEWS risk index was developed using data from 5 years of admissions to PH, and validated on a year of data from both PH and OUH. We tested the risk index's ability to discriminate the primary outcome using the c-statistic. RESULTS: The development cohort contained 97,933 admissions (median age = 73 years) of which 4723 (4.8%) resulted inhospital death and 1078 (1.1%) in unanticipated ICU admission. We validated the risk index using data from PH (n = 21,028) and OUH (n = 16,383). The risk index showed a higher discrimination in the validation sets (c-statistic value (95% CI)) (PH, 0.901 (0.898-0.905); OUH, 0.916 (0.911-0.921)), than NEWS alone (PH, 0.877 (0.873-0.882); OUH, 0.898 (0.893-0.904)). CONCLUSIONS: The LDTEWS:NEWS risk index increases the ability to identify patients at risk of deterioration, compared to NEWS alone.

A Comparison of the Quick Sequential (Sepsis-Related) Organ Failure Assessment Score and the National Early Warning Score in Non-ICU Patients With/Without Infection.

OBJECTIVES: The Sepsis-3 task force recommended the quick Sequential (Sepsis-Related) Organ Failure Assessment score for identifying patients with suspected infection who are at greater risk of poor outcomes, but many hospitals already use the National Early Warning Score to identify high-risk patients, irrespective of diagnosis. We sought to compare the performance of quick Sequential (Sepsis-Related) Organ Failure Assessment and National Early Warning Score in hospitalized, non-ICU patients with and without an infection. DESIGN: Retrospective cohort study. SETTING: Large U.K. General Hospital. PATIENTS: Adults hospitalized between January 1, 2010, and February 1, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied the quick Sequential (Sepsis-Related) Organ Failure Assessment score and National Early Warning Score to 5,435,344 vital signs sets (241,996 hospital admissions). Patients were categorized as having no infection, primary infection, or secondary infection using International Classification of Diseases, 10th Edition codes. National Early Warning Score was significantly better at discriminating in-hospital mortality, irrespective of infection status (no infection, National Early Warning Score 0.831 [0.825-0.838] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.688 [0.680-0.695]; primary infection, National Early Warning Score 0.805 [0.799-0.812] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.677 [0.670-0.685]). Similarly, National Early Warning Score performed significantly better in all patient groups (all admissions, emergency medicine admissions, and emergency surgery admissions) for all outcomes studied. Overall, quick Sequential (Sepsis-Related) Organ Failure Assessment performed no better, and often worse, in admissions with infection than without. CONCLUSIONS: The National Early Warning Score outperforms the quick Sequential (Sepsis-Related) Organ Failure Assessment score, irrespective of infection status. These findings suggest that quick Sequential (Sepsis-Related) Organ Failure Assessment should be reevaluated as the system of choice for identifying non-ICU patients with suspected infection who are at greater risk of poor outcome.

What impact does nursing care left undone have on patient outcomes? Review of the literature.

AIMS AND OBJECTIVES: Systematic review of the impact of missed nursing care on outcomes in adults, on acute hospital wards and in nursing homes. BACKGROUND: A considerable body of evidence supports the hypothesis that lower levels of registered nurses on duty increase the likelihood of patients dying on hospital wards, and the risk of many aspects of care being either delayed or left undone (missed). However, the direct consequence of missed care remains unclear. DESIGN: Systematic review. METHODS: We searched Medline (via Ovid), CINAHL (EBSCOhost) and Scopus for studies examining the association of missed nursing care and at least one patient outcome. Studies regarding registered nurses, healthcare assistants/support workers/nurses' aides were retained. Only adult settings were included. Because of the nature of the review, qualitative studies, editorials, letters and commentaries were excluded. PRISMA guidelines were followed in reporting the review. RESULTS: Fourteen studies reported associations between missed care and patient outcomes. Some studies were secondary analyses of a large parent study. Most of the studies used nurse or patient reports to capture outcomes, with some using administrative data. Four studies found significantly decreased patient satisfaction associated with missed care. Seven studies reported associations with one or more patient outcomes including medication errors, urinary tract infections, patient falls, pressure ulcers, critical incidents, quality of care and patient readmissions. Three studies investigated whether there was a link between missed care and mortality and from these results no clear associations emerged. CONCLUSIONS: The review shows the modest evidence base of studies exploring missed care and patient outcomes generated mostly from nurse and patient self-reported data. To support the assertion that nurse staffing levels and skill mix are associated with adverse outcomes as a result of missed care, more research that uses objective staffing and outcome measures is required. RELEVANCE TO CLINICAL PRACTICE: Although nurses may exercise judgements in rationing care in the face of pressure, there are nonetheless adverse consequences for patients (ranging from poor experience of care to increased risk of infection, readmissions and complications due to critical incidents from undetected physiological deterioration). Hospitals should pay attention to nurses' reports of missed care and consider routine monitoring as a quality and safety indicator.

National Early Warning Score Accurately Discriminates the Risk of Serious Adverse Events in Patients With Liver Disease.

BACKGROUND & AIMS: The National Early Warning Score (NEWS) is used to identify deteriorating adult hospital inpatients. However, it includes physiological parameters frequently altered in patients with cirrhosis. We aimed to assess the performance of the NEWS in acute and chronic liver diseases. METHODS: We collected vital signs, recorded in real time, from completed consecutive admissions of patients 16 years or older to a large acute-care hospital in Southern England, from January 1, 2010, through October 31, 2014. Using International Classification of Diseases, 10th revision, codes, we categorized patients as having primary liver disease, secondary liver disease, or none. For patients with liver disease, 2 analysis groups were developed: the first was based on clinical group (such as acute or chronic, alcohol-induced, or associated with portal hypertension), and the second was based on a summary of liver-related, hospital-level mortality indicator diagnoses. For each, we compared the abilities of the NEWS and 34 other early warning scores to discriminate 24-hour mortality, cardiac arrest, or unanticipated admission to the intensive care unit using the area under the receiver operating characteristic (AUROC) curve and early warning score efficiency curve analyses. RESULTS: The NEWS identified patients with primary, nonprimary, and no diagnoses of liver disease with AUROC values of 0.873 (95% CI, 0.860-0.886), 0.898 (95% CI, 0.891-0.905), and 0.879 (95% CI, 0.877-0.881), respectively. High AUROC values were also obtained for all clinical subgroups; the NEWS identified patients with alcohol-related liver disease with an AUROC value of 0.927 (95% CI, 0.912-0.941). The NEWS identified patients with liver diseases with higher AUROC values than other early warning scoring systems. CONCLUSIONS: The NEWS accurately discriminates patients at risk of death, admission to the intensive care unit, or cardiac arrest within a 24-hour period for a range of liver-related diagnoses. Its widespread use provides a ready-made, easy-to-use option for identifying patients with liver disease who require early assessment and intervention, without the need to modify parameters, weightings, or escalation criteria.