A mixed-methods study to investigate feasibility and acceptability of an early warning score for preterm infants in neonatal units in Kenya: results of the NEWS-K study : Neonatal early warning scores in Kenya.

Preterm birth (< 37 weeks gestation) complications are the leading cause of neonatal mortality. Early-warning scores (EWS) are charts where vital signs (e.g., temperature, heart rate, respiratory rate) are recorded, triggering action. To evaluate whether a neonatal EWS improves clinical outcomes in low-middle income countries, a randomised trial is needed. Determining whether the use of a neonatal EWS is feasible and acceptable in newborn units, is a prerequisite to conducting a trial. We implemented a neonatal EWS in three newborn units in Kenya. Staff were asked to record infants' vital signs on the EWS during the study, triggering additional interventions as per existing local guidelines. No other aspects of care were altered. Feasibility criteria were pre-specified. We also interviewed health professionals (n = 28) and parents/family members (n = 42) to hear their opinions of the EWS. Data were collected on 465 preterm and/or low birthweight (< 2.5 kg) infants. In addition to qualitative study participants, 45 health professionals in participating hospitals also completed an online survey to share their views on the EWS. 94% of infants had the EWS completed at least once during their newborn unit admission. EWS completion was highest on the day of admission (93%). Completion rates were similar across shifts. 15% of vital signs triggered escalation to a more senior member of staff. Health professionals reported liking the EWS, though recognised the biggest barrier to implementation was poor staffing. Newborn unit infant to staff ratios varied between 10 and 53 staff per 1 infant, depending upon time of shift and staff type. A randomised trial of neonatal EWS in Kenya is possible and acceptable, though adaptations are required to the form before implementation.

Early Warning Systems for Critical Illness Outside the Intensive Care Unit.

Early warning systems (EWSs) are designed and deployed to create a rapid assessment and response for patients with clinical deterioration outside the intensive care unit (ICU). These models incorporate patient-level data such as vital signs and laboratory values to detect or prevent adverse clinical events, such as vital signs and laboratories to allow detection and prevention of adverse clinical events such as cardiac arrest, intensive care transfer, or sepsis. The applicability, development, clinical utility, and general perception of EWS in clinical practice vary widely. Here, we review the field as it has grown from early vital sign-based scoring systems to contemporary multidimensional algorithms and predictive technologies for clinical decompensation outside the ICU.

Predicting the potentially exacerbation of severe viral pneumonia in hospital by MuLBSTA score joint CD4 + and CD8 +T cell counts: construction and verification of risk warning model.

PURPOSE: This study mainly focuses on the immune function and introduces CD4+, CD8+ T cells and their ratios based on the MuLBSTA score, a previous viral pneumonia mortality risk warning model, to construct an early warning model of severe viral pneumonia risk. METHODS: A retrospective single-center observational study was operated from January 2021 to December 2022 at the People's Hospital of Liangjiang New Area, Chongqing, China. A total of 138 patients who met the criteria for viral pneumonia in hospital were selected and their data, including demographic data, comorbidities, laboratory results, CT scans, immunologic and pathogenic tests, treatment regimens, and clinical outcomes, were collected and statistically analyzed. RESULTS: Forty-one patients (29.7%) developed severe or critical illness. A viral pneumonia severe risk warning model was successfully constructed, including eight parameters: age, bacterial coinfection, CD4+, CD4+/CD8+, multiple lung lobe infiltrations, smoking, hypertension, and hospital admission days. The risk score for severe illness in patients was set at 600 points. The model had good predictive performance (AUROC = 0.94397), better than the original MuLBSTA score (AUROC = 0.8241). CONCLUSION: A warning system constructed based on immune function has a good warning effect on the risk of severe conversion in patients with viral pneumonia.

[Research progress in emergency assessment of acute poisoning].

Acute poisoning represents a prevalent critical illness jeopardizing patient survival. Early, precise assessment of the condition and subsequent appropriate therapeutic intervention are pivotal in enhancing treatment success rates. Currently, a standardized approach to evaluating the severity of acute poisoning is lacking. Various scoring systems, including Poisoning Severity Score (PSS) , Modified Early Warning Score (MEWS) , and Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) , offer valuable insights into acute poisoning assessment. Nevertheless, the distinct attributes of each scoring system constrain their broad clinical utility. Confronted with the intricate clinical demands of acute poisoning, the adoption of staged and dynamic assessment strategies is imperative to ascertain the condition of acute poisoning patients with greater accuracy.

