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Background The National Early Warning Score (NEWS) is implemented internationally for in-hospital monitoring. It has been superior to other predictive scores, but its preventive abilities are still unclear. Additionally, data on patients who experience critical events but are not identified by NEWS as being at risk are scarce. We aimed to explore the National Early Warning Score (NEWS) as an actionable trigger to flag high-risk patients for unplanned transfers from a ward to an intensive care unit (ICU). Methods Single-centre, retrospective study with case record reviews of all adult, unplanned ICU admissions from a ward to an ICU (level 2 and/ or level 3 ICU) for one year in a Norwegian, 200-bed, urban hospital. We examined the portion of patients flagged by a NEWS of five or seven within 24 h of an ICU transfer, if there was a change in NEWS from the previous 48 h, and how NEWS findings in this patient population differed from a general ward population. Results Among 264 unplanned transfers from a ward to an ICU, 164 (62%) and 121 (46%) were flagged by a NEWS of five or seven, respectively. Up to 31% had a change in their NEWS, crossing the five-threshold from the previous 48 h. In contrast, nearly one in five (2077 of 11,310) of all adult admissions to the wards had at least one NEWS of five or higher, though with large variations between departments. Conclusion NEWS did not predictably identify patients who were urgently transferred to an ICU from a ward. Less than one-third could have been identified by a recent change in their NEWS, and more than one-third did not meet the criteria of a moderately high NEWS (of five). In addition, a large portion of the ward population have NEWS of five or higher during their hospital stay. Our study emphasizes the vital role of clinical judgment in interaction with early warning scores.

Background Emergency department (ED) overcrowding has become a global public health concern, underscoring the importance of rapid and reliable risk stratification tools. Early warning scores are widely used to identify patients at risk of deterioration and mortality. The recently developed International Early Warning Score (IEWS), which incorporates age and sex adjustments into the National Early Warning Score (NEWS) model, has shown promising results and has undergone initial external validation in a Danish cohort; however, no prospective external validation has yet been conducted, and broader international validation remains limited. This study aimed to evaluate the performance of IEWS compared with NEWS in predicting in-hospital mortality, 30-day mortality, and ICU admission among adult ED patients. Methods This prospective observational cohort study was conducted between July and August 2024 in a tertiary university hospital ED with an annual census of ~ 70,000 visits. Adult patients presenting to the ED were included, while trauma cases, patients without vital signs on arrival, interhospital transfers, and cases with incomplete data were excluded. IEWS and NEWS were calculated at presentation. The primary outcome was all-cause in-hospital mortality; secondary outcomes included 30-day mortality and ICU admission. Results A total of 8,666 patients were analyzed. The median age was 40 years (IQR: 26–58), and 51.5% were female. In-hospital mortality was 1.5% ( n  = 134), and 30-day mortality was 1.9% ( n  = 163). IEWS demonstrated excellent discriminative ability for in-hospital and 30-day mortality (AUC: 0.944 and 0.930, respectively), and good performance for ICU admission (AUC: 0.876). In contrast, NEWS showed good performance for in-hospital and 30-day mortality (AUC: 0.884 and 0.848, respectively) and moderate performance for ICU admission (AUC: 0.781). IEWS consistently outperformed NEWS across all outcomes ( p  < 0.05, DeLong’s test). Conclusion IEWS outperformed NEWS in predicting in-hospital mortality, 30-day mortality, and ICU admission among non-traumatic ED patients. Given its high sensitivity, specificity, and overall discriminative performance, IEWS may serve as a reliable bedside tool for patient risk stratification in the ED. Large-scale multicenter studies are needed to confirm its generalizability across diverse populations. Clinical trial number Not applicable.