Optimal timing for the Modified Early Warning Score for prediction of short-term critical illness in the acute care chain: a prospective observational study.

INTRODUCTION: The Modified Early Warning Score (MEWS) is an effective tool to identify patients in the acute care chain who are likely to deteriorate. Although it is increasingly being implemented in the ED, the optimal moment to use the MEWS is unknown. This study aimed to determine at what moment in the acute care chain MEWS has the highest accuracy in predicting critical illness. METHODS: Adult patients brought by ambulance to the ED at both locations of the Amsterdam UMC, a level 1 trauma centre, were prospectively included between 11 March and 28 October 2021. MEWS was calculated using vital parameters measured prehospital, at ED presentation, 1 hour and 3 hours thereafter, imputing for missing temperature and/or consciousness, as these values were expected not to deviate. Critical illness was defined as requiring intensive care unit admission, myocardial infarction or death within 72 hours after ED presentation. Accuracy in predicting critical illness was assessed using the area under the receiver operating characteristics curve (AUROC). RESULTS: Of the 790 included patients, critical illness occurred in 90 (11.4%). MEWS based on vital parameters at ED presentation had the highest performance in predicting critical illness with an AUROC of 0.73 (95% CI 0.67 to 0.79) but did not significantly differ compared with other moments. Patients with an increasing MEWS over time are significantly more likely to become critical ill compared with patients with an improving MEWS. CONCLUSION: The performance of MEWS is moderate in predicting critical illness using vital parameters measured surrounding ED admission. However, an increase of MEWS during ED admission is correlated with the development of critical illness. Therefore, early recognition of deteriorating patients at the ED may be achieved by frequent MEWS calculation. Further studies should investigate the effect of continuous monitoring of these patients at the ED.

Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record.

OBJECTIVE: Obtain clinicians' perspectives on early warning scores (EWS) use within context of clinical cases. MATERIAL AND METHODS: We developed cases mimicking sepsis situations. De-identified data, synthesized physician notes, and EWS representing deterioration risk were displayed in a simulated EHR for analysis. Twelve clinicians participated in semi-structured interviews to ascertain perspectives across four domains: (1) Familiarity with and understanding of artificial intelligence (AI), prediction models and risk scores; (2) Clinical reasoning processes; (3) Impression and response to EWS; and (4) Interface design. Transcripts were coded and analyzed using content and thematic analysis. RESULTS: Analysis revealed clinicians have experience but limited AI and prediction/risk modeling understanding. Case assessments were primarily based on clinical data. EWS went unmentioned during initial case analysis; although when prompted to comment on it, they discussed it in subsequent cases. Clinicians were unsure how to interpret or apply the EWS, and desired evidence on its derivation and validation. Design recommendations centered around EWS display in multi-patient lists for triage, and EWS trends within the patient record. Themes included a "Trust but Verify" approach to AI and early warning information, dichotomy that EWS is helpful for triage yet has disproportional signal-to-high noise ratio, and action driven by clinical judgment, not the EWS. CONCLUSIONS: Clinicians were unsure of how to apply EWS, acted on clinical data, desired score composition and validation information, and felt EWS was most useful when embedded in multi-patient views. Systems providing interactive visualization may facilitate EWS transparency and increase confidence in AI-generated information.

Using the NEWS2 and ABCDE assessment to identify early signs of clinical deterioration.

Nurses may encounter deteriorating patients in their clinical practice, so they require an understanding of the early physiological signs of deterioration and a structured approach to patient assessment. This enables appropriate management and a timely response to the most life-threatening issues identified, such as a compromised airway. This article describes how nurses can use early warning scores and a structured patient assessment, using the ABCDE (airway, breathing, circulation, disability, exposure) framework, to identify early signs of deterioration and facilitate the timely escalation of patient care where necessary.

Prehospital early warning scores for adults with suspected sepsis: the PHEWS observational cohort and decision-analytic modelling study.

Background: Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department. Objectives: To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment. Design: Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness. Setting: Two ambulance services and four acute hospitals in England. Participants: Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded. Interventions: Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation. Main outcome measures: Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained. Results: Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed. Limitations: We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling. Conclusions: No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis. Study registration: This study is registered as Research Registry (reference: researchregistry5268). Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

Sepsis is a life-threatening condition in which an abnormal response to infection causes heart, lung or kidney failure. People with sepsis need urgent treatment. They need to be prioritised at the emergency department rather than waiting in the queue. Paramedics attempt to identify people with possible sepsis using an early warning score (based on simple measurements, such as blood pressure and heart rate) alongside their impression of the patient's diagnosis. They can then alert the hospital to assess the patient quickly. However, an inaccurate early warning score might miss cases of sepsis or unnecessarily prioritise people without sepsis. We aimed to measure how accurately early warning scores identified people with sepsis when used alongside paramedic diagnostic impression. We collected data from 71,204 people that two ambulance services transported to four different hospitals in 2019. We recorded paramedic diagnostic impressions and calculated early warning scores for each patient. At one hospital, we linked ambulance records to hospital records and identified who had sepsis. We then calculated the accuracy of using the scores alongside diagnostic impression to diagnose sepsis. Finally, we used modelling to predict how many patients (with and without sepsis) paramedics would prioritise using different strategies based on early warning scores and diagnostic impression. We found that none of the currently available early warning scores were ideal. When they were applied to all patients, they prioritised too many people. When they were only applied to patients whom the paramedics thought had infection, they missed many cases of sepsis. The NEWS2, score, which ambulance services already use, was as good as or better than all the other scores we studied. We found that using the NEWS2, score in people with a paramedic impression of infection could achieve a reasonable balance between prioritising too many patients and avoiding missing patients with sepsis.

Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

Importance: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioration, but there is limited evidence for their effectiveness. Objective: To evaluate the effectiveness of an artificial intelligence deterioration model-enabled intervention to reduce the risk of escalations in care among hospitalized patients using a study design that facilitates stronger causal inference. Design, Setting, and Participants: This cohort study used a regression discontinuity design that controlled for confounding and was based on Epic Deterioration Index (EDI; Epic Systems Corporation) prediction model scores. Compared with other observational research, the regression discontinuity design facilitates causal analysis. Hospitalized adults were included from 4 general internal medicine units in 1 academic hospital from January 17, 2021, through November 16, 2022. Exposure: An artificial intelligence deterioration model-enabled intervention, consisting of alerts based on an EDI score threshold with an associated collaborative workflow among nurses and physicians. Main Outcomes and Measures: The primary outcome was escalations in care, including rapid response team activation, transfer to the intensive care unit, or cardiopulmonary arrest during hospitalization. Results: During the study, 9938 patients were admitted to 1 of the 4 units, with 963 patients (median [IQR] age, 76.1 [64.2-86.2] years; 498 males [52.3%]) included within the primary regression discontinuity analysis. The median (IQR) Elixhauser Comorbidity Index score in the primary analysis cohort was 10 (0-24). The intervention was associated with a -10.4-percentage point (95% CI, -20.1 to -0.8 percentage points; P = .03) absolute risk reduction in the primary outcome for patients at the EDI score threshold. There was no evidence of a discontinuity in measured confounders at the EDI score threshold. Conclusions and Relevance: Using a regression discontinuity design, this cohort study found that the implementation of an artificial intelligence deterioration model-enabled intervention was associated with a significantly decreased risk of escalations in care among inpatients. These results provide evidence for the effectiveness of this intervention and support its further expansion and testing in other care settings.

Is the National Early Warning Score able to identify nursing activity load? A prospective observational study.