Limited research exists on the effectiveness of the New Zealand Early Warning Score (NZEWS). To determine the impact of a modified NZEWS (M–NZEWS) and NZEWS on ward patients’ medical emergency team activation triggers. Mixed methods sequential design. Three phases included: 1) review of M–NZEWS electronic data to determine the effect of a M–NZEWS and NZEWS on ward patients; 2) an in-depth review of 20 Māori patients allocated to lower escalation zones if the NZEWS were adopted and 3) the number of electronic medical emergency team activation triggers compared to the number of actual medical emergency team activations. 1255 patients and 3505 vital sign data sets were analysed. Adopting the NZEWS would result in 396 (26.8%) fewer patients triggering a medical emergency team activation. The biggest impact would be on Māori, with 38.6% of Māori allocated to a lower escalation zone. Only 51.2% of patients with a medical emergency team activation had vital signs triggering the response electronically documented. Changing from the M–NZEWS to NZEWS will reduce the number of medical emergency team activation triggers, with the biggest impact on Māori. Electronic vital sign data does not accurately reflect the number of ward medical emergency team triggers or activations.

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability.

This study aimed to develop a new model on the basis of the National Early Warning Score to predict intensive care unit admission and the mortality of patients with acute pancreatitis. Patients diagnosed with acute pancreatitis in the emergency department were enrolled. The values of the National Early Warning Score, Modified Early Warning Score, and Bedside Index of Severity in Acute Pancreatitis in predicting intensive care unit admission and mortality of patients with acute pancreatitis were evaluated. A total of 379 patients with acute pancreatitis were enrolled; 77 patients (20.3%) were admitted to the intensive care unit and 14 (3.7%) died. The National Early Warning Score and calcium level were identified as independent risk factors of intensive care unit admission. Serum calcium exhibited a moderate correlation with National Early Warning Score (r = -0.46; P < 0.001), Modified Early Warning Score (r = -0.37; P < 0.001), and Bedside Index of Severity in Acute Pancreatitis (r = -0.39; P < 0.001). A new model called National Early Warning Score-calcium was developed by combining National Early Warning Score and calcium blood test result, which had larger areas under the curve for predicting intensive care unit admission and mortality than the other 3 scoring systems. A new model developed by combining National Early Warning Score and calcium exhibited better value in predicting the prognosis of acute pancreatitis than the models involving National Early Warning Score, Modified Early Warning Score, and Bedside Index of Severity in Acute Pancreatitis alone.

Aims To explore Registered Nurses' experiences and perceptions with National Early Warning Score and Individual Early Warning Score to identify patient deterioration. Design A qualitative exploratory design. Methods Six focus groups were conducted at six Danish hospitals from February to June 2019. Registered Nurses from both medical, surgical and emergency departments participated. The focus groups were analysed using content analysis. Results One theme and four categories were identified. Theme: Meaningful in identifying patient deterioration but causing frustration due to lack of flexibility. Categories: (a) Inter‐professional collaboration strengthened through the use of Early Warning Score systems, (b) Enhanced professional development and communication among nurses when using Early Warning Score systems, (c) Detecting patient deterioration by integrating nurses' clinical gaze with Early Warning Score systems and (d) Modification and fear of making mistakes when using Early Warning Score systems.

This year is the 10th anniversary since the launch of the National Early Warning Score (NEWS) by the Royal College of Physicians in 2012. This review reflects on the journey, from the nascent concept of a standardised system to detect acute illness severity and clinical deterioration through to the adoption of NEWS2 by the NHS and, ultimately, its incorporation into quality indicators of acute care provision. The impact of NEWS/NEWS2 on the transformation of provision and configuration and training of acute care teams in hospitals is reviewed. User feedback has been key in iterating guidance on the use of NEWS/NEWS2 and key elements of this are discussed. The ultimate aim of NEWS was to improve patient outcomes with acute illness or deterioration and the impact on outcomes is now becoming apparent but, paradoxically, an effective response can eliminate the link between the score and the ultimate outcome. This review concludes with a reflection on what the next 10 years may bring, particularly with the digital transformation of healthcare and its potential impact on scoring systems, as well as the necessary permeation of NEWS2 beyond the acute hospital setting into emergency response triage in primary and community care settings. Ten years on, via NEWS/NEWS2, the NHS is the first healthcare system globally with a ‘common language’ of illness severity and a standardised early warning system for acute clinical illness and deterioration, a system that is now being replicated in many other areas of the world.