BACKGROUND: The National Early Warning Score scale correlates well with the intensity of the patient's acute condition. It could also correlate with the nursing activity load and prove useful in defining and redistributing nursing resources based on the acuity of patients. AIM: To assess whether patients' National Early Warning Score at hospital admission correlates with objective nursing demands and can be used to optimize the distribution of available care resources. METHODS: This single-center prospective study included patients admitted to the Department of Internal Medicine at the Civil Hospital in Altovicentino (Italy) between September 1 and December 31, 2022. Nursing activities were recorded for the first three days after admission and standardized to the daily mean as performance/5 min/patient/day. Linear regression was used to assess the correlation between nursing demands for different National Early Warning Scores. RESULTS: This study included 333 patients. Their mean National Early Warning Score was 3.9 (standard deviation: 2.9), with 61 % (203/333) in the National Early Warning Score <5 category, 19.5 % (65/333) in the National Early Warning Score 5-6 category, and 19.5 % (65/333) in the National Early Warning Score >6 category. Their average daily care requirements increased from 22 (16-30) activities/5 min/patient/day in the low National Early Warning Score category to 30 (20-39) activities/5 min/patient/day in the intermediate National Early Warning Score category (p < 0.001) and 35 (23-45) activities/5 min/patient/day in the high National Early Warning Score category (p < 0.001). CONCLUSION: The National Early Warning Score correlates with nursing care activities for patients with an acute condition and can be used to optimize the distribution of available care resources.

Early warning scores for sepsis identification and prediction of in-hospital mortality in adults with sepsis: A systematic review and meta-analysis.

AIM: The early warning scores (EWS), quick Sequential Organ Failure Assessment (qSOFA) and systemic inflammatory response syndrome (SIRS) criteria have been proposed as sepsis screening tools. This review aims to summarise and compare the performance of EWS with the qSOFA and SIRS criteria for predicting sepsis diagnosis and in-hospital mortality in patients with sepsis. DESIGN: A systematic review with meta-analysis. REVIEW METHODS: Seven databases were searched from January 1, 2016 until March 10, 2022. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Sensitivity, specificity, likelihood ratios and diagnostic odd ratios were pooled by using the bivariate random effects model. Overall performance was summarised by using the hierarchical summary receiver-operating characteristics curve. This paper adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. RESULTS: Ten studies involving 52,474 subjects were included in the review. For predicting sepsis diagnosis, the pooled sensitivity of EWS (65%, 95% CI: 55, 75) was similar to SIRS ≥2 (70%, 95% CI: 49, 85) and higher than qSOFA ≥2 (37%, 95% CI: 20, 59). The pooled specificity of EWS (77%, 95% CI: 64, 86) was higher than SIRS ≥2 (62%, 95% CI: 41, 80) but lower than qSOFA ≥2 (94%, 95% CI: 86, 98). Results were similar for the secondary outcome of in-hospital mortality. CONCLUSIONS: Although no one scoring system had both high sensitivity and specificity, the EWS had at least equivalent values in most measures of diagnostic accuracy compared with SIRS or qSOFA. IMPLICATIONS FOR THE PROFESSION: Healthcare systems in which EWS is already in place should consider whether there is any clinical benefit in adopting qSOFA or SIRS. NO PATIENT OR PUBLIC CONTRIBUTION: This systematic review did not directly involve patient or public contribution to the manuscript.

Comparison of Early Warning Scores in Determining the Prognosis of COVID-19 Patients.

OBJECTIVE:  To compare the effectiveness of early warning score systems in predicting 30-day poor outcomes in Coronavirus Disease (COVID-19) patients admitted to the emergency department. STUDY DESIGN: Descriptive study. Place and and Duration of the Study: Fatih Sultan Mehmet Education and Research Hospital, Istanbul, Turkiye, from March 2020 to March 2021. METHODOLOGY: The patients who presented to the emergency department, diagnosed with COVID-19 and tested positive for polymerase chain reaction were analysed. The study included the calculation of the rapid emergency medicine score, risk stratification in the emergency department in acutely ill older patients score, 4C mortality score, and modified early warning score for the patients. These scores were then compared in terms of their ability to predict adverse outcomes, defined as intensive care admission and/or mortality. RESULTS: During the study period, 10,281 COVID-19 patients were admitted to the emergency department. Out of them, 1,826 patients were included in the study. There were 159 (8.7%) cases with poor outcomes. The risk stratification in the emergency department in acutely ill older patients Score was the most successful in poor prognosis. CONCLUSION: Based on the findings of this study, the risk stratification in the emergency department in acutely ill older patients score demonstrated greater efficacy compared to other early warning scores in identifying patients diagnosed with COVID-19 who had an early indication of a poor prognosis. KEY WORDS: Early warning score, 4C mortality score, REMS, Rise-up score, MEWS, Emergency department, COVID-19.

Comparison of the new-Poisoning Mortality Score and the Modified Early Warning Score for predicting in-hospital mortality in patients with acute poisoning.

INTRODUCTION: The evaluation of acute poisoning is challenging due to varied toxic substances and clinical presentations. The new-Poisoning Mortality Score was recently developed to assess patients with acute poisoning and showed good performance in predicting in-hospital mortality. The objective of this study is to externally validate the performance of the new-Poisoning Mortality Score and to compare it with the Modified Early Warning Score. METHODS: This retrospective analysis used data from the 2019-2020 Injury Surveillance Cohort, established by the Korea Center for Disease Control and Prevention, to perform external validation of the new-Poisoning Mortality Score. The statistical performances of the new-Poisoning Mortality and Modified Early Warning Scores were assessed and compared in terms of discrimination and calibration. Discrimination analysis involved metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. For calibration analysis, the Hosmer-Lemeshow goodness-of-fit test was utilized and calibration curves for each score were generated to elucidate the relationship between observed and predicted mortalities. RESULTS: This study analysed 16,570 patients with acute poisoning. Significant differences were observed between survivors and those who died in-hospital, including age, sex, and vital signs. The new-Poisoning Mortality Score showed better performance over the Modified Early Warning Score in predicting in-hospital mortality, in terms of the area under the receiver operating characteristic curve (0.947 versus 0.800), sensitivity (0.863 versus 0.667), specificity (0.912 versus 0.817), and accuracy (0.911 versus 0.814). When evaluated through calibration curves, the new-Poisoning Mortality Score showed better concordance between predicted and observed mortalities. In subgroup analyses, the score system consistently showed strong performance, excelling particularly in substances with high mortality indices and remaining superior in all substances as a group. CONCLUSIONS: Our study has helped to validate the new-Poisoning Mortality Score as an effective tool for predicting in-hospital mortality in patients with acute poisoning in the emergency department. The score system demonstrated superior performance over the Modified Early Warning Score in various metrics. Our findings suggest that the new-Poisoning Mortality Score can contribute to the enhancement of clinical decision-making and patient management.

Early sepsis care with the National Early Warning Score 2-guided Sepsis Hour-1 Bundle in the emergency department: hybrid type 1 effectiveness-implementation pilot stepped wedge randomised controlled trial (NEWS-1 TRIPS) protocol.

INTRODUCTION: Early sepsis treatment in the emergency department (ED) is crucial to improve patient survival. Despite international promulgation, the uptake of the Surviving Sepsis Campaign (SSC) Hour-1 Bundle (lactate measurement, blood culture, broad-spectrum antibiotics, 30 mL/kg crystalloid for hypotension/lactate ≥4 mmol/L and vasopressors for hypotension during/after fluid resuscitation within 1 hour of sepsis recognition) is low across healthcare settings. Delays in sepsis recognition and a lack of high-quality evidence hinder its implementation. We propose a novel sepsis care model (National Early Warning Score, NEWS-1 care), in which the SSC Hour-1 Bundle is triggered objectively by a high NEWS-2 (≥5). This study aims to determine the feasibility of a full-scale type 1 hybrid effectiveness-implementation trial on the NEWS-1 care in multiple EDs. METHODS AND ANALYSIS: We will conduct a pilot type 1 hybrid trial and prospectively recruit 200 patients from 4 public EDs in Hong Kong cluster randomised in a stepped wedge design over 10 months. All study sites will start with an initial period of standard care and switch in random order at 2-month intervals to the NEWS-1 care unidirectionally. The implementation evaluation will employ mixed methods guided by the Reach, Effectiveness, Adoption, Implementation and Maintenance framework, which includes qualitative and quantitative data from focus group interviews, staff survey and clinical record reviews. We will analyse the 14 feasibility outcomes as progression criteria to a full-scale trial, including trial acceptability to patients and staff, patient and staff recruitment rates, accuracy of sepsis screening, protocol adherence, accessibility to follow-up data, safety and preliminary clinical impacts of the NEWS1 care, using descriptive statistics. ETHICS AND DISSEMINATION: The institutional review boards of all study sites approved this study. This study will establish the feasibility of a full-scale hybrid trial. We will disseminate the findings through peer-reviewed publications, conference presentations and educational activities. TRIAL REGISTRATION NUMBER: NCT05731349.