We aimed to describe and evaluate the National Early Warning Score (NEWS) in the 24 hours preceding an in-hospital cardiac arrest among general somatic ward patients. The 24 hours preceding the in-hospital cardiac arrest were divided into four timespans and analysed by a medical record review of 127:254 matched case-control patients. The median NEWS ranged from 3 (2–6) to 6 (3–9) points for cases vs 1 (0–3) to 1 (0–3) point for controls. The proportion of cases ranged from 23–45% at high risk vs 3–6% for controls. The NEWS high-risk category was associated with an increase of 3.17 (95% confidence interval (CI) 1.66–6.04) to 4.43 (95% CI 2.56–7.67) in odds of in-hospital cardiac arrest compared to the low-risk category. NEWS, with its intuitive and for healthcare staff easy to interpret risk classification, is suitable for discriminating deteriorating patients with major deviating vital signs scoring high risk on NEWS.

To systematically review and critically appraise the methodology of developing modified obstetric early warning scores (MOEWSs). We searched Medline, CINAHL, EMBASE, and the Web of Science for MOEWS studies published between January 1, 2000, and December 31, 2022. Eligible studies included models predicting maternal death, intensive care unit (ICU) admission, and/or a composite of two or more maternal morbidities occurring in a hospital setting in women of any gestational age and up to 1 week after the end of pregnancy. Models were critically appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and adherence to the transparent reporting of prediction models (TRIPOD). 20 studies were included: five (25%) were model development studies, five (25%) were model development and validation, and ten (50%) were validation only. Four development studies used statistical methods, and the remaining six studies used clinical consensus (ie, expert opinion). The four data-driven model development studies did not address key statistical challenges, such as repeated measures or missing data, nor did they assess the performance adequately or dataset characteristics clearly. All but one study (95%) were rated at high risk of bias due to data sources, poor reporting, and analysis limitations. The fifteen validation studies were poorly reported and eleven (73%) were at high risk of bias. None of the data-driven models were independently validated, a key step toward implementation. There is a lack of MOEWSs developed using methods that follow recommended statistical guidelines. Substantial problems with the methodological quality of included development and validation studies, along with high risk of bias,indicating published scores could perform poorly or be potentially harmful if used in clinical practice. Future work should address handling missing data and repeated measures and consider how an MOEWS will perform in different populations and key subgroups. •Several MOEWSs have been developed, but most models were not derived using statistical methods.•Developed models are rarely implemented, even if they have been externally validated.•Methodological deficiencies and poor reporting led to nearly all studies being at high risk of bias.

Background Early warning scores (EWS) are widely used in emergency departments and on general wards to detect critical illness and deterioration. TOKS (“Tidlig Opsporing af Kritisk Sygdom”) is an early warning score used in Central Denmark Region to monitor hospitalized patients. The objective of this study is to investigate whether inclusion of supplement in the TOKS algorithm (modified TOKS; mTOKS), would improve the ability to predict 7-day mortality. Secondarily, we compare the discriminatory ability between TOKS, mTOKS and the National Early Warning Score (NEWS). Methods This is a prediction study including a cohort of adult patients who attended an emergency department in Central Denmark Region during a 3-month period in 2015. The discriminatory ability of TOKS, mTOKS and NEWS was evaluated by calculating the area under the receiver operating characteristics- curve (AUROC) with 7-day mortality as outcome. mTOKS was defined by adding 2 points for oxygen supplement to the normal TOKS score. Results 18.853 patients were included. AUROC for TOKS: 0,78 (95%-CI: 0,76-0,81). AUROC for mTOKS: 0,81 (95 %-CI: 0,78-0,83). AUROC for NEWS: 0,83 (95%-CI: 0,80-0,85). The predictive ability of all three early warning scores are statistically significantly different from each other ( p -value < 0,01). Conclusion The discriminatory ability of TOKS improved statistically by including oxygen supplement. All models showed moderate to good discriminatory ability.