Clinical performance of the infant nutrition early warning score in routine practice across four international clinical settings in Europe: A study by the ESPGHAN special interest group in clinical malnutrition.

There is a scarcity of nutritional screening tools for use in infants (<1 year). The infant Nutrition Early Warning Score (iNEWS) has been developed to identify infants who need further dietetic review. We introduced the iNEWS into clinical practice and evaluated its performance in Scotland, Belgium, Athens and Bulgaria. Of the 352 infants screened, 72 (20%) were placed in the high iNEWS category, and of these, 70 (97%) were reviewed by a hospital dietitian. iNEWS produced a true positive rate of 80% which increased to 96% after accounting for anticipated misclassified cases due to prematurity. In Belgium, false positive screens had a shorter length of stay (p = 0.014). Otherwise, misclassification was not related to a specific iNEWS component. This study corroborates previous research, underscoring the validity of iNEWS as a dietetic referral tool and demonstrating that it can be integrated into "real-world" clinical practice across international settings with diverse healthcare resources.

Study protocol of a national multicentre prospective evaluation study assessing the validity and impact of the Dutch Paediatric Early Warning Score (PEWS) in the Netherlands.

INTRODUCTION: Early recognition of clinical deterioration and timely intervention are important to improve morbidity and mortality in paediatric care. The Paediatric Early Warning Score (PEWS) is a scoring system aiming to identify hospitalised children at risk for deterioration. Currently, there is a large heterogeneity of PEWS systems in the Netherlands, with a considerable number remaining unvalidated or self-designed. Therefore, a consensus-based Dutch PEWS has been developed in a national study using the Core Outcome Measures in Effectiveness Trials initiative. The Dutch PEWS is a uniform system that integrates a core set of vital parameters together with pre-existing risk factors and uses risk stratification to proactively follow-up on patients at risk (so-called 'watcher patients'). This study aims to validate the Dutch PEWS and to determine its impact on improving patient safety in various hospital settings. METHODS AND ANALYSIS: This national study will be a large multicentre evaluation study, in which the Dutch PEWS will be implemented and evaluated in 12 hospitals in the Netherlands. In this study, a mixed methods methodology will be used and evaluated on predefined outcome measures. To examine the validity of the Dutch PEWS, statistical analyses will be undertaken on quantitative data retrieved from electronic health records. Surveys among physicians and nurses; semistructured interviews with healthcare providers and parents; and daily evaluation forms are being conducted to determine the impact of the Dutch PEWS. The study is being conducted from December 2020 to June 2024.

Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study.

BACKGROUND: A myriad of early warning scores (EWSs) exist, yet there is a need to identify the most clinically valid score to be used in prehospital respiratory assessments to estimate short-term and midterm mortality, intensive-care unit admission, and airway management in life-threatening acute respiratory distress. METHODS: This is a prospective, observational, multicentre, ambulance-based, external validation study performed in 44 ambulance services and four hospitals across three Spanish provinces (ie, Salamanca, Segovia, and Valladolid). We identified adults (ie, those aged 18 years and older) discharged to the emergency department with suspected acute respiratory distress. The primary outcome was 2-day all-cause in-hospital mortality, for all the patients or according to prehospital respiratory conditions, including dyspnoea, chronic obstructive pulmonary disease (COPD), COVID-19, other infections, and other conditions (asthma exacerbation, haemoptysis, and bronchoaspirations). 30-day mortality, intensive-care unit admission, and invasive and non-invasive mechanical ventilation were secondary outcomes. Eight EWSs, namely, the National Early Warning Score 2, the Modified Rapid Emergency Medicine Score, the Rapid Acute Physiology Score, the Quick Sequential Organ Failure Assessment Score, the CURB-65 Severity Score for Community-Acquired Pneumonia, the BAP-65 Score for Acute Exacerbation of COPD, the Quick COVID-19 Severity Index, and the Modified Sequential Organ Failure Assessment (mSOFA), were explored to determine their predictive validity through calibration, clinical net benefit as determined through decision curve analysis, and discrimination analysis (area under the curve of the receiver operating characteristic [AUROC], compared with Delong's test). FINDINGS: Between Jan 1, 2020, and Nov 31, 2022, 902 patients were enrolled. The global 2-day mortality rate was 87 (10%); in proportion to various respiratory conditions, the rates were 35 (40%) for dyspnoea, nine (10%) for COPD, 13 (15%) for COVID-19, 28 (32%) for other infections, and two (2%) for others conditions. mSOFA showed the best calibration, a higher net benefit, and the best discrimination (AUROC 0·911, 95% CI 0·86-0·95) for predicting 2-day mortality, and its discrimination was statistically significantly more accurate (p<0·0001) compared with the other scores. The performance of mSOFA for predicting 2-day mortality was higher than the other scores when considering the prehospital respiratory conditions, and was also higher for the secondary outcomes, except for non-invasive mechanical ventilation. INTERPRETATION: Our results showed that mSOFA outperformed other EWSs. The inclusion of mSOFA in prehospital decision making will entail a quick identification of patients in acute respiratory distress at high risk of deterioration, allowing prioritisation of resources and patient care. FUNDING: Gerencia Regional de Salud, Public Health System of Castilla y León (GRS Spain). TRANSLATION: For the Spanish translation of the abstract see Supplementary Materials section.

An Evaluation of the National Early Warning Score 2 and the Laboratory Data Decision Tree Early Warning Score in Predicting Mortality in Geriatric Patients.

BACKGROUND: Due to the high rate of geriatric patient visits, scoring systems are needed to predict increasing mortality rates. OBJECTIVE: In this study, we aimed to investigate the in-hospital mortality prediction power of the National Early Warning Score 2 (NEWS2) and the Laboratory Data Decision Tree Early Warning Score (LDT-EWS), which consists of frequently performed laboratory parameters. METHODS: We retrospectively analyzed 651 geriatric patients who visited the emergency department (ED), were not discharged on the same day from ED, and were hospitalized. The patients were categorized according to their in-hospital mortality status. The NEWS2 and LDT-EWS values of these patients were calculated and compared on the basis of deceased and living patients. RESULTS: Median (interquartile range [IQR]) NEWS2 and LDT-EWS values of the 127 patients who died were found to be statistically significantly higher than those of the patients who survived (NEWS2: 5 [3-8] vs. 3 [1-5]; p < 0.001; LDT-EWS: 8 [7-10] vs. 6 [5-8]; p < 0.001). In the receiver operating characteristic curve analysis, the NEWS2, LDT-EWS, and NEWS2+LDT-EWS-formed by the sum of the two scoring systems-resulted in 0.717, 0.705, and 0.775 area under curve values, respectively. CONCLUSIONS: The NEWS2 and LDT-EWS were found to be valuable for predicting in-hospital mortality in geriatric patients. The power of the NEWS2 to predict in-hospital mortality increased when used with the LDT-EWS.

Real-world implementation of the National Early Warning Score-2 in an acute respiratory unit.

INTRODUCTION: The National Early Warning Score-2 (NEWS-2) is used to detect deteriorating patients in hospital settings. We aimed to understand how NEWS-2 functions in the real-life setting of an acute respiratory unit. METHODS: Clinical observations data were extracted for adult patients (age ≥18 years), admitted under the care of respiratory medicine services from July to December 2019, who had at least one recorded task relating to clinical deterioration. The timing and nature of urgent out-of-hours medical reviews (escalations) were extracted through manual review of the case notes. RESULTS: The data set comprised 765 admission episodes (48.9% women) with a mean (SD) age of 69.3 (14.8). 8971 out of 35 991 out-of-hours observation sets (24.9%) had a NEWS-2 ≥5, and 586 of these (6.5%) led to an escalation. Out of 687 escalations, 101 (14.7%) were associated with observation sets with NEWS-2<5. Rising oxygen requirement and extreme values of individual observations were associated with an increased risk of escalation. 57.6% of escalations resulted in a change in treatment. Inpatient mortality was higher in patients who were escalated at least once, compared with those who were not escalated. CONCLUSIONS: Most observation sets with NEWS-2 scores ≥5 did not lead to a medical escalation in an acute respiratory setting out-of-hours, but more than half of escalations resulted in a change in treatment. Rising oxygen requirement is a key indicator of respiratory patient acuity which appears to influence the decision to request urgent out-of-hours medical reviews.